Targeting some problems of the RRT_Connect path planning algorithm, such as average search and low efficiency, proposes an improved RRT_Connect algorithm that may optimize the searched nodes and parts of planned paths. Firstly, an improved RRT_Connect algorithm based on destination and searched node bias strategy is proposed. Secondly, an improved RRT_Connect algorithm is put forward for the optimization of the searched nodes and some planned paths to deal with the problem of low quality reflected in the improved RRT_Connect path planning algorithm, and the optimization for the cost of path planning by figuring out valid new nodes and parent nodes of adjacent nodes within a certain range. On this basis, the path planning algorithm is verified by simulation and actual experiments. It is shown by the experimental results that the improved RRT_Connect algorithm proposed in this paper can not only shorten the time and length of path planning but also decrease the number of search iterations and nodes.

To address the issues of exploration imbalance and slow convergence speed in the Q‐learning path planning algorithm, an adaptive improved Q‐learning path planning algorithm based on an obstacle learning matrix and artificial potential field (APF) is proposed. First, an obstacle learning matrix is established to store the positions of obstacles encountered in each learning iteration, avoiding redundant learning of the same obstacle. Second, an adaptive exploration enhancement strategy is introduced by incorporating the concept of success rate. This strategy divides the decay process of the exploration rate into an exploration‐dominant initial stage and an exploitation‐dominant later stage. Finally, an APF‐weighted action selection strategy is introduced, utilizing the guiding force generated by the APF to encourage the mobile robot to avoid obstacles more efficiently. Simulation results show that the proposed method can effectively reduce the number of iterations and time consumption in the optimization process, resulting in a smoother and more stable planned path.