Multi-Agent Task Allocation for Robot Soccer

This is the published version. Copyright De GruyterThis paper models and analyzes task allocation methodologies for multiagent systems. The evaluation process was implemented as a collection of simulated soccer matches. A soccer-simulation software package was used as the test-bed as it provided the necessary features for implementing and testing the methodologies. The methodologies were tested through competitions with a number of available soccer strategies. Soccer game scores, communication, robustness, fault-tolerance, and replanning capabilities were the parameters used as the evaluation criteria for the mul1i-agent systems

Optimization of swarm robotic constellation communication for object detection and event recognition

Swarm robotics research describes the study of how a group of relatively simple physically embodied agents can, through their interaction collectively accomplish tasks which are far beyond the capabilities of a single agent. This self organizing but decentralized form of intelligence requires that all members are autonomous and act upon their available information. From this information they are able to decide their behavior and take the appropriate action. A global behavior can then be witnessed that is derived from the local behaviors of each agent. The presented research introduces the novel method for optimizing the communication and the processing of communicated data for the purpose of detecting large scale meta object or event, denoted as meta event, which are unquantifiable through a single robotic agent. The ability of a swarm of robotic agents to cover a relatively large physical environment and their ability to detect changes or anomalies within the environment is especially advantageous for the detection of objects and the recognition of events such as oil spills, hurricanes, and large scale security monitoring. In contrast a single robot, even with much greater capabilities, could not explore or cover multiple areas of the same environment simultaneously. Many previous swarm behaviors have been developed focusing on the rules governing the local agent to agent behaviors of separation, alignment, and cohesion. By effectively optimizing these simple behaviors in coordination, through cooperative and competitive actions based on a chosen local behavior, it is possible to achieve an optimized global emergent behavior of locating a meta object or event. From the local to global relationship an optimized control algorithm was developed following the basic rules of swarm behavior for the purpose of meta event detection and recognition. Results of this optimized control algorithm are presented and compared with other work in the field of swarm robotics

Regional target surveillance with cooperative robots using APFs

Target surveillance in a bounded environment has been a growing focus in the past few years, particularly with recent world events prompting the need for environmental monitoring using automated surveillance. Scenarios exist where the goal is to be able to track targets within a certain distance and yet maintain a proper distribution of the surveillance units to provide field coverage. Previous works in this area using mobile robots as the surveillance units have made assumptions of a global awareness capability provided by a central controller. Artificial Potential Fields (APFs) have been used in cooperative robots and swarm research for applications such as threat containment and related formation control without as much focus on the surveillance tasks. This thesis aims to extend the use of APFs to the concept of Regional Target Surveillance in a distributed algorithm among cooperative robots, with the utilization of Voronoi cells to aid in coverage control. This investigation proposes a system to utilize only the necessary number of robots with local awareness capability. Each of these robots integrates the use of a centroid force and a target force to provide a balanced coverage and target tracking performance. This is accomplished by implicitly defining three circular regions of responsibility for each robot, namely, the full sensing region, the target tracking region, and the centroid calculation region. The target tracking region is within the full sensing region and encompasses the centroid calculation region. The centroid calculation region is used to define the Voronoi cells and thus the centroid of the responsible field of each robot. By adjusting the relative size of the three regions, the system accomplishes implicit target handoff between robots, and, in turn, provides an overall balance between regional target tracking and environmental coverage for the surveillance goal. Matlab simulation results show that with a proper balance in the tradeoff between the tracking and coverage performance, the algorithm is scalable to larger field sizes with a similar robot density, while successfully accomplishing the surveillance tasks

Neural Networks and Q-Learning for Robotics

International audienceIntroductionBehavior-Based ApproachSupervised Learning of a BehaviorMiniature Mobile Robot KheperaIllustration: Reward-Penalty LearningReinforcement LearningGenetic AlgorithmsLearning Classifier SystemsGA & ANNQ-learningEvaluation FunctionAlgorithmReinforcement FunctionUpdate FunctionConvergenceLimitationsGeneralizationNeural Implementations of the Q-learningMultilayer Perceptron Implementation (ideal & Q-CON)Q-KOHONComparisonKnowledge IncorporationReinforcement Function DesignBuilding of a non-explicit ModelLearning in Cooperative RoboticsReference

A General Framework for Multi-Agent Task Selection

Ph.DDOCTOR OF PHILOSOPH

Multi-robot cooperative surveillance in unknown environments

Ph.DDOCTOR OF PHILOSOPH

Task Allocation Strategies in Multi-Robot Environment

Multirobot systems (MRS) hold the promise of improved performance and increased fault tolerance for large-scale problems. A robot team can accomplish a given task more quickly than a single agent by executing them concurrently. A team can also make effective use of specialists designed for a single purpose rather than requiring that a single robot be a generalist. Multirobot coordination, however, is a complex problem. An empirical study is described in the thesis that sought general guidelines for task allocation strategies. Different strategies are identified, and demonstrated in the multi-robot environment.Robot selection is one of the critical issues in the design of robotic workcells. Robot selection for an application is generally done based on experience, intuition and at most using the kinematic considerations like workspace, manipulability, etc. This problem has become more difficult in recent years due to increasing complexity, available features, and facilities offered by different robotic products. A systematic procedure is developed for selection of robot manipulators based on their different pertinent attributes. The robot selection procedure allows rapid convergence from a very large number of candidate robots to a manageable shortlist of potentially suitable robots. Subsequently, the selection procedure proceeds to rank the alternatives in the shortlist by employing different attributes based specification methods. This is an attempt to create exhaustive procedure by identifying maximum possible number of attributes for robot manipulators.Availability of large number of robot configurations has made the robot workcell designers think over the issue of selecting the most suitable one for a given set of operations. The process of selection of the appropriate kind of robot must consider the various attributes of the robot manipulator in conjunction with the requirement of the various operations for accomplishing the task. The present work is an attempt to develop a systematic procedure for selection of robot based on an integrated model encompassing the manipulator attributes and manipulator requirements

Cooperative Motion Control for Multi-Target Observation

many security, surveillance, and reconnaissance tasks is that of monitoring (or observing) the movements of targets navigating in a bounded area of interest. A key research issue in these problems is that of sensor placement --- determining where sensors should be located to maintain the targets in view. In complex applications involving limited-range sensors, the use of multiple sensors dynamically moving over time is required. In this paper, we investigate the use of a cooperative team of autonomous sensor-based robots for the observation of multiple moving targets. We focus primarily on developing the distributed control strategies that allow the robot team to attempt to minimize the total time in which targets escape observation by some robot team member in the area of interest. This paper first formalizes the problem and discusses related work. We then present a distributed approximate approach to solving this problem that combines low-level multi-robot control with higher-level reasoning control based on the ALLIANCE formalism. We analyze the effectiveness of our approach by comparing it to three other feasible algorithms for cooperative control, showing the superiority of our approach for a large class of problems