Multi-robot consistent formation control based on novel leader-follower model and optimization motion planning approach - Yao, Liguo; Yuan, Xiaoyang; Li, Guanghui; Lu, Yao; Zhang, Taihua (Knowledge-based systems, 2025)
Path planning and formation control are of great significance for improving the efficiency of robot collaboration. In practical applications, traditional algorithms are complex due to the complexity and immediacy of real scenes. Optimization algorithms with their problem independence and easy scalability, can effectively solve the difficulties encountered in multi-robot path planning and formation control. However, current optimization algorithms generally have problems such as being easily trapped in local optimality and incomplete algorithm exploration. To better solve the problems of significant path trajectory error and poor formation adaptability in the collaborative formation of multi-robot systems. Based on Crayfish Optimization Algorithm (COA), this paper introduces three strategies that effectively improve the optimization effect of the COA, and proposes a hybrid algorithm suitable for collaborative formation of multi-robot systems (SCCOA). We design a novel hierarchical leader-follower formation framework and prove the stability of the formation control model. The proposed SCCOA was combined with the framework to use to adjust and parallel control the trajectory, speed, and other related factors of the leader and follower to realize the formation control system to achieve the goal of multi-robot collaborative formation. To verify the effectiveness of the SCCOA in path planning and formation control systems, SCCOA was compared with 11 excellent algorithms in experiments, and various evaluation indicators were established by analyzing various coefficients in multi-robot path planning and formation control. Results showed that SCCOA ranked first in all metrics, demonstrating the excellent performance of the SCCOA in solving multi-robot path planning and formation control problems.