基于改进RRT-Connect算法的无人车路径规划OA

To address the issues of blind searching,redundant nodes,and non-smooth paths inherent in the tradi-tional rapidly-exploring random tree connect algorithm for unmanned vehicles,a series of improvements were pro-posed in goal sampling,node expansion and trajectory optimization.Firstly,a goal-guided dynamic probability sampling strategy was introduced to filter the randomly selected points,thereby improving sampling efficiency and accelerating convergence.Next,an improved artificial potential field component based on the escape force was in-corporated into the node expansion process to help the unmanned vehicle avoid getting trapped in local minima while enhancing its target-searching capability and node expansion efficiency.Finally,a trajectory quality evalua-tion function was constructed to assess the safety,deviation,and smoothness of the trajectories generated by the un-manned vehicle at different time steps.The trajectory with the minimum cost value was then selected to guide the vehicle's motion.The enhanced algorithm was simulated and compared with the traditional RRT-Connect algorithm in different testing environments.The simulation results showed that,compared to the traditional algorithm,the proposed algorithm could reduce the average path length by 9.83%and the average planning time by 85.40%in simple obstacle environments.In narrow passage environments,the average path length and planning time could be reduced by 10.56%and 64.63%,respectively.In U-shaped obstacle environments,the average path length and planning time could be reduced by 22.82%and 66.92%,respectively.Furthermore,the proposed algorithm sig-nificantly improved the path planning success rate in complex environments,making it more suitable for autonomous vehicle path planning.