Formation Rotation and Assignment: Avoiding Obstacles in Multi-Robot Scenarios

Abstract

Current formation assignment and optimization methods frequently overlook the influence of rotational dynamics, limiting their operational flexibility. Additionally, these methods typically neglect the impact of obstacles, which may also hinder their effectiveness in obstacle-rich environments. To address these limitations, this letter proposes a novel approach that incorporates both rotation and assignment into the formation optimization of multi-robot systems. This approach allows for dynamic adjustment of the formation orientation and introduces a collaborative obstacle avoidance strategy. This strategy is specifically designed to assess and integrate the influence of obstacles into the optimization process, thereby enhancing the ability to maneuver around obstacles. Simulation experiments, including scenarios involving the encirclement of stationary and moving targets, validate the effectiveness of the proposed algorithm. The proposed algorithm outperforms non-rotational methods in maintaining formations under the influence of various types of obstacles while encircling targets. Furthermore, real-world flight experiments demonstrate the robustness and feasibility of the algorithm.