“Unfavorable” environmental effects can also be beneficial: a simulation analysis of centipede-like swarm robots based on implicit control for navigation and exploration in unknown environments

In this study, we propose a centipede-like swarm robotic system capable of utilizing “unfavorable” environmental effects such as robot–environment collisions, robot–robot collisions, signal noise, and signal interval, to aid navigation and exploration in 2D unknown environments. Traditional swarm robots demand complex systems to counteract “unfavorable” environmental effects, making the design of robots to handle unknown environments with theoretical infinite possibilities exceedingly challenging. In contrast, our approach does not view these “unfavorable” environmental effects as foes to be combated, but instead employs strategies like implicit control to convert them into beneficial influences on the task. This simplifies the swarm robot system, enabling navigation and exploration with only goal direction as the single input, eliminating the need for inter-robot observation, inter-robot communication and obstacle detection. Finally, we validate our system’s effectiveness through simulations in the CoppeliaSim environment, demonstrating the benefits of “unfavorable” environmental effects and the method’s universal applicability.