Tuning swarm behavior for environmental sensing tasks represented as coverage problems

In recent years, increases in industrial residue have become a significant environmental threat. These residues can cause problems for natural ecosystems and their inhabitants, including animals and humans. Environmental monitoring through sensing is one approach to predict or detect the presence of pollution from such residue. One of the emerging tools to do distributed sensing, and thus a novel approach to environmental sensing, is to use swarms of robots to carry sensors. Swarming robots are programmed to move like ants, birds, or other swarming or flocking organisms to distribute through and explore an environment. Swarm robots have advantages over other multiagent or single-agent systems because of their number and decentralized control strategies, which means they can carry multiple sensors and explore wide areas in a fault tolerant manner. However, there are challenges that remain before swarm robots can be applied productively in this scenario. This chapter addresses two of these challenges: (1) how swarming behavior can be achieved quickly on a given set of robots and (2) how the swarm can conduct environmental sensing. We present a novel system design that combines two algorithms: the first is a novel algorithm for autonomous tuning of swarm behavior and the second for conducting an environmental sensing task, represented as an area coverage problem. We show that the proposed system can tune the behavior of swarms, suitable for completing a coverage task more effectively than an untuned group of robots. We demonstrate the system in both point mass simulator and in simulated robots.