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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.

Constrained by the numbers of action space and state space, Q-learning cannot be applied to continuous state space. Targeting this problem, the double deep Q network (DDQN) algorithm and the corresponding improvement methods were explored. First of all, to improve the accuracy of the DDNQ algorithm in estimating the target Q value in the training process, a multi-step guided strategy was introduced into the traditional DDQN algorithm, for which the single-step reward was replaced with the reward obtained in continuous multi-step interactions of mobile robots. Furthermore, an experience classification training method was introduced into the traditional DDQN algorithm, for which the state transition generated by the mobile robot–environment interaction was divided into two different types of experience pools, and experience pools were trained by the Q network, and the sampling proportions of the two experience pools were updated through the training loss. Afterward, the advantages of a multi-step guided DDQN (MS-DDQN) algorithm and experience classification DDQN (EC-DDQN) algorithm were combined to develop a novel experience classification multi-step DDQN (ECMS-DDQN) algorithm. Finally, the path planning of these four algorithms, including DDQN, MS-DDQN, EC-DDQN, and ECMS-DDQN, was simulated on the OpenAI Gym platform. The simulation results revealed that the ECMS-DDQN algorithm outperforms the other three in the total return value and generalization in path planning.