Modelling and Simulation for Autonomous Systems

In this paper, we report on the developed system for assessment of ground unit terrain traversal cost using aerial reconnaissance of the expected mission environment. The system combines an aerial vehicle with ground robot terrain learning in the traversal cost modeling utilized in the mission planning for ground units. The aerial vehicle is deployed to capture visual data used to build a terrain model that is then used for the extraction of the terrain features of the expected operational area of the ground units. Based on the previous traversal experience of the ground units in similar environments, the learned model of the traversal cost is employed to predict the traversal cost of the new expected operational area to plan a cost-efficient path to visit the desired locations of interest. The particular modules of the system are demonstrated in an experimental scenario combining the deployment of an unmanned aerial vehicle with a multi-legged walking robot used for learning the traversal cost model.

In this paper selected methods used for Unmanned Aerial Vehicles (UAVs) swarm simulation are presented. Proposed approach is based on Particle Swarm Optimization algorithm applied for a search problem in a large-scale terrain with enhancements like collision avoidance, leadership or formation arrangement mechanisms. The uniqueness of proposed work relies on appliance these mechanisms (separately or in combined way) to the PSO algorithm, whereas they have not been designed in the presented manner. Moreover, the mechanisms are meant to keep the PSO algorithm idea and its simplicity.The UAVs swarm simulation is performed in original distributed simulation environment (built completely from the scratch) with the alternative communication provided by DIS or HLA protocols. The environment consists of a constructive component (responsible for multiagent and multiresolution modelling) for UAVs behaviours simulation and the VBS3 virtual simulator handling 3D visualizations and another objects control.

This paper presents a model of a specific nontrivial robotic manipulator used in practice for example for material handling. The main goal is to build a model of forward and inverse kinematics, using classical methods and differential geometry. Thus, this model is mahtematically precise. The kinematics of the robotic manipulator is also solved and visualized in MATLAB for better clarity. The acquired knowledge will be used to compare this precise kinematic model with other models that are obtained by other less explored methods.

Robot navigation inside a building relies on efficient localization of the device for motion planning. Mapping is also to be considered, and, along with high-end and costly solutions, low-cost devices prove to be useful. Therefore, this article presents well known low-cost solutions, with a methodology tailored for the mapping of a complex building floor. It takes care of the limitations of the sensor with some post-processing added to obtain beautiful point cloud as results of the experiments. Data acquired during an intensive campaign of measures by a trainee are presented. Finally, the analysis of the results shows the impact of the point cloud’s size and the storage needed.

Vehicle detection on aerial imagery taken with UAVs (unmanned aerial vehicles) plays an important role in many fields of application, such as traffic monitoring, surveillance or defense and rescue missions. Deep learning based object detectors are often used to overcome the resulting detection challenges. The generation of training data under different conditions and with the necessary variance is difficult and costly in real life. Therefore, virtual simulation environments are meanwhile often applied for this purpose. Our current research interests focus on the difference in performance, also called reality gap, of trainable vehicle detectors between both domains and the influence of differently designed training data. A general method for automatic image annotation with the required bounding boxes is described. In the first part of the investigations the training behavior of YOLOv3 on the natural UAVDT data set is analyzed and examined to what extent algorithms trained with natural images can be evaluated in the simulation. Finally, it is shown which performance can be achieved by exclusively synthetic training and how the performance can be improved by synthetic extension of the natural training set.

Multi-Robot Task Allocation (MRTA) will gain much importance by the rise of autonomous vehicles and the Internet of Things (IoT) where several agents coordinate and work for a common goal. Due to their distributed nature, hardware complexity and environmental constraints, constructing and testing multi-robot systems may be expensive, dangerous and time-consuming. MRTA includes sub-problems such as coordination strategy, bid valuation, path planning, terrain complexity, robot design, path optimization, and overall optimization. There is a need for building a generic MRTA model to experiment with these numerous combinations in a controlled and automated fashion. This paper presents the structure of the MRTA generic simulation model which is designed to search for the optimal combination of MRTA taxonomy elements. An MRTA Simulation Tool (MRTASim) is designed to adapt the generic model to specific cases and to run simulations for real-life scenarios. Decision-makers can build their own MRTA models and they can be sure for the feasibility of large distributed and collaborated systems before initiating huge investments.

In this article we assemble kinematic machine chains using homogeneous matrices and we get error vector DX, DY, DZ. At the same time we are working with error vector obtained using standard software tools by multilateration method. We compare the resulting values of both approaches and we are discussing.

In this paper, we concern learning of terrain types based on the traversal experience observed by a hexapod walking robot. The addressed problem is motivated by the navigation of unmanned ground vehicles in long-term autonomous missions in a priory unknown environments such as extraterrestrial exploration. In such deployments, the robotic vehicle needs to learn hard to traverse terrains to improve its autonomous performance and avoid possibly dangerous areas. We propose to utilize Growing Neural Gas for terrain learning to capture the robot experience with traversing the terrain and thus learn a classifier of individual terrain types. The classifier is learned using a real time-series dataset collected by a hexapod walking robot traversing various terrain types. The learned model can be utilized to predict the traversal cost of newly observed terrains to support decisions on where to navigate next.

Intelligent and/or autonomous vehicle technologies are rapidly growing to meet the needs of marine safety and transport efficiency. One of the requirements to manage autonomous vehicles includes the integration between route planning and automatic motion control. In the authors’ opinion, the latter could be sketched in three different layers: obstacle detection, planning and actuation. Moreover, the three layers should be able to interact in real-time. Dealing with such a challenging task, one of the best techniques to develop and test the logic is the use of the time-domain simulation. In the present work, a simulation model, integrating a path planning algorithm in the presence of obstacles with a track keeping controller, is developed. The path planning is based on a modified version of the Rapidly-exploring Random Tree (RRT*) algorithm. The track keeping is based on the Line-of-Sight (LOS) waypoints navigation for underactuated vessels. To achieve more reliable results, a detailed ship simulation model is used as a benchmark. Different scenarios and navigation modes are successfully tested, and the results are presented and analysed.

An innovative use of the Extended Kalman Filter (EKF) is proposed to perform automatic take off and landing by the rejection of disturbances due to turbulence.By using two simultaneously working Extended Kalman Filters, a procedure is implemented: the first filter, by using measurements gathered in turbulent air, estimates wind components; the second one, by using the estimated disturbances, obtains command laws that are able to reject disturbances.The fundamental innovation of such a procedure consists in the fact that the covariance matrices of process (Q) and measurement (R) noise are not treated as filter design parameters. In this way determined optimal values of the aforementioned matrices lead to robust control laws.At any moment, during the estimation process, the EKF employs the optimal values of Q and R. To determine these ones, adequate constrains, related to flight path characteristics, are inserted into the algorithm.In particular, to determine wind components, the constrains are imposed to elevation, altitude and longitudinal position; whereas, to determine control actions, the constrains are imposed to an adequate performance index obtained by using measurements gathered by a small set of sensors (IMU, air data boom and a low rate GPS) in turbulent air.

This paper discusses the mapping of ionizing radiation on building surfaces by using an unmanned aircraft system (UAS). The mapping task itself is important for the decommissioning of various nuclear facilities, for example, fuel processing sites or nuclear waste storage areas. The surface map can inform relevant authorities about the strength and distribution of radioactive sources inside an investigated building. The UAS is employed for the given purpose thanks to its many advantages, such as low price and the possibility of approaching a surface closely; moreover, the applied technique enables the user to create a 3D model of the target via such means as aerial photogrammetry. We set up an approximate model of a real building within our university campus, capturing the inner structures and subdividing the examined area into partial sectors, or groups, according to the construction materials; this criterion is relevant for simulating radiation propagation. The choice of the actual study location allows future experimental verification of the proposed methods; moreover, we can work with authentic photogrammetric products obtained during previous flights. In the project, two surface mapping methods are designed and tested on the simulated scenario, which assumes several radiation sources inside the building. One of the techniques simply assigns the measured value to the nearest point of the photogrammetric building model, while the other considers also rough information on the position of the sources to estimate the surface intensity more precisely. For better interpretation, the scattered data points are interpolated. Finally, the results of both approaches are compared with the computed reference map.

We present a solution to the inverse kinematics problem of 5 DoF IRB4400 industrial serial robot arm based on conformal geometric algebra. The algorithm relies on the intersections of geometric primitives such as lines, circles, planes and spheres. This approach provides clear geometric intuition about the problem. In the solution we analytically describe the way how to find the final position of each joint and based on these positions we also determine the joint parameters (i.e. the angles) including the orientation according to the robot’s construction.

This paper presents a novel design of an active grasping mechanism for autonomous Unmanned Aerial Vehicles (UAVs) aimed to carry ferromagnetic objects in assembly tasks. The proposed design uses electromagnets along with a combination of sensors to provide fast and reliable feedback. The designed gripper with its control system is aimed to be fully autonomous and will be employed in a task in the Mohamed Bin Zayed International Robotics Challenge (MBZIRC) 2020 competition where a group of autonomous UAVs cooperatively build a wall. The design is optimized for the Tarot 650 drone platform and for outdoor operation, while taking into consideration robustness of performance and resilience to aerial maneuvers. We describe the design of the gripper, the overall system and the approach used to obtain the feedback from the sensors which is crucial for robust aerial grasping and for high level planning of the assembly task. Various outdoor experiments were conducted on fabricated bricks to verify the proposed approach and to demonstrate the ability of the system to autonomously build a wall.

In this paper the Carrier-Vehicle Travelling Salesman Problem (CV-TSP) is extended to the case of 2 carriers and one small vehicle. The paper defines a minimum-time trajectory mission plan for the visit of a group of target points by the small vehicle. In this scenario, the main goal is to optimize the use of both carriers as a support of the vehicle. A Mixed-Integer Second Order Conic Programming (MISCOP) formulation is proposed for the case of a given order of visit. Additionally, the authors develop a fast heuristic which provides close to optimal results in a decent computational time. To end the paper several simulations are computed to show the effectiveness of the proposed solution.

In this paper, we address a problem of the exploration of large-scale subterranean environments using autonomous ground mobile robots. In particular, we focus on an efficient data representation of the large-scale elevation map, where it is desirable to capture the shape of the terrain to avoid areas not traversable by a robot. Subterranean environments such as mine tunnel systems can be in units of kilometers large, but only a relatively small portion of the environment represents observable parts. Therefore, uniform grid-based elevation maps with resolution in units of centimeters are not memory efficient, and more suitable are hierarchical tree-based structures. However, hierarchical structures suffer from the increased computational requirements of accessing particular grid cells needed in determination of the navigational goals or evaluation of the terrain traversability in planning safe and cost-efficient paths. We propose a speed-up technique to combine the benefits of uniform grid-based and tree-based representations. The proposed elevation map representation keeps the memory footprint low using tree structure but enables fast access to the grid cells corresponding to the robot surroundings. The efficiency of the proposed data representation is demonstrated in an experimental deployment of the autonomous exploration of outdoor and subterranean environments.

Rovers and other unmanned ground vehicles (UGV) were a matter of space exploration or post-disaster reconnaissance. Rovers work in tough conditions where presence of a human is uncomfortable or dangerous. This results in main requirements for rover - durable chassis, stable communication for direct control or transfer of data and task-specific tools for experiments and measurements. In this article we propose solutions for fulfilling those requirements.

M&S of systems, their dynamic structures and particularly the behaviour of internal component objects, should be performed at the level of detail which is adequate to the problem and modelling purpose defined. In the scenarios related to the complex world strictly one level is insufficient - there is necessary to build a multi-resolution model that represents structures and actions at different levels of detail. This is the main and direct reason for the application of the Multi-resolution Agent Model (MrAM) approach in the simulation with functions for a state’s transformation (aggregation/disaggregation). It is common practice to implement methods of resolution adaptation in such a way that they are completely closed in the compiled program code. Meanwhile, the multiplicity of different possible scenarios regarding group and individual behaviours indicates that there are necessary software constructions enabling the end user to create both new, open models of behaviours and algorithms for aggregation/disaggregation of the state.Moreover, the environment surrounding agents influences target states differently at different moments in time. The article proposes an approach to determining the consensus state of agents with the use of machine learning methods.The consensus between agents according to the appropriate approach, depending on the conditions and state, will be a generalized method adaptable to the environment of agents. We propose the reinforcement learning model as a multiagent game in order to achieve HRE state and thus complete disaggregation.To meet the requirements, the original Java-based framework for hybrid simulation (discrete, event-based and continuous) with the ability to model the object as an agent at multiple levels, automatic triggering of updates on all modelled levels, and Groovy-based scripts. The scripting technology is integrated with the standalone Java software and enables the implementation of behaviors and state transformations that are really open in scripts.The article presents the proposed framework solution on the example of an autonomous system model composed of many cooperating objects. We share our experiences related to the extension of the “SymSG Border Tactics” simulation environment dedicated to CAX exercises in The Poland Border Guard.

This paper deals with the model of observation posts deployment in the area of operations. The goal of the model is to find optimal positions for a number of observation posts in order to observe as large area of interest as possible provided that the tactical requirements and conditions for the task at hand are kept. The first part of the paper presents the mathematical formulation of the model. Then, a stochastic algorithm based on the simulated annealing principle is proposed for the problem solution. Next part shows the application of the model which is implemented in the Tactical Decision Support System, the purpose of which is to aid commanders on the tactical level in their decision-making process. The last part shows results from experiments conducted on a set of benchmark problems which are based on the typical military scenarios.

We modify the core axioms towards the so–called weighted solution. We also discuss the corresponding properties of Myerson value. We present a modification of axiomatic definition of solution on the set of games w.r.t. tree structure of the set of players. Significant properties of new solutions are discussed. The theory is motivated by tactical decisions in army and the notions are demonstrated on an example.

The missile guidance system is one of the most important component of a missile itself and it should be reflected also in a simulation environment. Therefore, the tactical simulators should contain as precise missile models as possible. Unfortunately, the most of contemporary simulators use just a very simplified mathematical models. To bring the simulation closer to the reality the model of each missile should work according to a specific guidance method containing a real guidance equations. The article focuses on the creation and implementation of specific missile characteristics into a tactical simulation environment and outlines its usage for the future simulator of an autonomous air defense system.

The aim of this paper is to describe development of an evaluation system for practical comparison of wireless communication technologies used in field robotics. The evaluation system is capable of measuring data rate, drop rate and latency in both communication directions. The evaluation system closely simulates communication of a teleoperated field robot with its base station - which is a highly asymmetric communication link, because there is a high data rate video feed transmission from the robot to the base station.This paper describes specifics of wireless communication links used in field robotics and considerations that need to be taken into account when developing such system. The wireless link evaluation system is described from hardware and software point of view.

The Subterranean Challenge (SubT) is a contest organised by the Defense Advanced Research Projects Agency (DARPA). The contest reflects the requirement of increasing safety and efficiency of underground search-and-rescue missions. In the SubT challenge, teams of mobile robots have to detect, localise and report positions of specific objects in an underground environment. This paper provides a description of the multi-robot heterogeneous exploration system of our CTU-CRAS team, which scored third place in the Tunnel Circuit round, surpassing the performance of all other non-DARPA-funded competitors. In addition to the description of the platforms, algorithms and strategies used, we also discuss the lessons-learned by participating at such contest.

A key feature of a military operation is its non-repeatability, with a results that can affect history. The success of a military operation depends on the ability and art of the commanders and their staffs to implement the decision-making process. Critical part of the decision-making process is the creation of variants of the activities of own troops based on variants of enemy activities and based on knowledge of the structure and behavior of the enemy. In the case of ground air defense, the variants (courses) of actions are primarily designed with respect to the anticipated air enemy in air defense clusters. Courses of actions can be mutually assessed based on the effectiveness and efficiency of air defense clusters. Nowadays, the creation of courses of actions is a time-consuming stage in the decision-making process, which depends on the knowledge, skills and experience of the commanders and staffs. War games used to evaluate courses of actions do not allow to assess all possible situations and responses. Modeling and simulation is an appropriate apparatus for evaluating variants of the air enemy’s activities and based on such results it is possible to decide on the optimum battle configuration of own forces. The model of the enemy’s activity structure, own troops, and environment will allow to simulate a large number of combat activities on both sides and assess the most appropriate option for own troops. Probabilistic methods such as the Monte Carlo method and its derivatives will allow the evaluation of the activity variants. The results of the use of probabilistic methods will then be used in the application of game theory and determination of optimal courses of actions, strategy in the sense of game theory, own forces. By programming the models, commanders and staffs will be given a tool to assess the effectiveness and efficiency of ground-based air defense clusters and will be able to make adequate decisions. This article brings a concept of such tool.

The importance of communication networks increases with every new system and over the last 20 years these networks have become a critical infrastructure like the electricity network. Besides all cables and connections, the core of these networks are the information transfer systems with routers and switches. To guarantee the availability of these systems resilience is needed. The present paper evaluates possibilities to ensure resilience by the training for IT-Experts. For this purpose, typical errors and the way to fix these failures are inspected. A concept for combining the simulation of failures in systems and the mean time to restore (MTTR) was developed. Related to the information provided by the simulation, the concept was extended by details about the experts training level to specify the MTTR. Our approach is a possible way for providing information related to the field of training in context between experience and computer-generated information. Goal of our work is to enlarge the resilience of systems in critical infrastructures like communication networks by drawing conclusions about the IT-Experts training.

This paper presents a virtual/augmented reality (VR/AR) framework to enhance the situational and spatial awareness at tactical level in the underwater domain. Technology supporting operations in this challenging environment has been scarcely explored in the literature. Consequently, a detailed study has been carried out in order to identify all the steps necessary to transform underwater data into formats suitable for the representation in VR/AR environments. In this context, an application for enhancing the situational and, more precisely, the spatial awareness in the maritime domain has been drafted and proposed.

Smart Ships represent the next generation of ships that use ICT to connect all the devices on board for integrated monitoring and safety management. In such a context, the Shipboard Power System (SPS) is critical to the survival and safety of the ship because many accidents are due to electrical failures. The SPS reconfiguration consists of a variation of the electrical topology to supply energy to critical services successfully. The proposed reconfiguration procedure uses an autonomous and mission-oriented approach, and it employs a generic-purpose self-adaptive Fault Management System.It delivers a set of possible runtime solutions that properly consider the current ship mission and operating scenario while dealing with multiple failures.Solutions, achieving a partial reconfiguration of the system, are considered when a full recovery strategy is not available according to the current ship conditions.

Robotics and artificial intelligence (AI) are changing business, and transforming defense and warfare. In particular, the paper addresses the challenges from the easy availability of AI resources to be applied to drones that are already an asymmetric threat, and the issues concerning the Lethal Autonomous Weapons Systems (LAWS) that promise military advantages like the reduction of deployed soldiers, loss of civilians, damage of infrastructures, and the rapid support to decision-making. Autonomous systems can also help in disaster and humanitarian operations. However, the deployment of systems that are more and more autonomous from humans poses multidimensional challenges for their certification, compliance with ethics and international humanitarian law and present risks to security. Mechanisms for the mitigation of their proliferation to malicious non-state actors and a meaningful human control should be implemented. War gaming in a theoretical, risk-safe environment can assess the threats and impact of robotic weapons on future defense planning and operations. From the example of the lessons learned in the automotive industry, simulation can be applied to digital twin models of the AI system and the environment to train and test under unexpected and extreme conditions, unanticipated hardware and software failure situations and in complex dynamic operational contexts including interaction with civilian entities.

The article is focused on the area of modelling and simulation of the Surface Based Air Defence (SBAD) units, especially on issues of Command and Control (C2) and Tactical Battle Management Functions (TBMF) in the simulator environment. The article describes the general overview of the C2 and TBMF of the SBAD units, presents the state of art in today’s tactical simulators (PRESAGIS simulation SW) and suggests the possible way of implementation to reach the model and simulation corresponding with the real world entities.

Military Autonomous Systems are one of the critical elements in the current and future operations. System with a level of autonomy is known in the military for more than 50 years. However fully autonomous systems have not been yet fully operationalized. Taken as a military capability, autonomous system must be analyzed, designed and implemented to reflect all Doctrine, Organization, Training, Materiel, Leadership, Personnel, Facilities, Interoperability and Information (DOTMLPFI) aspects. The first part of the paper describes autonomous system current state of the art and challenges following DOTMPLFI classification. Secondly, the modeling and simulation paradigms, Discrete Event Simulation, Agent Based Modelling and System Dynamics are proposed to be the right candidate for each DOTMLPFI aspects of AS capability development. There are two Use Cases, the first one based on Agent Based Modeling paradigm and the second one based on System Dynamics paradigm, both demonstrating advantages and drawbacks of a single modelling and simulation paradigm. The last part of the paper discusses differences, and mutual support of these two paradigms in the context of AS capability development.

The text deals with the psychological and ethical aspects of using autonomous weapons. It focuses on controversies associated with the contemporary use of robotic weapons, respectively unmanned weapon systems and the possible use of autonomous weapons in future armed conflicts led by state and non-state actors. These means can achieve significant success at the tactical level while minimizing their own human loss or even the complete absence of their own human element at the point of projection of military force. However, their use may, on the other hand, be in direct contradiction with the long-term strategic objectives of their user and partially delegitimize his intentions. War, as a complex phenomenon, is not limited to direct combat activity, and in relation to a number of non-military factors, the use of autonomous weapons can be problematic from both ethical and psychological points of view. Thus, the military and technological superiority of one party may be partially offset in some conflicts by the ideological superiority of the weaker adversary. The text tries to characterize the main controversies that the deployment of autonomous weapon systems can represent in this respect.

The planning and decision-making process for conducting combat operations must take into account the development of the situation in a number of diverse areas that affect the action of forces and resources on the battlefield. An analysis of their impact can be done through computational programs and mathematical algorithmic models, using a raster representation of geographic and tactical data. Raster data layers in the mathematical model allow for a combination of surface effects, terrain elevation and weather effects as well as the forces and vehicles of both the enemy and friendly units. Mathematical calculations result in a maneuver route, optimized based on predefined criteria, such as speed and safety. The result can have a double use. In case of the maneuver of enemy forces and vehicles, it is possible to simulate their probable future activity. The result of combining all effects of the situation on the battlefield is the optimal route of maneuver of friendly units designed in several seconds. Utilization of such software will provide commanders with substantive independence and speed of decision-making in the course of military operations. Sharing designed maneuver route with all adjacent units and higher headquarters also enables to coordinate the activity of all superior task forces.

Technical Activity in the NATO Modelling and Simulation Group (MSG–145) for operationalization of new Simulation Interoperability Standards Organization (SISO) C2SIM standard is approaching its completion. This second generation of C2SIM standards from SISO for coalitions to interoperate their national command and control (C2) and simulation systems is ready for balloting and being the basis of a STANAG. This standard for synthetic battlespace can have a great impact on the effectiveness of coalition military operations and training. MSG-145 conducted extensive testing to validate this standard. Modelling and Simulation Centre of Excellence participated to these efforts focusing in extending the operationalization of the C2SIM standard to the Autonomous System functional area in the framework of the Research on Robotics for Concept and Capability Development (R2CD2) project. In this paper, at first the rational and goals of the R2CD2 project are illustrated, then the process followed to develop an extension of the C2SIM standard is described. In particular, how was possible to follow the SISO guidelines for developing a simulation scenario for Autonomous Systems and generating the necessary information exchange requirements to build the C2SIM extension is treated. Then, the methodology suggested by SISO to construct the extension is shown as applied in this specific use case. Finally, this paper deals with the design and development of a modular, scalable and distributed architecture demonstrator to run the designed scenario and test the C2SIM extension to Autonomous Systems. Results of the experimentation performed during R2CD2 project testing, the Coalition Warrior Interoperability eXercise (CWIX) and the MSG–145 mini-exercise are reported. This paper can provide complete guidelines to successfully extend the C2SIM standard, with a particular focus on the use of this new standard to automatize the scenario initialization and the C2 messages between humans in a command post and simulated or real robots.

Nowadays, from the military perspective, development of autonomous weapon systems (AWS) represents opportunity especially for States to enhance their military power and change their status in the system of international relations probably with the presumptions of the new Revolution in Military Affairs (RMA). Not-only the most powerful States such as the U.S., People’s Republic of China, or Russian Federation, but practically all State and non-State actors closely scrutinize this process and its implication for the future security environment. On the other hand, implementation of the AWS technologies poses many challenges (for example independence of AWS on the human control and decision-making processes), which are connected with almost completely new nature of these weapon systems and their influence on the relations across the humankind and its general character. The aim of this paper is to analyse one possible set of such challenges focusing on legal and legitimate (ethical) aspects of the AWS and their compliance with the public international law. Analyses will focus on norms of international humanitarian law and their connections with ethical perspective. This paper will provide necessary understanding of the AWS’ (non)conformity with the international norms based on the international customs and treaties relevant (and binding) for the NATO members State and their efforts to implement such technologies.

After more than 60 years of human activities in outer space, society is highly dependent on space based technologies.A State that aims to enhance its capacities in the sectors of defence, communication, Earth monitoring and emergency management needs to invest in the Space sector. This instigated enormous investments by private and public entities in order to develop Space programmes and deploy satellites in orbit. Satellites are used for both military and civilian purposes and are developed in order to establish a state of security. The military applications of satellites vary from reconnaissance, early warning and telecommunications to meteorology and geodesy.States, recognising these strategic advantages space based assets provide and their importance in the modern theatre of operations, sought the development of technologies capable of neutralising them, in order to deprive the adversary of these advantages. This introduced a new type of weapons, the Anti-Satellite Weapons (ASAT), which can be both hard-kill (kinetic energy weapons, explosions) and soft-kill (jamming, cyber attacks) in nature. The US, Russia, China and now India have developed and tested these weapons which can drastically change the established Space ecosystem.The paper will demonstrate that, the development and use of ASAT creates a new field where NATO and its member States can enhance their operational capabilities in order to safeguard their security and defence. Additionally, given the ultra-hazardous nature of Outer Space, security concepts should extend beyond cyber security to cyber defence and eventually also cyber resilience.

Collaboration (Bruzzone et al. 2013a, b, c, d, e, f) is often mentioned as an opportunity to develop new capabilities for autonomous systems; indeed this paper proposes a practical application where use this approach to enhance the autonomy of the systems during operations in coastal areas or around offshore platforms. The proposed case deals with developing a collaborative approach (Bruzzone et al. 2013a, b, c, d, e, f) among an USV (Unmanned Surface Vehicle) with several AUV (Autonomous Underwater Vehicles) to guarantee persistent surveillance over a marine area (Shkurti et al. 2012). Obviously, the proposed solution could be adopted also for defense and homeland security (Bruzzone et al. 2011a, b, 2010) as well as for archeological site protection in consistence with related cost analysis. The authors propose a technological solution as well as a simulation framework to validate and demonstrate the capabilities of this new approach as well as to quantify expected improvements.