Swarm robots using Lévy walk with concession in targets exploration

Abstract

In the swarm robotics community, Lévy walk has been recognized as one of the most efficient search strategies for the environment, with sparse targets that robots have no prior knowledge of. Generally, Lévy walk is generated by following the Lévy distribution. Our previous results also confirmed that the Lévy walk outperformed the usual random walk for exploration strategy in real swarm robot experiments. On the other hand, it has been reported in several papers that each individual in swarm robots does not follow Lévy distribution due to collision avoidance from other robots, resulting in inefficient search. Therefore, we introduced concessions to the swarm robots to improve search efficiency. This paper investigated the performance of the Lévy walk with concession. Robots concede other robots when they receive the signal that other robots execute longer walks. We conducted a series of computer simulations varying ranges detecting other robots’ walk distance signals, the number of robots, the number of targets, and the distribution of targets. The results suggest that the search efficiency of Lévy walk was improved by concession. Furthermore, we confirmed that improving search efficiency saturates beyond the threshold of range detecting other robots’ walk distance signals.