LF-ACO: an effective formation path planning for multi-mobile robot

Multi-robot path planning is a hot problem in the field of robotics. Compared with single-robot path planning, complex problems such as obstacle avoidance and mutual collaboration need to be considered. This paper proposes an efficient leader follower-ant colony optimization (LF-ACO) to solve the collaborative path planning problem. Firstly, a new Multi-factor heuristic functor is proposed, the distance factor heuristic function and the smoothing factor heuristic function. This improves the convergence speed of the algorithm and enhances the smoothness of the initial path. The leader-follower structure is reconstructed for the position constraint problem of multi-robots in a grid environment. Then, the pheromone of the leader ant and the follower ants are used in the pheromone update rule of the ACO to improve the search quality of the formation path. To improve the global search capability, a max-min ant strategy is used. Finally, the path is optimized by the turning point optimization algorithm and dynamic cut-point method to improve path quality further. The simulation and experimental results based on MATLAB and ROS show that the proposed method can successfully solve the path planning and formation problem.

Automated summary

This paper proposes a leader follower-ant colony optimization (LF-ACO) algorithm to solve the collaborative path planning problem in multi-robot systems. The algorithm incorporates new heuristic functions, leader-follower structure, and path optimization techniques to improve performance.