A Novel Integrated Path Planning Algorithm for Warehouse AGVs

Abstract

We propose an integrated path planning method for multiple automated guided vehicles performing logistics delivery within a real‐world warehouse environment considering obstacles. By applying it on each vehicle, this proposed method enables the vehicle the vehicles have the capabilities for autonomous path planning. The path planning consists of three parts, K‐means algorithm based task points clustering, genetic algorithm based task points ordering, and the probabilistic road map based best path search. Vehicle conflict resolution is depending on implementing the probabilistic road map construction considering the realistic map with obstacles. The simulations result validate that the clustering and ordering are necessary for the path planning, both the path planning time and the Automated guided vehicles (AGVs) running time can be dramatically reduced.

Analysis of Indoor Path Planning Techniques for Wheeled Mobile Robots

“…The floor surface in indoor environments is usually flatter, making it more suitable for wheeled robots. Indoor applications for wheeled robots are diverse and can include automated guided vehicle (AGV) carts in busy logistics and warehousing environments [5,6], autonomous forklifts [7], as well as service robots in hotel lobbies for welcoming guests or in restaurants for serving meals [8]. Wheeled robots also have applications in the medical and healthcare field, such as transporting patients in wheelchairs to different examination rooms [9].…”

An Improved PSO-GWO Algorithm With Chaos and Adaptive Inertial Weight for Robot Path Planning

“…The current path planning algorithms mainly include the colony algorithms (Liu et al, 2019;Ye et al, 2020;Zhang et al, 2020;Zhu et al, 2020), PSO (Krell et al, 2019;Wang Y. B. et al, 2019;Liu X. H. et al, 2021;Song et al, 2021), A * algorithms (Xiong et al, 2020;Tang et al, 2021;Tullu et al, 2021), artificial potential field methods Azmi and Ito, 2020;Song et al, 2020;Yao et al, 2020), genetic algorithms (Hao et al, 2020;Li K. R. et al, 2021;Wen et al, 2021), fuzzy control algorithms (Guo et al, 2020;Zhi and Jiang, 2020), fast marching algorithms (Sun et al, 2021;Wang et al, 2021;Xu et al, 2021), and deep reinforcement learning algorithms (Li L. Y. et al, 2021;Lin et al, 2021;Xie et al, 2021). PSO is an evolutionary computation algorithm that can be used to find the optimal solution through collaboration and information sharing between individuals in the group, as in path planning, the optimal solution is to find the shortest path.…”