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.

An important issue that arises in the automation of many security, surveillance, and reconnaissance tasks is that of observing (or monitoring) 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 article, we investigate the use of a cooperative team of autonomous sensor-based robots for the observation of multiple moving targets (a problem that we term CMOMMT). We focus primarily on developing the distributed control strategies that allow the robot team to attempt to maximize the collective time during which each target is being observed by at least one robot team member in the area of interest. Our initial efforts on this problem address the aspects of distributed control in robot teams with e...

This paper describes a control mechanism by which large numbers of inexpensive robots can be deployed as a distributed remote sensing instrument, and in which the desired large-scale properties of the sensing instrument emerge from the simple pair-wise interactions of its component robots. Such sensing instruments are called distributed robotic macrosensors. Robots in the macrosensor interact with their immediate neighbors using a virtual spring mesh abstraction, which is governed by a simple physics model. By carefully defining the nature of the spring mesh and the associated physics model, it is possible to create a number of desirable global behaviors without any global control or configuration. Properties of the resulting macrosensor include arbitrary scalability, the ability to function in complex environments, sophisticated target tracking ability, and natural fault tolerance. We describe the control mechanisms that yield these results, and the simulation results that demonstrate their efficacy. 1.

We have developed a novel control mechanism that deploys a large number of inexpensive robots as a distributed remote sensing array, called a Distributed Robotic Macrosensor (DRM). This DRM has the capability to track targets of both a discrete (e.g., a vehicle) and diffuse (e.g., a chemical plume) nature. Attack resistance is an inherant property of the DRM as well. A simple virtual spring mesh abstraction is used to provide fully distributed control that is both flexible and faulttolerant. We describe the algorithms for spring mesh formation and control, discrete target tracking, and diffuse target tracking. We also present simulation results demonstrating the efficacy and robustness of the spring mesh approach.

Abstract — This paper describes a method for observing maneuvering targets using a group of mobile robots equipped with video cameras. These robots are part of a team of small-size (7x7x7 cm) robots configured from modular components that collaborate to accomplish a given task. The cameras seek to observe the target while facing it as much as possible from their respective viewpoints. This work considers the problem of scheduling and maneuvering the cameras based on the evaluation of their current positions in terms of how well can they maintain a frontal view of the target. We describe our approach, which distributes the task among several robots and avoids extensive energy consumption on a single robot. We explore the concept in simulation and present results. Keywords-sensor placement; cooperative sensors; distributed vision; automatic surveillance. I.

Abstract: A new method in robot path generation is presented using an analysis of the characteristics of multi-robot collision avoidance. The research is based on the concept of the collision map, where the collision between two robots is presented by a collision region and a crossing curve TLVSTC (traveled length versus servo time curve). Analytic collision avoidance is considered by translating the collision region in the collision map. The 4 different translations of collision regions correspond to the 4 parallel movements of the actual original robot path in the real world. This analysis is applied to path modifications where the analysis of collision characteristics is crucial and the resultant path for collision avoidance is generated. Also, the correlations between the translations of the collision region and robot paths are clarified by analyzing the collision/non-collision areas. The influence of the changes of robot velocity is investigated analytically in view of collision avoidance as an example.

Summary. Distributed play-based approaches have been proposed as an effective means of switching strategies during the course of timed, zero-sum games, such as robot soccer. In this paper, we empirically show that different plays have a significant effect on opponent performance in real robot soccer games. We also consider the problem of distributed play recognition: classifying the strategy being played by the opponent team. Play recognition in real robot soccer is a particularly challenging problem because our observations are only “incidental ” — that is, the primary task of our team is to play soccer, not to explicitly observe members of the other team. Despite these challenges, we achieve high classification accuracy in the robot soccer domain. 1

Market-based mechanisms can be used to coordinate self-interested multi-robot systems in fully distributed environments, where by self-interested we mean that each robot agent attempts to maximize a payoff function which accounts for both the resources consumed and the contribution made by the robot. In previous work, we have studied the effect of various market rules and bidding strategies on the global performance of the multi-robot system. However, rather than use a central monitoring and enforcement mechanisms, we rely on agents to self-report their actions. This assumes that the agents act honestly. In this paper, we drop the honesty assumption, raising the possibility that agents may exaggerate their contribution in order to increase their payoff. To address the problem of such malicious behavior, we propose an audit mechanism to maintain the integrity of reported payoffs. Our algorithm extends previous work on preventing free-riding in peer-to-peer networks. Specifically, we consider locality and mobility in multi-robot systems. We show that our approach efficiently detects malicious behaviors with a high probability. 1