Dynamic Area Coverage using Faulty Multi-Agent Swarms

We consider the problem of distributed coverage of an unknown two-dimensional environment using a swarm of mobile mini-robots. In contrast to previous approaches for robotic area coverage, we assume that each robot (agent) in our system is limited in its communication range and memory capacity. Agents are also susceptible to sensor noise while communicating with other agents, and, can be subject to transient or permanent failures. The objective of the agents is to cover the entire environment while reducing the coverage time and the redundancy in the area covered by different agents. First, we describe our distributed coverage algorithm where each agent uses a local heuristic based on Manhattan distances and the information gained from other agents at each step to decide on its next action (movement). We then describe and analyze the fault model of our agents and show that the local heuristic used by the agents deteriorate linearly as the communication noise increases. Finally, we verify the performance of our system empirically within a simulated environment and show that the system is able to scale efficiently in the number of robots and size of the environment, and, determine the effect of communication faults and robot failures on the system performance.