Adaptive Shape Formation Against Swarm-Scale Variants in Robot Swarms

Shape formation of robot swarms is quite challenging when the robot number varies, as the number often needs to match the number of goal locations in the shape. For this challenge, state-of-the-art methods characterize the shape as a continuous region and distribute robots in the region. However, such methods can only handle a small swarm-scale variant due to the fixed shape size. In this letter, we propose a distributed adaptive shape formation method with variable shape size. The core idea is that each robot can dynamically adjust the shape size according to variants of local density, induced by variants of the robot number. Furthermore, this individual adjustment by each robot can be propagated through peer-to-peer communications. In particular, this strategy is integrated into our previous work, which employs the mean-shift algorithm to achieve the shape formation of large-scale robot swarms. In addition, we also adapt the mean-shift algorithm for redesigning all the negotiation and control components in the previous work such that a concise and unified alternative for shape formation can be provided via the mean-shift algorithm. Finally, simulation and experiment results demonstrate that the proposed method can achieve the desired shape with the robot number decreasing or increasing.