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Über dieses Buch

This book constitutes the proceedings of the 6th International Conference on Biomimetic and Biohybrid Systems, Living Machines 2017, held in Stanford, CA, USA, in July 2017.The 42 full and 19 short papers presented in this volume were carefully reviewed and selected from 63 submissions. The theme of the conference encompasses biomimetic methods for manufacture, repair and recycling inspired by natural processes such as reproduction, digestion, morphogenesis and metamorphosis.



Straight Swimming Algorithm Used by a Design of Biomimetic Robotic Fish

Teleost species of fish mostly move with their peduncle. They are the fastest moving underwater creature. In this work, focus is specifically on the algorithm that was used for propelling a design of a robotic fish based on Mackerel in a straight swimming motion. The approach used is fundamentally called built in motion pattern algorithm as against follow the leader approach and mathematically generated serpentine motion used in hyper-redundant robot motion control strategies. The design requires just 3 actuators (RC servomotors) that are controlled using Microchip PIC18F4520 microcontroller. The 3 PWM controlling the motors are dynamically adjusted to be at fixed phase to each other at all times. This design was able to produce a travelling wave which propels the robot forward. Though not a very flexible implementation in terms of dynamically reprogramming the robot firmware while executing code, like the other methods that involve onboard mathematical position generation, it can however save battery life. This method works perfectly because of the unique design of the robot hardware. A field test yield 1/3 of the speed (3.66 km/h) of a life Mackerel.

M. O. Afolayan

Animal and Robotic Locomotion on Wet Granular Media

Most of the terrestrial environments are covered with some type of flowing ground; however, inadequate understanding of moving bodies interacting with complex granular substrates has hindered the development of terrestrial/all-terrain robots. Although there has been recent performance of experimental and computational studies of dry granular media, wet granular media remain largely unexplored. In particular, this encompasses animal locomotion analysis, robotic system performance, and the physics of granular media at different saturation levels. Given that the presence of liquid in granular media alters its properties significantly, it is advantageous to evaluate the locomotion of animals inhabiting semi-aquatic and tropical environments to learn more about effective locomotion strategies on such terrains. Lizards are versatile and highly agile animals. Therefore, this study evaluated the brown basilisk, which is a lizard species from such habitats that are known for their performance on wet granular media. An extensive locomotion study was performed on this species. The animal experiments showed that on higher saturation levels, velocity of the animal was increased due to an increase in the stride length. A basilisk-inspired robot was then developed to further study the locomotion on wet granular media and it was observed that the robot can also achieve higher velocities at increased saturation levels. This work can pave the way for developing robotic systems which can explore complex environments for scientific discovery, planetary exploration, or search-and-rescue missions.

Hosain Bagheri, Vishwarath Taduru, Sachin Panchal, Shawn White, Hamidreza Marvi

Tunable Normal and Shear Force Discrimination by a Plant-Inspired Tactile Sensor for Soft Robotics

In plants, particular biomechanical protruding structures, tactile bleps, are thought to be specialized tactile sensory organs and sensitive to shear force. In this work, we present a 2D finite element analysis of a simplified plant-inspired capacitive tactile sensor. These preliminary results show that the variation of geometrical and material parameters permits to tune the sensitivity to normal and shear force and, with particular configurations, to discriminate between the two forces with a simple electrical layout and no signal processing.

Afroditi Astreinidi Blandin, Massimo Totaro, Irene Bernardeschi, Lucia Beccai

Pop! Observing and Modeling the Legless Self-righting Jumping Mechanism of Click Beetles

Click beetles (Coleoptera: Elateridae) have evolved a jumping mechanism to right themselves when on their dorsal side, without using their legs or any other appendages. This paper describes and analyzes the stages of the click beetle jump using high-speed video recordings and scanning electron micrographs of four beetle species, namely Alaus oculatus, Ampedus nigricollis, Ampedus linteus and Melanotus spp. The body of the click beetle is considered as two masses linked by a hinge. Dynamic and kinematic models of the jump stages are developed. The models were used to calculate the hinge stiffness and the elastic energy stored in the body during the jump. The modeling results show agreement with the experimental values. The derived models provide a framework that will be used for the design of a click beetle inspired self-righting robot.

Ophelia Bolmin, Chengfang Duan, Luis Urrutia, Ahmad M. Abdulla, Alexander M. Hazel, Marianne Alleyne, Alison C. Dunn, Aimy Wissa

Bioinspired Magnetic Navigation Using Magnetic Signatures as Waypoints

Diverse taxa use Earth’s magnetic field in conjunction with other sensor modes to accomplish navigation tasks that range from local homing to long-distance migration across continents and ocean basins. However, despite extensive research, animal magnetoreception remains a poorly understood, and active research area. Concurrently, Earth’s magnetic field offers a signal that engineered systems can leverage for navigation and localization in environments where man-made systems such as GPS are either unavailable or unreliable. Using a proxy for Earth’s magnetic field, and inspired by migratory animal behavior, this work implements behavioral strategies to navigate through a series of magnetic waypoints. The strategies are able to navigate through a closed set of points, in some cases running through several “laps”. Successful trials were observed in both a range of environmental parameters, and varying levels of sensor noise. The study explores several of these parameter combinations in simulation, and presents preliminary results from a version of the strategy implemented on a mobile robot platform. Alongside success, limitations of the simulated and hardware algorithms are discussed. The results illustrate the feasibility of either an animal, or engineered platform to use a set of waypoints based on the magnetic field to navigate. Additionally, the work presents an engineering/quantitative biology approach that can garner insight into animal behavior while simultaneously illuminating paths of development for engineered algorithms and systems.

Brian K. Taylor, Grant Huang

Jellyfish Inspired Soft Robot Prototype Which Uses Circumferential Contraction for Jet Propulsion

Several robotic jellyfish have been designed over the years, yet none have properly mimicked the very efficient method of propulsion that jellyfish use. Using circumferential contraction, water is pushed out the bottom of the bell creating upwards thrust. Jellyfish use this basic movement along with more complex features to move around the seas. In this paper, we attempt to mimic this circumferential contraction using hydraulically actuated silicone bellows that expand and contract a bell made of flexible silicone skin. 3D printed polylactic acid (PLA) was used to make the structure of the robot, and hinges and jubilee clips were used to fasten it together in order to maintain exchangeability of parts. The jellyfish expands and contracts using a pump with a simple on-off control which switches dependent on the internal pressure of the hydraulic system. This very simple control mechanism is similar to real jellyfish, and much like jellyfish, our design attempts to use both passive and active movements to maximize thrust.

George Bridges, Moritz Raach, Martin F. Stoelen

You Made Him Be Alive: Children’s Perceptions of Animacy in a Humanoid Robot

Social robots are becoming more sophisticated; in many cases they offer complex, autonomous interactions, responsive behaviors, and biomimetic appearances. These features may have significant impact on how people perceive and engage with robots; young children may be particularly influenced due to their developing ideas of agency. Young children are considered to hold naive beliefs of animacy and a tendency to mis-categorise moving objects as being alive but, with development, children can demonstrate a biological understanding of animacy. We experimentally explore the impact of children’s age and a humanoid’s movement on children’s perceptions of its animacy.Our humanoid’s behavior varied in apparent autonomy, from motionless, to manually operated, to covertly operated. Across conditions, younger children rated the robot as being significantly more person-like than older children did. We further found an interaction effect: younger children classified the robot as significantly more machine-like if they observed direct operation in contrast observing the motionless or apparently autonomous robot. Our findings replicate field results, supporting the modal model of the developmental trajectory for children’s understanding of animacy. We outline a program of research to both deepen the theoretical understanding of children’s animacy beliefs and develop robotic characters appropriate across key stages of child development.

David Cameron, Samuel Fernando, Emily C. Collins, Abigail Millings, Michael Szollosy, Roger Moore, Amanda Sharkey, Tony Prescott

Analysing the Limitations of Deep Learning for Developmental Robotics

Deep learning is a powerful approach to machine learning however its inherent disadvantages leave much to be desired in the pursuit of the perfect learning machine. This paper outlines the multiple disadvantages of deep learning and offers a view into the implications to solving these problems and how this would affect the state of the art not only in developmental learning but also in real world applications.

Daniel Camilleri, Tony Prescott

Reducing Training Environments in Evolutionary Robotics Through Ecological Modularity

Due to the large number of evaluations required, evolutionary robotics experiments are generally conducted in simulated environments. One way to increase the generality of a robot’s behavior is to evolve it in multiple environments. These environment spaces can be defined by the number of free parameters (f) and the number of variations each free parameter can take (n). Each environment space then has $$n^f$$nf individual environments. For a robot to be fit in the environment space it must perform well in each of the $$n^f$$nf environments. Thus the number of environments grows exponentially as n and f are increased. To mitigate the problem of having to evolve a robot in each environment in the space we introduce the concept of ecological modularity. Ecological modularity is here defined as the robot’s modularity with respect to free parameters in its environment space. We show that if a robot is modular along m of the free parameters in its environment space, it only needs to be evolved in $$n^{f-m+1}$$nf-m+1 environments to be fit in all of the $$n^f$$nf environments. This work thus presents a heretofore unknown relationship between the modularity of an agent and its ability to generalize evolved behaviors in new environments.

Collin Cappelle, Anton Bernatskiy, Josh Bongard

Automated Calibration of a Biomimetic Space-Dependent Model for Zebrafish and Robot Collective Behaviour in a Structured Environment

Bio-hybrid systems made of robots and animals can be useful tools both for biology and robotics. To socially integrate robots into animal groups the robots should behave in a biomimetic manner with close loop interactions between robots and animals. Behavioural zebrafish experiments show that their individual behaviours depend on social interactions producing collective behaviour and depend on their position in the environment. Based on those observations we build a multilevel model to describe the zebrafish collective behaviours in a structured environment. Here, we present this new model segmented in spatial zones that each corresponds to different behavioural patterns. We automatically fit the model parameters for each zone to experimental data using a multi-objective evolutionary algorithm. We then evaluate how the resulting calibrated model compares to the experimental data. The model is used to drive the behaviour of a robot that has to integrate socially in a group of zebrafish. We show experimentally that a biomimetic multilevel and context-dependent model allows good social integration of fish and robots in a structured environment.

Leo Cazenille, Yohann Chemtob, Frank Bonnet, Alexey Gribovskiy, Francesco Mondada, Nicolas Bredeche, José Halloy

Spiking Cooperative Stereo-Matching at 2 ms Latency with Neuromorphic Hardware

We demonstrate a spiking neural network that extracts spatial depth information from a stereoscopic visual input stream. The system makes use of a scalable neuromorphic computing platform, SpiNNaker, and neuromorphic vision sensors, so called silicon retinas, to solve the stereo matching (correspondence) problem in real-time. It dynamically fuses two retinal event streams into a depth-resolved event stream with a fixed latency of 2 ms, even at input rates as high as several 100,000 events per second. The network design is simple and portable so it can run on many types of neuromorphic computing platforms including FPGAs and dedicated silicon.

Georgi Dikov, Mohsen Firouzi, Florian Röhrbein, Jörg Conradt, Christoph Richter

Development of Novel Foam-Based Soft Robotic Ring Actuators for a Biomimetic Peristaltic Pumping System

Peristaltic pumping in nature allows for the transport of various media in a simple and secure way. Different types of peristaltic pumps exist in the application area of soft robotics. Most systems are based on pneumatic network (pneu-net) fluidic elastomer actuators or artificial muscle actuators. In this study the development of a pump actuated by foam-based, flexible, compliant and lightweight ring actuators is presented. Utilizing a custom built pump test bench the soft robotic ring actuators are characterized in terms of contraction rate and volumetric displacement. Furthermore we introduce a flexible and elastic soft robotic peristaltic pumping system as an alternative to conventional technical pumps.

Falk Esser, Tibor Steger, David Bach, Tom Masselter, Thomas Speck

Introducing Biomimomics: Combining Biomimetics and Comparative Genomics for Constraining Organismal and Technological Complexity

Integrated genomics and transcriptomics data, together with the analysis of total protein and metabolite content of a given cell, is providing the basis for complex, multi-scale and dynamic models of cellular metabolism in health and disease. Accordingly, the functional triad of genomics, transcriptomics and metabolomics is regarded as a foundational methodology in systems biology. Opening up a never-before seen vista into the organization and dynamical evolution of cellular life at multiple scales of complexity, Omics-approaches are poised to facilitate discoveries in biomimetic design processes. In the following, the proposed merger of biomimetics with Omics-techniques will be called “Biomimomics”. Focusing on comparative genomics, this paper will outline how ongoing work in the field is revising our understanding of early nervous system and synapse evolution in animals and, at the same time, promises to give insights into truly, i.e. evolutionarily-based, biomimetic neuromorphic computing architectures. We will show how a new kind of modular workflow based on a “Biomimomic Traceability Matrix” (BTM) can structure and facilitate both biomimetic design solutions and the discovery of universal principles underlying complexifying biological and technological systems.

Claudio L. Flores Martinez

Effects of Locomotive Drift in Scale-Invariant Robotic Search Strategies

Robots play a fundamental role in the exploration of environments that are harmful to humans or animals: robotic probes can reach deep into the earth’s crust, explore our oceans, traverse high radiation areas, navigate in outer space, etc. The harsh conditions and large amounts of uncertainty of these environments can complicate the use of global positioning systems, and in some cases robots have to depend exclusively in local information as external position landmarks are not available. Lévy walks are increasingly studied as effective solutions in these exploratory contexts. The superdiffusive (dispersive) properties of these forms of random walks are often exploited by many animal species, in particular when tackling search problems that have uncertainty. Based on experimentation with low-cost mobile robots, this work has characterized how long-term motion drift (which is inherent to search contexts that lack global positioning systems) can have an effect in the overall characteristics of Lévy trajectories. The results show that Lévy-based searches can be robust and maintain superdiffusive properties for some ranges of motion drift parameters that are closely related to the scale of the search problem. Locomotive drift seems to act effectively as a long-term truncation parameter that could be corrected or even incorporated during the design of a search task.

Carlos Garcia-Saura, Eduardo Serrano, Francisco B. Rodriguez, Pablo Varona

Simulation of Human Balance Control Using an Inverted Pendulum Model

Human balance control is a complex feedback system that must be adaptable and robust in an infinitely varying external environment. It is probable that there are many concurrent control loops occurring in the central nervous system that achieve stability for a variety of postural perturbations. Though many engineering models of human balance control have been tested, no models of how these controllers might operate within the nervous system have yet been developed. We have created a synthetic nervous system that provides Proportional-Derivative (PD) control to a single jointed inverted pendulum model of human balance. In this model, angular position is measured at the ankle and corrective torque is applied about the joint to maintain a vertical orientation. The neural network computes the derivative of the angular position error, which allows the system to maintain an unstable equilibrium position and provide corrections at perturbations. This controller demonstrates the most basic components of human balance control, and will be used as the basis for more complex controllers and neuromechanical models in future work.

Wade W. Hilts, Nicholas S. Szczecinski, Roger D. Quinn, Alexander J. Hunt

Reducing Versatile Bat Wing Conformations to a 1-DoF Machine

Recent works have shown success in mimicking the flapping flight of bats on the robotic platform Bat Bot (B2). This robot has only five actuators but retains the ability to flap and fold-unfold its wings in flight. However, this bat-like robot has been unable to perform folding-unfolding of its wings within the period of a wingbeat cycle, about 100 ms. The DC motors operating the spindle mechanisms cannot attain this folding speed. Biological bats rely on this periodic folding of their wings during the upstroke of the wingbeat cycle. It reduces the moment of inertia of the wings and limits the negative lift generated during the upstroke. Thus, we consider it important to achieve wing folding during the upstroke. A mechanism was designed to couple the flapping cycle to the folding cycle of the robot. We then use biological data to further optimize the mechanism such that the kinematic synergies of the robot best match those of a biological bat. This ensures that folding is performed at the correct point in the wingbeat cycle.

Jonathan Hoff, Alireza Ramezani, Soon-Jo Chung, Seth Hutchinson

Mathematical Modeling to Improve Control of Mesh Body for Peristaltic Locomotion

In this work, we built a kinematic simulation model for our worm robot, which does peristaltic locomotion. We studied the construction of our robot’s mesh-rhombus structure and the structural behavior in response to the actuator controls and simulated them in MATLAB. With some kinematic assumptions, we can model changes in body shape. Friction, gravity, internal forces are not directly modeled, however a single correction factor can be used to align the simulation and hardware progress. New control methods are found based on this model, which reduced the motion slip on the robot. In future work, this simulation can help us control and design future mesh-based robots.

Yifan Huang, Akhil Kandhari, Hillel J. Chiel, Roger D. Quinn, Kathryn A. Daltorio

Neuronal Distance Estimation by a Fly-Robot Interface

The ability of an autonomous robot to avoid collisions depends on distance estimates. In this paper, we focus on open-loop responses of the identified directional-selective H1-cell in the fly brain, recorded in animals that are mounted on a 2-wheeled robot. During oscillatory forward movement along a wall clad with a pattern of vertical stripes on one side of the robot, the H1-cell periodically increases and decreases its spike rate, where the response amplitude depends on two parameters: (i) the turning radius of the robot, and (ii) the distance between the wall and the mean forward trajectory of the robot. For small turning radii, we found a monotonic relationship between the H1-cell’s spike rate and wall distance. Our results suggest that, given a known turning radius, the responses of the H1-cell could be used in a negative feedback-loop to control the average forward trajectory of an autonomous robot that avoids collisions with potential obstacles in its environment.

Jiaqi V. Huang, Holger G. Krapp

Bio-inspired Robot Design Considering Load-Bearing and Kinematic Ontogeny of Chelonioidea Sea Turtles

This work explores the physical implications of variation in fin shape and orientation that correspond to ontogenetic changes observed in sea turtles. Through the development of a bio-inspired robotic platform – CTurtle – we show that (1) these ontogenetic changes apparently occupy stable extrema for either load-bearing or high-velocity movement, and (2) mimicry of these variations in a robotic system confer greater load-bearing capacity and energy efficiency, at the expense of velocity (or vice-versa). A possible means of adapting to load conditions is also proposed. We endeavor to provide these results as part of a theoretical framework integrating biological inquiry and inspiration within an iterative design cycle based on laminate robotics.

Andrew Jansen, Kevin Sebastian Luck, Joseph Campbell, Heni Ben Amor, Daniel M. Aukes

Feather-Inspired Sensor for Stabilizing Unmanned Aerial Vehicles in Turbulent Conditions

Stabilizing unmanned aerial vehicles (UAVs) in turbulent conditions is a challenging problem. Typical methods of stabilization do not use feedforward information about the airflow disturbances but only UAV attitude feedback signals, e.g. from an inertial measurement unit. The novel proposal of this work is the development of a feather-inspired sensor and feedforward controller that transforms from sensed turbulent airflow to feedforward control action for improving the stability of the UAV. The feedforward controller was based on fuzzy logic, combined in a feedforward-feedback loop with a standard PID control system. An experimental rig based on a one degree of freedom helicopter plant (elevation only) was developed to evaluate the potential of the sensor and control algorithm. Evaluation results showed reduction of disturbance using the fuzzy feedforward-feedback scheme, under turbulent airflow, versus a classical feedback PID-controlled system.

Christos Kouppas, Martin Pearson, Paul Dean, Sean Anderson

Towards Identifying Biological Research Articles in Computer-Aided Biomimetics

When solving engineering problems through biomimetic design, a lack of knowledge of biology often impedes the translation of biological ideas into engineering principles. Specific challenges are the identification, selection and abstraction of relevant biological information. The use of engineering terminology to search for relevant biological information is hypothesised to contribute to the adventitious character of biomimetics. Alternatively, a holistic approach is proposed where a division is made between the analysis of biological research papers and the decomposition of the engineering problem. The aim of a holisitic approach is to take into account the importance of context during analogical problem solving and provide a theoretical framework for the development of Computer-Aided Biomimetics (CAB) tools. Future work will focus on the development of tools that support engineers during the analysis of biological research papers and modelling of biological systems by providing relevant biological knowledge.

Ruben Kruiper, Julian F. V. Vincent, Jessica Chen-Burger, Marc P. Y. Desmulliez

Deep Dynamic Programming: Optimal Control with Continuous Model Learning of a Nonlinear Muscle Actuated Arm

We outline a new technique for on-line continuous model learning control and demonstrate its utility by controlling a simulated 2-DOF arm actuated by 6 muscles as well as on an inverted pendulum. Work presented is part of an effort to develop controllers for human appendages rendered inoperable by paralysis. Computerized control provides an alternative to neural regeneration by means of electric muscle stimulation. It has been demonstrated that paralyzed individuals can regain self-powered mobility via use of external muscle controllers. A barrier to proliferation of the technology, is the difficulty in control over the living system which is highly nonlinear and unique to each individual. Here we demonstrate a novel, continuous model learning technique to simultaneously learn and control continuous, non-linear systems. The technique expands upon vanilla Q-learning and dynamic programming. Unlike typical Q-learning, where the action-value function updates are only for the most recent set of states visited and stored in memory, the method presented also generates updates to the action-value function for unvisited state-space and state-space visited far in the past. This is made feasible by giving the agent the ability to continually learn and update explicit local models of the environment and of itself, which we encapsulated in a set of deep neural networks.

Andrew G. Lonsberry, Alexander J. Lonsberry, Roger D. Quinn

An Adaptive Modular Recurrent Cerebellum-Inspired Controller

Animals and robots face the common challenge of interacting with an unstructured environment. While animals excel and thrive in such environments, modern robotics struggles to effectively execute simple tasks. To help improve performance in the face of frequent changes in the mapping between action and outcome (change in context) we propose the Modular-RDC controller, a bio-inspired controller based on the Recurrent Decorrelation Control (RDC) architecture. The proposed controller consists of multiple modules, each containing a forward and inverse model pair. The combined output of all inverse models is used to control the plant, with the contribution of each inverse model determined by a responsibility factor. The controller is able to correctly identify the best module for the current context, enabling a significant reduction of 70.9% in control error for a context-switching plant. It is also shown that the controller results in a degree of generalization in control.

Kiyan Maheri, Alexander Lenz, Martin J. Pearson

Stimulus Control for Semi-autonomous Computer Canine-Training

For thousands of years, humans have domesticated and trained dogs to perform tasks for them. Humans have developed areas of study, such as Applied Behavior Analysis, which aim to improve the training process. We introduce a semi-autonomous, canine-training system by combining existing research in Applied Behavior Analysis with computer systems consisting of hardware, software, audio, and visual components. These components comprise a biohybrid system capable of autonomously training a dog to perform a specific behavior on command. In this paper we further our previous computer canine-training system by the application of stimulus control over a newly-acquired, free operant behavior. This system uses light and sound as a discriminative stimulus for the behavior of a dog pushing a button with its nose. Indications of simple stimulus control of this behavior were achieved. Our pilot of this system indicates canine learning comparable to that from a professional dog trainer.

John J. Majikes, Sherrie Yuschak, Katherine Walker, Rita Brugarolas, Sean Mealin, Marc Foster, Alper Bozkurt, Barbara Sherman, David L. Roberts

Exploiting Morphology of a Soft Manipulator for Assistive Tasks

The idea of using embodied intelligence over traditional well-structured design and control formulations has given rise to simple yet elegant applications in the form of soft grippers and compliant locomotion-based robots. Real-world applications of soft manipulators are however limited, largely due to their low accuracy and force transmission. Nonetheless, with the rise of robotic appliances in the field of human-robot interaction, their advantages could outweigh their control deficiencies. In this context, the embodied intelligence could play an important role in developing safe and robust controllers. In this paper, we present a three module soft manipulator to experimentally demonstrate how its morphological properties can be exploited through interactions with the external environment. In particular, we show how to improve the pose accuracy in an assistive task using a simple control algorithm. The soft manipulator takes advantage of its inherent compliance and the physical constraints of the external environment to accomplish a safe interactive task with the user. There exists a continuous and mutual adaptation between the soft-bodied system and the environment. This feature can be used in tasks where the environment is unstructured (e.g. specific body region), and the adaptability of the interaction is entirely dependent on the morphology and control of the system. Experimental results indicate that significant improvements in the tracking accuracy can be achieved by a simple yet appropriate environmental constraint.

Mariangela Manti, Thomas George Thuruthel, Francesco Paolo Falotico, Andrea Pratesi, Egidio Falotico, Matteo Cianchetti, Cecilia Laschi

Autonomous Thrust-Assisted Perching of a Fixed-Wing UAV on Vertical Surfaces

We present the first fixed-wing drone that autonomously perches and takes off from vertical surfaces. Inspired by birds, this airplane uses a thrust-assisted pitch-up maneuver to slow down rapidly before touchdown. Microspines are used to cling to rough walls, while strictly onboard sensing is used for control. The effect of thrust on the suspension’s landing envelope is analyzed and a simple vertical velocity controller is proposed to create smooth and robust descents towards a wall. Multiple landings are performed over a range of flight conditions (a video of S-MAD is available at:

Dino Mehanovic, John Bass, Thomas Courteau, David Rancourt, Alexis Lussier Desbiens

An Integrated Compliant Fabric Skin Softens, Lightens, and Simplifies a Mesh Robot

Earthworms are particularly skilled at navigating through confined spaces. Therefore, creating a soft robot that mimics their peristaltic locomotion could provide unique advantages for pipe inspection, search and rescue, exploration, and medical applications. Here we present the design of a new robot, FabricWorm, that like its predecessor, CMMWorm, has six segments that are actuated with circumferential cables sequentially to mimic the peristaltic motion in an earthworm. However, compared to its predecessor, FabricWorm is 41% softer, is 23% lighter, and has 64% fewer rigid structural components due to the integration of the mesh within a fabric skin. These improvements, and the benefit of a continuous fabric skin, can be important advantages for worm-like robots.

Anna Mehringer, Akhil Kandhari, Hillel Chiel, Roger Quinn, Kathryn Daltorio

Causal Biomimesis: Self-replication as Evolutionary Consequence

For millions of years, hominins have been engaged in tool-making and concomitant experimentation. This cognitive enterprise has eventually led to the creation of synthetic intelligence in the form of complex computing and artificial agents, whose purported purpose is to elucidate the workings of human biology and consciousness, automate tasks, and develop interventions for disease. However, much of the expensive research efforts invested in understanding complex natural systems has resulted in limited rewards for treatment of disease. This paper proposes the novel ‘causal biomimesis’ hypothesis: with respect to the relationship between humans and artificial life, the virtually inevitable intrinsic evolutionary consequence of tool-making and biomimetic efforts—and the capacity for objective thought and the scientific method itself—is the full-scale replication of human cognitive functionality, agency, and potentially consciousness in silico. This self-replication transpires through a cycle of anthropogenic biomimetic auto-catalysis driven by instrumental cognition—from objective reasoning in hominin tool-maker through to post-biological reproduction by synthetic agents—and is self-organized and co-enacted between agent and the produced artefactual aggregates. In light of this radical hypothesis, existential and ethical implications are considered for further exploration.

Gabriel Axel Montes

Non-ordinary Consciousness for Artificial Intelligence

Humans are active agents in the design of artificial intelligence (AI), and our input into its development is critical. A case is made for recognizing the importance of including non-ordinary functional capacities of human consciousness in the development of synthetic life, in order for the latter to capture a wider range in the spectrum of neurobiological capabilities. These capacities can be revealed by studying self-cultivation practices designed by humans since prehistoric times for developing non-ordinary functionalities of consciousness. A neurophenomenological praxis is proposed as a model for self-cultivation by an agent in an entropic world. It is proposed that this approach will promote a more complete self-understanding in humans and enable a more thoroughly mutually-beneficial relationship between in life in vivo and in silico.

Gabriel Axel Montes

A Biomimetic Vocalisation System for MiRo

There is increasing interest in the use of animal-like robots in applications such as companionship and pet therapy. However, in the majority of cases it is only the robot’s physical appearance that mimics a given animal. In contrast, MiRo is the first commercial biomimetic robot to be based on a hardware and software architecture that is modelled on the biological brain. This paper describes how MiRo’s vocalisation system was designed, not using pre-recorded animal sounds, but based on the implementation of a real-time parametric general-purpose mammalian vocal synthesiser tailored to the specific physical characteristics of the robot. The novel outcome has been the creation of an ‘appropriate’ voice for MiRo that is perfectly aligned to the physical and behavioural affordances of the robot, thereby avoiding the ‘uncanny valley’ effect and contributing strongly to the effectiveness of MiRo as an interactive device.

Roger K. Moore, Ben Mitchinson

A Scalable Neuro-inspired Robot Controller Integrating a Machine Learning Algorithm and a Spiking Cerebellar-Like Network

Combining Fable robot, a modular robot, with a neuroinspired controller, we present the proof of principle of a system that can scale to several neurally controlled compliant modules. The motor control and learning of a robot module are carried out by a Unit Learning Machine (ULM) that embeds the Locally Weighted Projection Regression algorithm (LWPR) and a spiking cerebellar-like microcircuit. The LWPR guarantees both an optimized representation of the input space and the learning of the dynamic internal model (IM) of the robot. However, the cerebellar-like sub-circuit integrates LWPR input-driven contributions to deliver accurate corrective commands to the global IM. This article extends the earlier work by including the Deep Cerebellar Nuclei (DCN) and by reproducing the Purkinje and the DCN layers using a spiking neural network (SNN) implemented on the neuromorphic SpiNNaker platform. The performance and robustness outcomes from the real robot tests are promising for neural control scalability.

Ismael Baira Ojeda, Silvia Tolu, Henrik H. Lund

Behavior-State Dependent Modulation of Perception Based on a Model of Conditioning

The embodied mammalian brain evolved to adapt to an only partially known and knowable world. The adaptive labeling of the world is critically dependent on the neocortex which in turn is modulated by a range of subcortical systems such as the thalamus, ventral striatum and the amygdala. A particular case in point is the learning paradigm of classical conditioning where acquired representations of states of the world such as sounds and visual features are associated with predefined discrete behavioral responses such as eye blinks and freezing. Learning progresses in a very specific order, where the animal first identifies the features of the task that are predictive of a motivational state and then forms the association of the current sensory state with a particular action and shapes this action to the specific contingency. This adaptive feature selection has both attentional and memory components, i.e. a behaviorally relevant state must be detected while its representation must be stabilized to allow its interfacing to output systems. Here we present a computational model of the neocortical systems that underlie this feature detection process and its state dependent modulation mediated by the amygdala and its downstream target, the nucleus basalis of Meynert. Specifically, we analyze how amygdala driven cholinergic modulation these mechanisms through computational modeling and present a framework for rapid learning of behaviorally relevant perceptual representations.

Jordi-Ysard Puigbò, Miguel Ángel Gonzalez-Ballester, Paul F. M. J. Verschure

Describing Robotic Bat Flight with Stable Periodic Orbits

From a dynamic system point of view, bat locomotion stands out among other forms of flight. During a large part of bat wingbeat cycle the moving body is not in a static equilibrium. This is in sharp contrast to what we observe in other simpler forms of flight such as insects, which stay at their static equilibrium. Encouraged by biological examinations that have revealed bats exhibit periodic and stable limit cycles, this work demonstrates that one effective approach to stabilize articulated flying robots with bat morphology is locating feasible limit cycles for these robots; then, designing controllers that retain the closed-loop system trajectories within a bounded neighborhood of the designed periodic orbits. This control design paradigm has been evaluated in practice on a recently developed bio-inspired robot called Bat Bot (B2).

Alireza Ramezani, Syed Usman Ahmed, Jonathan Hoff, Soon-Jo Chung, Seth Hutchinson

Research of a Lensless Artificial Compound Eye

One of the challenges in designing an artificial compound eye (ACE) is the manufacturing and assembly of the ommatidia lenses. The paper presents three lensless ACE designs based on light guiding structure and an array image detector. In the solid design, deep holes pointed to different directions in space act as artificial ommatidia. Low image resolution of the solid design is improved by the shell design, where the artificial ommatidia are created within the perforated shell. In the third design, artificial ommatidia are created between the pinhole and pixels on the image detector. The best image performance is achieved with the third pinhole design. An array of pinholes on a single image detector can improve the environment perception by combining the images and assessing the object distance based on image disparity. The proposed lensless design can be easily manufactured and miniaturised to the microchip scale, and finds potential application in small and lightweight autonomous robots.

Gašper Škulj, Drago Bračun

Development of a Bio-inspired Knee Joint Mechanism for a Bipedal Robot

This paper presents the design and development of a novel biologically inspired knee design for humanoid robots. The robotic joint presented mimics the design of the human knee joint by copying the condylar surfaces of the femur and tibia. The joint significantly reduces the complexity, while preserving the mechanisms of the human knee’s motion, and the torque requirements. This joint offers the remarkable feature of being multifunctional since it separates structural and kinematic functions namely integration of (i) high level of shock absorption due to its dynamic variation of pressure between the articular surfaces and its curved profile and (ii) high mechanical advantage due to its moving center of rotation. These functions are essential for humanoid robotic limbs where performance improvement is requisite. The design demonstrates the possibility to simplify the knee linkage arrangement while still providing a moving center of rotation by dynamically changing the pressure between the joint surfaces (femur and tibia). This dynamically controlled pressure enables accurate joint movement by mimicking the human knee property of the same feature. A prototype of the joint has been developed for testing the beneficial properties designed into the model.

Alexander G. Steele, Alexander Hunt, Appolinaire C. Etoundi

Predator Evasion by a Robocrab

We describe the first robot designed to emulate specific perceptual and motor capabilities of the fiddler crab. An omnidirectional robot platform uses onboard computation to process images from a $$360^{\circ }$$360∘ camera view, filtering it through a biological model of the crab’s ommatidia layout, extracting potential ‘predator’ cues, and executing an evasion response that also depends on contextual information. We show that, as for real crabs, multiple cues are needed for effective escape in different predator-prey scenarios.

Theodoros Stouraitis, Evripidis Gkanias, Jan M. Hemmi, Barbara Webb

MantisBot Changes Stepping Speed by Entraining CPGs to Positive Velocity Feedback

This paper demonstrates and analyzes how CPGs can entrain joints of a praying mantis robot (MantisBot) to positive velocity feedback resulting in a duration change of a leg’s stance phase. We use a model of a single leg segment, as well as previously presented design techniques to understand how the gain of positive velocity feedback to the CPGs should be modulated to successfully implement the active reaction (AR) during walking. Our results suggest that the AR simplifies the descending control of walking speed, naturally producing the asymmetrical changes in stance and swing phase duration seen in walking animals. We implement the AR in neural circuits of a dynamic network that control leg joints of MantisBot, and experiments confirm that the robot modulates its walking speed as the simple model predicted. Aggregating the data from hundreds of steps in different walking directions show that the robot changes speed by altering the duration of stance phase while swing phase remains unaffected, as seen in walking animals.

Nicholas S. Szczecinski, Roger D. Quinn

EvoBot: Towards a Robot-Chemostat for Culturing and Maintaining Microbial Fuel Cells (MFCs)

In this paper we present EvoBot, a RepRap open-source 3D-printer modified to operate like a robot for culturing and maintaining Microbial Fuel Cells (MFCs). EvoBot is a modular liquid handling robot that has been adapted to host MFCs in its experimental layer, gather data from the MFCs and react on the set thresholds based on a feedback loop. This type of robot-MFC interaction, based on the feedback loop mechanism, will enable us to study further the adaptability and stability of these systems. To date, EvoBot has automated the nurturing process of MFCs with the aim of controlling liquid delivery, which is akin to a chemostat. The chemostat is a well-known microbiology method for culturing bacterial cells under controlled conditions with continuous nutrient supply. EvoBot is perhaps the first pioneering attempt at functionalizing the 3D printing technology by combining it with the chemostat methods. In this paper, we will explore the experiments that EvoBot has carried out so far and how the platform has been optimised over the past two years.

Pavlina Theodosiou, Andres Faina, Farzad Nejatimoharrami, Kasper Stoy, John Greenman, Chris Melhuish, Ioannis Ieropoulos

Using Deep Autoencoders to Investigate Image Matching in Visual Navigation

This paper discusses the use of deep auto encoder networks to find a compressed representation of an image, which can be used for visual navigation. Images reconstructed from the compressed representation are tested to see if they retain enough information to be used as a visual compass (in which an image is matched with another to recall a bearing/movement direction) as this ability is at the heart of a visual route navigation algorithm. We show that both reconstructed images and compressed representations from different layers of the auto encoder can be used in this way, suggesting that a compact image code is sufficient for visual navigation and that deep networks hold promise for finding optimal visual encodings for this task.

Christopher Walker, Paul Graham, Andrew Philippides

3D-Printed Biohybrid Robots Powered by Neuromuscular Tissue Circuits from Aplysia californica

Biohybrid robotics offers the possibility of compliant, bio-compatible actuation and adaptive behavioral flexibility via the use of muscles as robotic actuators and neural circuits as controllers. In this study, neuromuscular tissue circuits from Aplysia californica have been characterized and implemented on 3D-printed inchworm-inspired biohybrid robots, creating the first locomotive biohybrid robots with both organic actuation and organic motor-pattern control. Stimulation via the organic motor-controller is shown to result in higher muscle tension and faster device speeds as compared to external electrical stimulation.

Victoria A. Webster, Fletcher R. Young, Jill M. Patel, Gabrielle N. Scariano, Ozan Akkus, Umut A. Gurkan, Hillel J. Chiel, Roger D. Quinn

Self-organising Thermoregulatory Huddling in a Model of Soft Deformable Littermates

Thermoregulatory huddling behaviours dominate the early experiences of developing rodents, and constrain the patterns of sensory and motor input that drive neural plasticity. Huddling is a complex emergent group behaviour, thought to provide an early template for the development of adult social systems, and to constrain natural selection on metabolic physiology. However, huddling behaviours are governed by simple rules of interaction between individuals, which can be described in terms of the thermodynamics of heat exchange, and can be easily controlled by manipulation of the environment temperature. Thermoregulatory huddling thus provides an opportunity to investigate the effects of early experience on brain development in a social, developmental, and evolutionary context, through controlled experimentation. This paper demonstrates that thermoregulatory huddling behaviours can self-organise in a simulation of rodent littermates modelled as soft-deformable bodies that exchange heat during contact. The paper presents a novel methodology, based on techniques in computer animation, for simulating the early sensory and motor experiences of the developing rodent.

Stuart P. Wilson

Bio-inspired Tensegrity Soft Modular Robots

In this paper, we introduce a design principle to develop novel soft modular robots based on tensegrity structures and inspired by the cytoskeleton of living cells. We describe a novel strategy to realize tensegrity structures using planar manufacturing techniques, such as 3D printing. We use this strategy to develop icosahedron tensegrity structures with programmable variable stiffness that can deform in a three-dimensional space. We also describe a tendon-driven contraction mechanism to actively control the deformation of the tensegrity modules. Finally, we validate the approach in a modular locomotory worm as a proof of concept.

D. Zappetti, S. Mintchev, J. Shintake, D. Floreano

Consciousness as an Evolutionary Game-Theoretic Strategy

The aim of this article is to highlight the role of consciousness as a survival strategy in a complex multi-agent social environment. Clinical approaches to investigating consciousness usually center around cognitive awareness and arousal. An evolutionary approach to the problem offers a complimentary perspective demonstrating how social games trigger a cognitive arms-race among interacting goal-oriented agents possibly leading to consciousness. We begin our discussion declaring the functions that consciousness serves for goal-oriented agents. From a functional standpoint, consciousness can be interpreted as an evolutionary game-theoretic strategy. To illustrate this, we formalize the Lotka-Volterra population dynamics to a multi-agent system with cooperation and competition. We argue that for small population sizes, supervised learning strategies using behavioral feedback enable individuals to increase their fitness. In larger populations, learning using adaptive schemes are more efficient. However, when the network of social interactions becomes sufficiently complex, including the prevalence of hidden states of other agents that cannot be accessed, then all aforementioned optimization schemes are rendered computationally infeasible. We propose that that is when the mechanisms of consciousness become relevant as an alternative strategy to make predictions about the world by decoding psychological states of other agents. We suggest one specific realization of this strategy: projecting self onto others.

Xerxes D. Arsiwalla, Ivan Herreros, Clement Moulin-Frier, Paul Verschure

Using the Robot Operating System for Biomimetic Research

Biomimetics seeks to reveal the methods by which natural systems solve complex tasks and abstract principles for development of novel technological solutions. If these outcomes are to either explain behaviour, or be applied in commercial settings, they must be verified on robot platforms in natural environments. Yet development and testing of hypothesis in real robots remains sufficiently challenging for many in this highly cross-disciplinary research field that it is often omitted from biomimetic studies. Here we evaluate whether the Robot Operating Systems (ROS) can address this issue taking desert ant navigation as a case study. We demonstrate and discuss both the strengths and weaknesses of the current ROS implementation with regard to the specific needs of biomimetic researchers of varying technical experience, and describe the establishment of our central code repository with user guides to aid novice users.

Alexander Billington, Gabriel Walton, Joseph Whitbread, Michael Mangan

Modeling of Bioinspired Apical Extension in a Soft Robot

Artificial apical extension in a soft robot, inspired by biological systems from plant cells to neurons, offers an interesting alternative to movement forms found traditionally in robots. Apically extending systems can move effectively in some environments that impede traditional locomotion. Artificial apical extension has been realized using a continuous stream of surface material, thin-walled, flexible plastic, which is everted at the tip by internal pressure. Understanding artificial apical extension as a form of movement requires a model to describe and predict the capabilities of the system. Unlike many other forms of movement, the model includes components that are dependent on the previous path in addition to path-independent terms associated with actuation. The model draws inspiration from biological models of apical extension and mechanical models of compliant Bowden cable actuation, and is verified though a series of tests on physical systems that isolate each term of the model.

Laura H. Blumenschein, Allison M. Okamura, Elliot W. Hawkes

A Biomechanical Characterization of Plant Root Tissues by Dynamic Nanoindentation Technique for Biomimetic Technologies

In this work we present a study on mechanical properties of Zea mays primary roots. In order to have an accurate overview of the root structure, three different regions have been analyzed: the outer wall, the inner part, and the root cap. We used a dynamic nanoindentation technique to measure the elasticity modulus of root tissues in correspondence of different distances from the root tip. A sample holder was built to test the tip and a method conceived to separate the outer wall of the root from the inner part. As determined by dynamic nanoindentation, we measured the storage modulus of plant roots over 1–200 Hz range. We found that the values of the storage modulus along the outer wall are higher with respect to the central core. Moreover, the inner core and root cap seem to be similar in terms of elasticity modulus. This study aims to shed light on the mechanical properties of roots that significantly affect root movements and penetration capabilities. The gathered data on mechanical response and adaptive behaviours of natural roots to mechanical stresses will be used as benchmarks for the design of new soft robots that can efficiently move in soil for exploration and rescue tasks.

Benedetta Calusi, Francesca Tramacere, Carlo Filippeschi, Nicola M. Pugno, Barbara Mazzolai

Biomimetic Creatures Teach Mechanical Systems Design

For over a decade, mechanical creatures have formed the basis for the final project in a large engineering class on mechanical systems design. Each year a different real or fictitious animal provides the inspiration and design requirements for synthesizing, fabricating, and analyzing mechanisms and power transmission systems to achieve a plausible biomimetic motion. We explore why biomimetic creatures are particularly suited to learning about mechanisms, and discuss implementation details and pedagogical insights based on our experience.

Matthew A. Estrada, John C. Kegelman, J. Christian Gerdes, Mark R. Cutkosky

Soft Fingers with Controllable Compliance to Enable Realization of Low Cost Grippers

Grippers are needed to manipulate objects using robotic arms.

Keng-Yu Lin, Satyandra K. Gupta

Self-organisation of Spatial Behaviour in a Kilobot Swarm

Applications of robotic swarms often face limitations in sensing and motor capabilities. We aim at providing evidence that the modest equipment of the individual robots can be compensated by the interaction within the swarm. If the robots, such as the well-known Kilobots, have no sense of place or directionality, their collective behaviour can still result in meaningful spatial organisation. We show that a variety of patterns can be formed based on a reaction-diffusion system and that these patters can be used by the robots to solve spatial tasks. In this contribution, we present first results for applications of this approach based on ‘physically realistic’ Kilobot simulations.

Calum Imrie, J. Michael Herrmann

Bio-inspired Design of a Double-Sided Crawling Robot

A CardBot is a crawler with a thin card-sized structure, which has a limit in crawling when turned upside down. A double-sided CardBot presented in this paper is a robot that can crawl even when it is turned upside down because it can crawl on both sides. By adding one more robot body on a single-sided CardBot and sharing a motor to drive both slider cranks, a low height double-sided robot can be made. This 19 mm high, 26.39 g robot can crawl at a speed of 0.25 m/s. Thanks to the low body height, the robot can explore narrow gaps. Experiments were conducted to compare the running performance between the single-sided CardBot and the double-sided CardBot. Compared with a single-sided CardBot, only little degradation of the performance occurs due to implementation of a double-sided driving. The design has been adapted to reduce the friction, but the weakness of the shared joint due to the interaction of both cranks remains an unsolved problem. The structure of the robot will be modified to provide better performance in the future.

Jong-Eun Lee, Gwang-Pil Jung, Kyu-Jin Cho

A Closed Loop Shape Control for Bio-inspired Soft Arms

We present a model-based approach for the control of the shape of a tendon-driven soft arm. The soft robotic structure, which is inspired by an octopus arm, has variable section that allows to obtain variable curvature when actuated. The main goal of our control system is to obtain a target curvature at a desired section of the arm. The controller combines input shaping and feedback integral control in order to overcome modeling errors and constant disturbances. Simulations show the coupling between the control loop and a dynamic model of the arm.

Dario Lunni, Matteo Cianchetti, Egidio Falotico, Cecilia Laschi, Barbara Mazzolai

Learning Modular Sequences in the Striatum

The execution of habitual actions is thought to rely on the exploitation of procedural motor memories. These memories encode motor commands as organized in functional sequences with well defined boundaries in the Striatum. Here, we present a biophysical model of the striatal network composed by inhibitory medium spiny neurons (MSNs) governed by anti-hebbian STDP. We show that these two features allow for learning an arbitrary sequence through multiple exposures to cortical inputs and reproducing it under a single, non-specific excitatory drive. Our results shed light on the computational properties of biologically plausible inhibitory networks and suggest a simple, yet effective mechanism of behavioral control through striatal circuits.

Giovanni Maffei, Jordi-Ysard Puigbò, Paul F. M. J. Verschure

Spermbots: Concept and Applications

Biohybrid systems are promising solutions in micro- and nanobiotechnology due to the possibility to combine exciting biological properties of living microorganisms/cells (e.g. sensing and taxis mechanisms), and the controllability of man-made microstructures. Here we present the development of tubular and helical spermbots, a concept that refers to a sperm-based microrobot. The recent achievements include the capture, guidance and release of motile and immotile sperm cells by artificial magnetic microstructures (microtubes, microhelices or four-armed microtubes). These approaches are interesting for potential applications in in vivo assisted fertilization and targeted drug delivery. The characteristics, challenges and possibilities are discussed in detail throughout this work.

Mariana Medina-Sánchez, Veronika Magdanz, Lukas Schwarz, Haifeng Xu, Oliver G. Schmidt

An Insect-Scale Bioinspired Flapping-Wing-Mechanism for Micro Aerial Vehicle Development

Steps have been taken to reduce the size and mass of a flapping-wing-mechanism previously designed for testing artificial Manduca sexta forewings. A modified scotch-yoke mechanism is implemented to convert continuous rotary motion into flapping-wing oscillatory motion with a desired inter-wing angle and stroke amplitude. The new device measures 33.0 × 41.9 × 33.4 mm (maximum dimensions excluding the forewings) with a total mass of 15.5 g. Approximately 9.3 g of this total mass is attributed to the DC motor alone, which is significantly overpowered, indicating room for improvement. This reduction in size allows for more accurate forewing testing as it achieves proportions closer to the model organism (Manduca sexta). Furthermore, the inclusion of precise micro load cells in the testing apparatus is possible, as the total mass no longer exceeds the maximum loads permissible for these instruments. The design lends itself to employing a compliant yoke that stores and returns energy during stroke reversal thus improving the flapping mechanism’s efficiency. As we make progress in mimicking the Manduca sexta thorax, we become closer in our goal of developing a flapping-wing micro aerial vehicle (FWMAV) with flight capabilities similar to the hawkmoth.

Kenneth C. Moses, Nathaniel I. Michaels, Joel Hauerwas, Mark Willis, Roger D. Quinn

Geometric Mechanics Applied to Tetrapod Locomotion on Granular Media

This study probes the underlying locomotion principles of earliest organisms that could both swim and walk. We hypothesize that properly coordinated leg and body movements could have provided a substantial benefit toward locomotion on complex media, such as early crawling on sand. In this extended abstract, we summarize some of our recent advances in integrating biology, physics and robotics to gain insight into tetrapod locomotor coordination and control principles. Here, we observe crawling salamanders as a biological model for studying tetrapod locomotion on sloped granular substrates. Further, geometric mechanics tools are used to provide a theoretical framework predicting efficacious body motions on yielding terrain. Finally, we employ these coordination strategies on a robophysical salamander model traversing a sandy slope. This analysis of salamander-like robotic motion in granular media can be seen as a first application of how tools from geometric mechanics can provide insight into the character and principles of legged locomotion.

Yasemin Ozkan Aydin, Baxi Chong, Chaohui Gong, Jennifer M. Rieser, Jeffery W. Rankin, Krijn Michel, Alfredo G. Nicieza, John Hutchinson, Howie Choset, Daniel I. Goldman

Bioinspired Grippers for Natural Curved Surface Perching

Perching and climbing as animals do is useful to aerial robots for extending mission life and for interacting with the physical world because flight is energetically costly. This paper presents the design and modeling of a claw or spine based gripper for perching on rough, curved surfaces. Drawing inspiration from the opposed grip techniques found in animals, we focus on the design considerations associated with surface geometry and preload. A model elucidates the relationship between these variables, and a mechanism demonstrates the effectiveness of the opposed grip technique.

William R. T. Roderick, Hao Jiang, Shiquan Wang, David Lentink, Mark R. Cutkosky

Collisional Diffraction Emerges from Simple Control of Limbless Locomotion

Snakes can utilize obstacles to move through complex terrain, but the development of robots with similar capabilities is hindered by our understanding of how snakes manage the forces arising from interactions with heterogeneities. To discover principles of how and when to use potential obstacles, we studied a desert-dwelling snake, C. occipitalis, which uses a serpenoid template to move on homogeneous granular materials. We tested the snake in a model terrestrial terrain—a single row of vertical posts—and compared its performance with a robophysical model. Interaction with the post array resulted in reorientation of trajectories away from the initial heading. Combining trajectories from multiple trials revealed an emergent collisional diffraction pattern in the final heading. The pattern appears in both the living and robot snake. Furthermore, the pattern persisted when we changed the maximum torque output of the robot motors from 1.5 N-m to 0.38 N-m in which case local deformation of the robot from the serpenoid curve appears during interaction with the posts. This suggests the emergent collisional diffraction pattern is a general feature of these systems. We posit that open-loop control of the serpenoid template in sparse terrains is a simple and effective means to progress, but if adherence to a heading is desired more sophisticated control is needed.

Perrin E. Schiebel, Jennifer M. Rieser, Alex M. Hubbard, Lillian Chen, Daniel I. Goldman

Binocular Vision Using Synthetic Nervous Systems

This paper presents a system that allows our 29 degree-of-freedom robot, MantisBot to detect the distance to an object labeled as “prey”. The prey is detected using two Pixy cameras with build-in software for object recognition. Each camera outputs analog signals encoding the azimuth and elevation of the prey, which are interpreted by MantisBot’s synthetic nervous system. An analytical derivation of the distance of an object from the robot as a function of the azimuth values from each camera produces training data for a neural network. The network learns this relationship, endowing MantisBot with binocular depth perception.

Anna Sedlackova, Nicholas S. Szczecinski, Roger D. Quinn

Cell Patterning Method by Vibratory Stimuli

Recently, micro robots driven by muscle cells attract a lot of attention. The shape of the robot can be controlled by modifying the growing of cells. This paper proposes a new cell patterning method by vibratory stimuli. The stimuli can detach redundant cells, and a certain cell pattern can be formed. In the experiment, around 80 Hz vibratory stimulus was applied to skeletal muscle cells via a PDMS scaffold and the cells were observed by camera. As a result, we found that a vibratory stimulus promotes detaching of the cells, especially at the moment of cell division. We also found that the increment of the cells depends on the vibratory energy. These results suggested that we can control the number of cells by vibration stimulus. Actually, we made specific cell pattern by the vibration stimulus.

Ippei Tashiro, Masahiro Shimizu, Koh Hosoda

Dry Adhesion of Artificial Gecko Setae Fabricated via Direct Laser Lithography

Biomimetics has introduced a new paradigm: by constructing structures with engineered materials and geometries, innovative devices may be fabricated. According to this paradigm, both shape and material properties are equally important to determine functional performance. This idea has been applied also in the field of the microfabrication of smart surfaces, exploiting properties already worked out by nature, like in the case of self-cleaning, drag reduction, structural coloration, and dry adhesion. Regarding dry adhesive properties, geckos represent a good example from which we take inspiration, since they have the extraordinary ability to climb almost every type of surface, even smooth ones, thanks to the hierarchical conformation of the fibrillary setae in their toe pads. Due to this design, they can increase the area of contact with a surface and thus the amount of attractive van der Waals forces. While reproducing with artificial materials the same functional morphology of gecko’s pads is typically not achievable with traditional microfabrication techniques, recently Direct Laser Litography offered new opportunities to fabrication of complex three-dimensional structures in the microscale with nanometric resolution. Using direct laser lithography, we have fabricated artificial gecko setae, reproducing with unprecedented faithfulness the natural morphology in the same dimensional scale. Adhesion force of artificial setae toward different surfaces have been tested in dry condition by means of a dedicated setup and compared with natural ones.

Omar Tricinci, Eric V. Eason, Carlo Filippeschi, Alessio Mondini, Barbara Mazzolai, Nicola M. Pugno, Mark R. Cutkosky, Francesco Greco, Virgilio Mattoli

Gesture Recognition Through Classification of Acoustic Muscle Sensing for Prosthetic Control

(Extended Abstract)

In this paper we present the initial evaluation of a new upper limb prosthetic control system to be worn on the residual limb, which is capable of identifying hand gestures through muscle acoustic signatures (mechanomyography, or MMG) measured from the upper arm. We report the development of a complete system consisting of a bespoke inertial measurement unit (IMU) to monitor arm motion and a skin surface sensor capturing acoustic muscle activity associated with digit movement. The system fuses the orientation of the arm with the synchronized output of six MMG sensors, which capture the low frequency vibrations produced during muscle contraction, to determine which hand gesture the user is making. Twelve gestures split into two test categories were examined, achieving a preliminary average accuracy of 89% on the offline examination, and 68% in the real time tests.

Samuel Wilson, Ravi Vaidyanathan

Erratum to: Bio-inspired Design of a Double-Sided Crawling Robot

Jong-Eun Lee, Gwang-Pil Jung, Kyu-Jin Cho


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