An Artificial Plant Community Algorithm for Collision-Free Multi-Robot Aggregation - Zhengying Cai; Qingqing Yu; Zhuimeng Lu; Zeya Liu; Guoqiang Gong (Applied Sciences)
Multi-robot aggregation is an important application for emergent robotic tasks, in which multiple robots are aggregated to work collaboratively. In this context, the collision-free problem poses a significant challenge, which is complicated to resolve, as aggregated robots are prone to collision. This study attempts to use robotic edge intelligence technology to solve this problem. First, a multiple objective function is built for the collision-free multi-robot aggregation problem, considering the characteristics of robotic aggregation and collision-free constraints. Second, a heuristic artificial plant community algorithm is proposed to obtain an optimal solution to the developed problem model, which is lightweight and can be deployed on edge robots to search for the optimal route in real-time. The proposed algorithm utilizes the swarm learning capability of edge robots to produce a set of collision-free aggregation assignments for all robots. Finally, a benchmark test set is developed, based on which a series of benchmark tests are conducted. The experimental results demonstrate that the proposed method is effective and its computational performance is suitable for robot edge computing.