Research on Path Planning of Improved RRT Algorithm in Complex Environment
Traditional fast expanding random tree(RRT)algorithm has many redundant nodes and low success rate in path planning in complex environment.It proposes an improved RRT path planning algorithm ED-RRT based on the combination of entry detection strategy and heuristic dynamic circular sampling strategy.Firstly,the algorithm proposes a range-seeking strate-gy to quickly find the accessible path in a complex environment.At the same time,heuristic strategy is adopted to optimize the se-lection of random points,accelerate the effective planning speed and reduce the generation of redundant branches.Secondly,the greedy algorithm is introduced to optimize the path and solve the problem of too many redundant points.Simulation results show that the proposed algorithm has obvious advantages over the traditional RRT algorithm and bias-RRT algorithm in terms of iter-ation time,iteration times,path length and path planning.
Path PlanningFast Search Random TreeEntrance Finding StrategyHeuristic Strategy
万方数据
维普
