Abstract: In the field of mobile robot motion path planning,the asymptotically optimal bidirectional rapid exploration random tree(B-RRT^*) algorithm has good obstacle avoidance and path search capabilities,but B-RRT^* has the shortcomings of many iterations and long planning time.As an efficient branch of the B-RRT^* algorithm,the kinematic constraint-based bidirectional rapid exploration random tree(KB-RRT) algorithm can effectively reduce the expansion of invalid trees and speed up the search for the optimal path,but the number of iterations of the algorithm is too large.For the improvement of the B-RRT^* algorithm,the latest B-RRT^* improved algorithm is the kinematically constrained B-RRT^*(KB-RRT^*) algorithm with efficient branches.Although the KB-RRT^* algorithm can effectively reduce the expansion of invalid trees,to speed up the search for the optimal path,but the number of iterations of the algorithm is still too large.Therefore,this paper proposes an improved B-RRT^* algorithm(AFB-RRT^*) based on adaptive sampling and fast search,which sets a safe area for obstacles and determines the search direction of a random tree according to the proposed adaptive sampling and fast search,reduces redundant sampling points,that is,AFB-RRT^* can achieve fast convergence in path planning.Simulation and experiments show that,compared with KB-RRT^*,AFB-RRT^* reduces the planning time and the number of convergence iterations under the premise that the planned path length is basically the same.

Key words: B-RRT^*, KB-RRT^*, AFB-RRT^*, Convergence iterations, Planning time