Research on industrial robot path planning based on improved RRT algorithm

Path planning is a supporting technology in the field of industrial robot research. Appropriate path planning algorithms can ensure the safety of industrial robots at work and improve their work efficiency. This paper proposes an SRRT path planning algorithm based on Sarsa reinforcement learning, which retains the high efficiency of RRT node expansion and uses the feedback value of the expanded node as the evaluation criterion for the expansion process for subsequent learning processes. By improving the greedy strategy and reward function, the random tree follows the idea of "exploring first and then using", selects valid nodes with high feedback values as much as possible, reduces the amount of calculation in the subsequent learning process, improves the utilization rate of valid nodes, and expands the applicable environment of the algorithm.