A Framework and Architecture for Multi-Robot Coordination

In this paper, we present a framework and the software architecture for the deployment of multiple autonomous robots in an unstructured and unknown environment with applications ranging from scouting and reconnaissance, to search and rescue and manipulation tasks. Our software framework provides the methodology and the tools that enable robots to exhibit deliberative and reactive behaviors in autonomous operation, to be reprogrammed by a human operator at run-time, and to learn and adapt to unstructured, dynamic environments and new tasks, while providing performance guarantees. We demonstrate the algorithms and software on an experimental testbed that involves a team of car-like robots using a single omnidirectional camera as a sensor without explicit use of odometry

Adversarial Search and Tracking with Multiagent Reinforcement Learning in Sparsely Observable Environment

We study a search and tracking (S&T) problem where a team of dynamic search agents must collaborate to track an adversarial, evasive agent. The heterogeneous search team may only have access to a limited number of past adversary trajectories within a large search space. This problem is challenging for both model-based searching and reinforcement learning (RL) methods since the adversary exhibits reactionary and deceptive evasive behaviors in a large space leading to sparse detections for the search agents. To address this challenge, we propose a novel Multi-Agent RL (MARL) framework that leverages the estimated adversary location from our learnable filtering model. We show that our MARL architecture can outperform all baselines and achieves a 46% increase in detection rate.Comment: Accepted by IEEE International Symposium on Multi-Robot & Multi-Agent Systems (MRS) 202

Mapping multiple gas/odor sources in an uncontrolled indoor environment using a Bayesian occupancy grid mapping based method

Author Posting. © The Author(s), 2011. This is the author's version of the work. It is posted here by permission of Elsevier B.V. for personal use, not for redistribution. The definitive version was published in Robotics and Autonomous Systems 59 (2011): 988–1000, doi:10.1016/j.robot.2011.06.007.In this paper we address the problem of autonomously localizing multiple gas/odor sources in an indoor environment without a strong airflow. To do this, a robot iteratively creates an occupancy grid map. The produced map shows the probability each discrete cell contains a source. Our approach is based on a recent adaptation [15] to traditional Bayesian occupancy grid mapping for chemical source localization problems. The approach is less sensitive, in the considered scenario, to the choice of the algorithm parameters. We present experimental results with a robot in an indoor uncontrolled corridor in the presence of different ejecting sources proving the method is able to build reliable maps quickly (5.5 minutes in a 6 m x 2.1 m area) and in real time