Asymmetric Robot Motion Design for Pursuit-Evasion Games

Symmetric turning control is the typical design choice for most machines. However, historical examples of asymmetric machine design, as well as examples of asymmetry in nature, suggest that asymmetric turning may be a potential advantage in adversarial applications. For instance, aircraft of World Wars I and II were plagued by asymmetric turning controls as a result of gyroscopic forces from the rotating engine. Pilots of the time actually believed this to be a feature, not a bug, suggesting that the asymmetric turning improved strategic evasion and pursuit during battle. As autonomous robots become increasingly critical in military operations, it is imperative that we endow them with strategic designs for better performance. We seek to understand if asymmetric turning is an advantageous design. Using Karaman and Frazzoli's sample-based algorithm for pursuit-evasion games, software simulates robot motion planning in an asymmetric Dubins state space to observe how asymmetric turning influences agent success. We demonstrate mathematically that the Dubins interval path solutions are applicable to asymmetric Dubins vehicles, as both are utilized within the simulation. The Open Motion Planning Library (OMPL) is leveraged to implement the pursuit-evasion game algorithm. To simulate asymmetric action, agents are assigned varying degrees of asymmetric turning constraints, such that as one turn sharpens, the other broadens. Agents then compete in a pursuit-evasion game. Pursuit-evasion games are simulated across a range of asymmetric turning match-ups and agent starting positions. Results show that pursuer success increases as its asymmetry increases. Evader success remains constant, regardless of asymmetric turning influence. Furthermore, the advantages of asymmetric turning can be further augmented when considered in conjunction with relative agent starting position. The results of this research inform more intelligent machine design strategies for vehicles in dynamic spaces

Dagstuhl Reports : Volume 1, Issue 2, February 2011

Online Privacy: Towards Informational Self-Determination on the Internet (Dagstuhl Perspectives Workshop 11061) : Simone Fischer-Hübner, Chris Hoofnagle, Kai Rannenberg, Michael Waidner, Ioannis Krontiris and Michael Marhöfer Self-Repairing Programs (Dagstuhl Seminar 11062) : Mauro Pezzé, Martin C. Rinard, Westley Weimer and Andreas Zeller Theory and Applications of Graph Searching Problems (Dagstuhl Seminar 11071) : Fedor V. Fomin, Pierre Fraigniaud, Stephan Kreutzer and Dimitrios M. Thilikos Combinatorial and Algorithmic Aspects of Sequence Processing (Dagstuhl Seminar 11081) : Maxime Crochemore, Lila Kari, Mehryar Mohri and Dirk Nowotka Packing and Scheduling Algorithms for Information and Communication Services (Dagstuhl Seminar 11091) Klaus Jansen, Claire Mathieu, Hadas Shachnai and Neal E. Youn

Multi-Agent Pursuit-Evasion Game Based on Organizational Architecture

Multi-agent coordination mechanisms are frequently used in pursuit-evasion games with the aim of enabling the coalitions of the pursuers and unifying their individual skills to deal with the complex tasks encountered. In this paper, we propose a coalition formation algorithm based on organizational principles and applied to the pursuit-evasion problem. In order to allow the alliances of the pursuers in different pursuit groups, we have used the concepts forming an organizational modeling framework known as YAMAM (Yet Another Multi Agent Model). Specifically, we have used the concepts Agent, Role, Task, and Skill, proposed in this model to develop a coalition formation algorithm to allow the optimal task sharing. To control the pursuers\u27 path planning in the environment as well as their internal development during the pursuit, we have used a Reinforcement learning method (Q-learning). Computer simulations reflect the impact of the proposed techniques

Mobile robotic network deployment for intruder detection and tracking

This thesis investigates the problem of intruder detection and tracking using mobile robotic networks. In the first part of the thesis, we consider the problem of seeking an electromagnetic source using a team of robots that measure the local intensity of the emitted signal. We propose a planner for a team of robots based on Particle Swarm Optimization (PSO) which is a population based stochastic optimization technique. An equivalence is established between particles generated in the traditional PSO technique, and the mobile agents in the swarm. Since the positions of the robots are updated using the PSO algorithm, modifications are required to implement the PSO algorithm on real robots to incorporate collision avoidance strategies. The modifications necessary to implement PSO on mobile robots, and strategies to adapt to real environments are presented in this thesis. Our results are also validated on an experimental testbed. In the second part, we present a game theoretic framework for visibility-based target tracking in multi-robot teams. A team of observers (pursuers) and a team of targets (evaders) are present in an environment with obstacles. The objective of the team of observers is to track the team of targets for the maximum possible time. While the objective of the team of targets is to escape (break line-of-sight) in the minimum time. We decompose the problem into two layers. At the upper level, each pursuer is allocated to an evader through a minimum cost allocation strategy based on the risk of each evader, thereby, decomposing the agents into multiple single pursuer-single evader pairs. Two decentralized allocation strategies are proposed and implemented in this thesis. At the lower level, each pursuer computes its strategy based on the results of the single pursuer-single evader target-tracking problem. We initially address this problem in an environment containing a semi-infinite obstacle with one corner. The pursuer\u27s optimal tracking strategy is obtained regardless of the evader\u27s strategy using techniques from optimal control theory and differential games. Next, we extend the result to an environment containing multiple polygonal obstacles. We construct a pursuit field to provide a guiding vector for the pursuer which is a weighted sum of several component vectors. The performance of different combinations of component vectors is investigated. Finally, we extend our work to address the case when the obstacles are not polygonal, and the observers have constraints in motion

Coordinated Exploration of unknown labyrinthine environments applied to the Pusruite-Evasion problem

International audienceThis paper introduces a multi-robot cooperation approach to solve the "pursuit evasion'' problem for mobile robots that have omni-directional vision sensors in unknown environments. The main characteristic of this approach is based on the robots cooperation by sharing knowledge and making them work as a team: a complete algorithm for computing robots motion strategy is presented as well as the deliberation protocol which distributes the exploration task among the team and takes the best possible outcome from the robots resources

Art and Engineering Inspired by Swarm Robotics

Swarm robotics has the potential to combine the power of the hive with the sensibility of the individual to solve non-traditional problems in mechanical, industrial, and architectural engineering and to develop exquisite art beyond the ken of most contemporary painters, sculptors, and architects. The goal of this thesis is to apply swarm robotics to the sublime and the quotidian to achieve this synergy between art and engineering. The potential applications of collective behaviors, manipulation, and self-assembly are quite extensive. We will concentrate our research on three topics: fractals, stability analysis, and building an enhanced multi-robot simulator. Self-assembly of swarm robots into fractal shapes can be used both for artistic purposes (fractal sculptures) and in engineering applications (fractal antennas). Stability analysis studies whether distributed swarm algorithms are stable and robust either to sensing or to numerical errors, and tries to provide solutions to avoid unstable robot configurations. Our enhanced multi-robot simulator supports this research by providing real-time simulations with customized parameters, and can become as well a platform for educating a new generation of artists and engineers. The goal of this thesis is to use techniques inspired by swarm robotics to develop a computational framework accessible to and suitable for both artists and engineers. The scope we have in mind for art and engineering is unlimited. Modern museums, stadium roofs, dams, solar power plants, radio telescopes, star networks, fractal sculptures, fractal antennas, fractal floral arrangements, smooth metallic railroad tracks, temporary utilitarian enclosures, permanent modern architectural designs, guard structures, op art, and communication networks can all be built from the bodies of the swarm