System and method for optimizing path exploration parameters based on deep reinforcement learning

A deep reinforcement learning-based path exploration parameter optimization method, comprising a non-transitory computer readable medium operable on a computer with memory for the deep reinforcement learning-based path exploration parameter optimization method, and comprising program instructions for executing the following steps of:

• S1:
• generating an optimal step length and an optimal steering angle based on a deep reinforcement learning network according to a current node and environmental information, and constructing a fixed steering angle set;
• performing node exploration by combining the optimal step length with the fixed steering angle set to generate a child node set, and combining the optimal step length with the optimal steering angle to generate a steering angle-optimized child node which is added to the child node set, and
• S2:
• performing collision detection on the child nodes in the child node set and calculating cost values of all the child nodes, and
• S3:
• obtaining the child node with the lowest cost value in each iteration round of the search process as the final selected next node of the current node, and
• S4:
• when the distance from the current node to the target point is less than a set threshold, generating a loading and parking path using a Reeds-Shepp curve, and generating a planned path through node backtracking;
• and
• S5:
• operating mining with reducing costs and a performance efficiency based on results of the deep reinforcement learning-based path exploration parameter optimization method.