A path planning algorithm for mobile robot based on edge-cloud collaborative computing

Path planning is a key problem to be solved for mobile robot to realize autonomous navigation. It is a typical computing intensive task, and high computing capacity is needed. The computing power carried by the mobile robot is difficult to support the calculation of path planning, and the traditional cloud computing model cannot meet the real-time requirement of path planning. In order to solve the problem of insufficient computing power and improve the execution efficiency and real-time performance, a real-time computing framework based on edge-cloud collaborative computing is constructed for path planning. In each control decision cycle, the mobile robot as the edge acquires the sensing data and transmits it to the cloud. By stream computing, the cloud plans the path in real-time. The edge integrates the planned path from the cloud and the partial obstacle avoidance result from the edge as a path sequence. The final path sequence is sent to the motion control layout, and drives the mobile robot to the target. By the edge-cloud collaborative computing, the computing capability of the edge is extended. By taking use of high real-time performance of stream computing, the proposed algorithm improves the efficiency of path planning. By taking use of the storage capacity of the cloud, environmental memory is realized and the problem of local traps is solved. Simulation experiment results in different environments show that the planned path by the proposed algorithm gets a higher path quality and shorter execution time comparing the other several traditional path planning algorithms. Experiments in real environment verify the feasibility and effectiveness of the algorithm.