Behavior-Based Coordination in Multi-Robot Systems

Abstract

The successful deployment of a multi-robot system (MRS) requires an effective method of coordination to mediate the interactions among the robots and between the robots and the task environment in order for a given system-level task to be performed. The design of coordination mechanisms has received increasing attention in recent years and has included investigations into a wide variety of coordination mechanisms. A popular and successful framework for the control of robots in coordinated MRS is behavior-based control (1,2). Behavior-based control is a methodology in which robots are controlled through the principled integration of a set of interacting behaviors (e.g., wall following, collision avoidance, landmark recognition, etc.) in order to achieve desired system-level behavior. This chapter will describe, through explanation, discussion of demonstrated simulated and physical mobile robots, and formal design and analysis, the range and capabilities of behavior-based control applied to multi-robot coordination.

Distributed Colony-Level Algorithm Switching for Robot Swarm Foraging

“…Matari¢ and Arkin have worked extensively in behavior-based robotics [2,4]. It is common for individual robots to switch between the behaviors of food collecting, obstacle avoidance, and resting, for example.…”

Effect of sensor and actuator quality on robot swarm algorithm performance

“…The swarm algorithms in the literature tend to be composed of a few common primitive behaviors, such as trail following, expansion, and shape formation [5], [11], [3]. A foraging algorithm, for example, could combine expansion (to find the target) with trail following (to return it to the nest).…”