Bioinspired Environment Exploration Algorithm in Swarm Based on Lévy Flight and Improved Artificial Potential Field

Abstract

Inspired by the behaviour of animal populations in nature, we propose a novel exploration algorithm based on Lévy flight (LF) and artificial potential field (APF). The agent is extended to the swarm level using the APF method through the LF search environment. Virtual leaders generate moving steps to explore the environment through the LF mechanism. To achieve collision-free movement in an unknown constrained environment, a swarm-following mechanism is established, which requires the agents to follow the virtual leader to carry out the LF. The proposed method, combining the advantages of LF and APF which achieve the effect of flocking in an exploration environment, does not rely on complex sensors for environment labelling, memorising, or huge computing power. Agents simply perform elegant and efficient search behaviours as natural creatures adapt to the environment and change formations. The method is especially suitable for the camouflaged flocking exploration environment of bionic robots such as flapping drones. Simulation experiments and real-world experiments on E-puck2 robots were conducted to evaluate the effectiveness of the proposed LF-APF algorithm.