Abstract: In this paper, we present a framework and the software architecture forthe deployment of multiple autonomous robots in an unstructured and unknownenvironment with applications ranging from scouting and reconnaissance, tosearch and rescue and manipulation tasks. Our software framework provides themethodology and the tools that enable robots to exhibit deliberative and reactivebehaviors in autonomous operation, to be reprogrammed by a human operator atrun-time, and to learn and adapt to unstructured, dynamic environments and newtasks, while providing performance guarantees. We demonstrate the algorithmsand software on an experimental testbed that involves a team of car-like robotsusing a single omnidirectional camera as a sensor without explicit use ofodometry. 1. Introduction It has long been recognized that there are several tasks that can be performed moreefficiently and robustly using multiple robots [1]-[4]. In fact, there is extensiveliterature on robot control and the coordination of multiple robots. Our goal, in thispaper, is to describe a set of software tools that allows the development ofcontrollers and estimators for multirobot coordination. The tools consist of aframework for developing software components, architecture for control andestimation modules, and a set of decentralized control, planning and sensingalgorithms.Our software framework divides the overall multi-robot control task into a set ofmodes or behaviors, which may be executed either sequentially or in parallel. Modescan consist of high-level behaviors such as planning a path to a goal position, as wellas low-level tasks such as obstacle avoidance. We use a high-level language toformally describe how and when transitions between these modes are to take placein order to achieve a set of global objectives. Finally, because it is difficult to predictexactly under what conditions switching between modes should occur, weparameterize mode boundary transitions within each robot’s information space anduse reinforcement reward to obtain locally optimal mode boundary locations. Thusthe multirobot system can learn to continually improve overall performance throughinteraction with the environment, without human intervention.
['Rajeev Alur', 'Aveek K. Das', 'Joel M. Esposito', 'Rafael Fierro', 'Gregory Z. Grudic', 'Yerang Hur', 'Vijay Kumar', 'Insup Lee', 'Jaeho Lee', 'James P. Ostrowski', 'George J. Pappas', 'Ben Southall', 'John R. Spletzer', 'Camillo J. Taylor']
A Framework and Architecture for Multirobot Coordination