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2007 | Buch

Mobile Robot Map Learning from Range Data in Dynamic Environments Kapitel lesen Erstes Kapitel lesen

Autonomous Navigation in Dynamic Environments

herausgegeben von: Christian Laugier, Raja Chatila

Verlag: Springer Berlin Heidelberg

Buchreihe : Springer Tracts in Advanced Robotics

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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Über dieses Buch

At the dawn of the new millennium, robotics is undergoing a major transformation in scope and dimension. From a largely dominant industrial focus, robotics is rapidly expanding into the challenges of unstructured environments. Interacting with, assi- ing, serving, and exploring with humans, the emerging robots will increasingly touch people and their lives. The goal of the new series of Springer Tracts in Advanced Robotics (STAR) is to bring, in a timely fashion, the latest advances and developments in robotics on the basis of their significance and quality. It is our hope that the wider dissemination of research developments will stimulate more exchanges and collaborations among the research community and contribute to further advancement of this rapidly growing field. The collection edited by Christian Laugier and Raja Chatila is the third one in the series on mapping and navigation, and is focused on the problem of autonomous navigation in dynamic environments. The state of the art is surveyed, a number of challenging technical aspects are discussed and upcoming technologies are addressed. The ambitious goal is to lay down the foundation for a broad class of robot m- ping and navigation methodologies for indoors, outdoors, and even exploratory m- sions. Future service robots and intelligent vehicles are waiting for effective solutions to such kind of problems.

Inhaltsverzeichnis

Frontmatter

Dynamic World Understanding and Modelling for Safe Navigation

Frontmatter
Mobile Robot Map Learning from Range Data in Dynamic Environments
Abstract
The problem of generating maps with mobile robots has received considerable attention over the past years. Most of the techniques developed so far have been designed for situations in which the environment is static during the mapping process. Dynamic objects, however, can lead to serious errors in the resulting maps such as spurious objects or misalignments due to localization errors. In this chapter, we consider the problem of creating maps with mobile robots in dynamic environments. We present two approaches to deal with non-static objects. The first approach interleaves mapping and localization with a probabilistic technique to identify spurious measurements. Measurements corresponding to dynamic objects are then filtered out during the registration process. Additionally, we present an approach that learns typical configurations of dynamic areas in the environment of a mobile robot. Our approach clusters local grid maps to identify the typical configurations. This knowledge is then used to improve the localization capabilities of a mobile vehicle acting in dynamic environments. In practical experiments carried out with a mobile robot in a typical office environment, we demonstrate the advantages of our approaches.
Wolfram Burgard, Cyrill Stachniss, Dirk Hähnel
Optical Flow Approaches for Self-supervised Learning in Autonomous Mobile Robot Navigation
Abstract
A common theme in autonomous mobile robotics is the desire to sense farther ahead of the robot than current approaches allow. This greater range would enable earlier recognition of hazards, better path planning, and higher speeds. In scenarios where the long range sensor modality is computer vision this has led to interest in developing techniques that can effectively identify and respond to obstacles at greater distances than those for which stereo vision methods are useful. This paper presents work on optical flow techniques that leverage the difference in appearance between objects at close range and the same objects at more distant locations in order to interpret monocular video streams in a useful manner. In particular, two applications are discussed: self-supervised off-road autonomous navigation, and adaptive road following in unstructured environments. Examples of the utility of the optical flow techniques discussed here in both arenas are provided.
Andrew Lookingbill, David Lieb, Sebastian Thrun
Steps Towards Safe Navigation in Open and Dynamic Environments
Abstract
Autonomous navigation in open and dynamic environments is an important challenge, requiring to solve several difficult research problems located on the cutting edge of the state of the art. Bassically, these problems can be classified into three main categories: SLAM in dynamic environments; Detection, characterization, and behavior prediction of the potential moving obstacles; On-line motion planning and safe navigation decision based on world state predictions. This paper addresses some aspects of these problems and presents our latest approaches and results. The solutions we have implemented are mainly based on the followings paradigms: Characterization and motion prediction of the observed moving entities using bayesian programming; On-line goal-oriented navigation decisions using the Partial Motion Planning (PMP) paradigm.
Christian Laugier, Stéphane Petti, Dizan Vasquez, Manuel Yguel, Thierry Fraichard, Olivier Aycard

Obstacle Avoidance and Motion Planning in Dynamic Environments

Frontmatter
Provably Safe Motions Strategies for Mobile Robots in Dynamic Domains
Abstract
We present in this paper a methodology for computing the maximum velocity profile over a trajectory planned for a mobile robot. Environment and robot dynamics as well as the constraints of the robot sensors determine the profile. The planned profile is indicative of maximum speeds that can be possessed by the robot along its path without colliding with any of the mobile objects that could intercept its future trajectory. The mobile objects could be arbitrary in number and the only information available is their maximum possible velocity. The velocity profile also enables to deform planned trajectories for better trajectory time. The methodology has been adopted for holonomic and non-holonomic motion planners. An extension of the approach to an online real-time scheme that modifies and adapts the path as well as velocities to changes in the environment such that both safety and execution time are not compromised is also presented for the holonomic case. Simulation and experimental results illustrate the efficacy of this methodology.
Rachid Alami, K. Madhava Krishna, Thierry Siméon
Motion Planning in Dynamic Environments
Abstract
Motion planning in dynamic environments is made possible using the concept of velocity obstacles. It maps the colliding velocities of the robot with any moving or static obstacle to the robot’s velocity space. Collision avoidance is achieved by selecting the robot velocity outside the velocity obstacles. This concept was first proposed in [3] for the linear case of obstacles moving on straight line trajectories, and is extended here to obstacles moving along arbitrary trajectories. The non-linear velocity obstacle (NLVO) takes into account the shape, velocity and path curvature of the moving obstacle. It allows to select a single avoidance maneuver (if one exists) that avoids any number of obstacles that move on any known trajectories. The nonlinear v-obstacle can be generated as a time integral of the colliding velocities, or by computing its boundaries using analytic expressions.
Zvi Shiller, Frederic Large, Sepanta Sekhavat, Christian Laugier
Recursive Agent Modeling with Probabilistic Velocity Obstacles for Mobile Robot Navigation Among Humans
Abstract
An approach to motion planning among moving obstacles is presented, whereby obstacles are modeled as intelligent decision-making agents. The decision-making processes of the obstacles are assumed to be similar to that of the mobile robot. A probabilistic extension to the velocity obstacle approach is used as a means for navigation and modeling uncertainty about the moving obstacles’ decisions.
Boris Kluge, Erwin Prassler
Towards Real-Time Sensor-Based Path Planning in Highly Dynamic Environments
Abstract
This paper presents work on sensor-based motion planning in initially unknown dynamic environments. Motion detection and probabilistic motion modeling are combined with a smooth navigation function to perform on-line path planning and replanning in cluttered dynamic environments such as public exhibitions. The SLIP algorithm, an extension of Iterative Closest Point, combines motion detection from a mobile platform with position estimation. This information is then processed using probabilistic motion prediction to yield a co-occurrence risk that unifies dynamic and static elements. The risk is translated into traversal costs for an E* path planner. It produces smooth paths that trade off collision risk versus detours.
Roland Philippsen, Björn Jensen, Roland Siegwart

Human-Robot Physical Interactions

Frontmatter
Tasking Everyday Interaction
Abstract
An important problem in the design of mobile robot systems for operation in natural environments for everyday tasks is the safe handling of encounters with people. People-People encounters follow certain social rules to allow co-existence even in cramped spaces. These social rules are often described according to the classification termed proxemics. In this paper we present an analysis of how the physical interaction with people can be modelled using the rules of proxemics and discuss how the rules of embodied feedback generation can simplify the interaction with novice users. We also provide some guidelines for the design of a control architecture for a mobile robot moving among people. The concepts presented are illustrated by a number of real experiments that verify the overall approach to the design of systems for navigation in human-populated environments.
Elena Pacchierotti, Patric Jensfelt, Henrik I. Christensen
Backmatter
Metadaten
Titel
Autonomous Navigation in Dynamic Environments
herausgegeben von
Christian Laugier
Raja Chatila
Copyright-Jahr
2007
Verlag
Springer Berlin Heidelberg
Electronic ISBN
978-3-540-73422-2
Print ISBN
978-3-540-73421-5
DOI
https://doi.org/10.1007/978-3-540-73422-2

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