Cooperative multi-robot observation of multiple moving targets

An important issue that arises in the automation of 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 of this type, the use of multiple sensors dynamically moving over time is required. In this paper, the authors investigate the sue of a cooperative team of autonomous sensor-based robots for multi-robot observation of multiple moving targets. They focus primarily on developing the distributed control strategies that allow the robot team to attempt to maximize the collective tie during which each object is being observed by at least one robot in the area of interest. The initial efforts in this problem address the aspects of distributed control in homogeneous robot teams with equivalent sensing and movement capabilities working in an uncluttered, bounded area. This paper first formalizes the problem, discusses related work, and then shows that this problem is NP-hard. They then present a distributed approximate approach to solving this problem that combines low-level multi-robot control with higher-level control

Online Multi-object k-coverage with Mobile Smart Cameras

In this paper, we combine k-coverage with the Cooperative Multi- robot Observation of Multiple Moving Targets problem, de ning the new problem of online multi-object k -coverage. We demonstrate the bene ts of mobility in tackling this and propose a decentralised multi-camera coordination that improves this further. We show that coordination exploiting shared visual features is more e ective than coordination based on Euclidean distance. When coordinating k-coverage in a distributed way, our results suggest that the design of coordination mechanisms should shi towards decisions being made by potential responders with up-to-date knowledge of their own state, rather than a coordinating camera

Distributed Lazy Q-learning for Cooperative Mobile Robots

International audienceCompared to single robot learning, cooperative learning adds the challenge of a much larger search space (combined individual search spaces), awareness of other team members, and also the synthesis of the individual behaviors with respect to the task given to the group. Over the years, reinforcement learning has emerged as the main learning approach in autonomous robotics, and lazy learning has become the leading bias, allowing the reduction of the time required by an experiment to the time needed to test the learned behavior performance. These two approaches have been combined together in what is now called lazy Q-learning, a very efficient single robot learning paradigm. We propose a derivation of this learning to team of robots : the «pessimistic» algorithm able to compute for each team member a lower bound of the utility of executing an action in a given situation. We use the cooperative multi-robot observation of multiple moving targets (CMOMMT) application as an illustrative example, and study the efficiency of the Pessimistic Algorithm in its task of inducing learning of cooperation

Robot Awareness in Cooperative Mobile Robot Learning

International audienceMost of the straight-forward learning approaches in cooperative robotics imply for each learning robot a state space growth exponential in the number of team members. To remedy the exponentially large state space, we propose to investigate a less demanding cooperation mechanism—i.e., various levels of awareness—instead of communication. We define awareness as the perception of other robots locations and actions. We recognize four different levels (or degrees) of awareness which imply different amounts of additional information and therefore have different impacts on the search space size (Θ(0), Θ(1), Θ(N), o(N),1 where N is the number of robots in the team). There are trivial arguments in favor of avoiding binding the increase of the search space size to the number of team members. We advocate that, by studying the maximum number of neighbor robots in the application context, it is possible to tune the parameters associated with a Θ(1) increase of the search space size and allow good learning performance. We use the cooperative multi-robot observation of multiple moving targets (CMOMMT) application to illustrate our method. We verify that awareness allows cooperation, that cooperation shows better performance than a purely collective behavior and that learned cooperation shows better results than learned collective behavior

Multi-robot motion control for cooperative observation

An important issue that arises in the automation of 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, the authors investigate the use of a cooperative team of autonomous sensor-based robots for the observation of multiple moving targets. They 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. The authors 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. They analyze the effectiveness of the approach by comparing it to 3 other feasible algorithms for cooperative control, showing the superiority of the approach for a large class of problems

Distributed autonomy and trade-offs in online multiobject k-coverage

In this article, we explore the online multiobject k-coverage problem in visual sensor networks. This problem combines k-coverage and the cooperative multirobot observation of multiple moving targets problem, and thereby captures key features of rapidly deployed camera networks, including redundancy and team-based tracking of evasive or unpredictable targets. The benefits of using mobile cameras are demonstrated and we explore the balance of autonomy between cameras generating new subgoals, and those responders able to fulfill them. We show that higher performance against global goals is achieved when decisions are delegated to potential responders who treat subgoals as optional, rather than as obligations that override existing goals without question. This is because responders have up-to-date knowledge of their own state and progress toward goals where they are situated, which is typically old or incomplete at locations remote from them. Examining the extent to which approaches overprovision or underprovision coverage, we find that being well suited for achieving 1-coverage does not imply good performance at k-coverage. Depending on the structure of the environment, the problems of 1-coverage and k-coverage are not necessarily aligned and that there is often a trade-off to be made between standard coverage maximization and achieving k-coverage

Data-driven Metareasoning for Collaborative Autonomous Systems

When coordinating their actions to accomplish a mission, the agents in a multi-agent system may use a collaboration algorithm to determine which agent performs which task. This paper describes a novel data-driven metareasoning approach that generates a metareasoning policy that the agents can use whenever they must collaborate to assign tasks. This metareasoning approach collects data about the performance of the algorithms at many decision points and uses this data to train a set of surrogate models that can estimate the expected performance of different algorithms. This yields a metareasoning policy that, based on the current state of the system, estimated the algorithms’ expected performance and chose the best one. For a ship protection scenario, computational results show that one version of the metareasoning policy performed as well as the best component algorithm but required less computational effort. The proposed data-driven metareasoning approach could be a promising tool for developing policies to control multi-agent autonomous systems.This work was supported in part by the U.S. Naval Air Warfare Center-Aircraft Division