A Path Planning Framework for Robots Based on Improved Parallel Sampling RRT and Offset Guidance DWA

The path planning is one of the critical technologies for robots to achieve the autonomous operation, enabling to quickly find a safe path in dynamic environments. However, relying on global path planning alone cannot avoid dynamic obstacles, while only using the local path planning may lead to falling into local minima. Therefore, a two-layer robot path planning method suitable for dynamic environments is proposed. This two-layer strategy consists of an efficient global path planning layer and a safe local dynamic obstacle avoidance layer. In the first layer, a parallel sampling and bidirectional guidance rapidly exploring random tree algorithm (PB-RRT) is proposed to search for the global path. To enhance efficiency, the parallel heuristic sampling is introduced to replace the random sampling in bidirectional rapidly exploring random tree (Bi-RRT), and an evaluation function incorporating distance and corner factors is designed to select optimal sampling nodes for adaptive expansion, making the sampling process directional and avoiding over-exploration of space. A bidirectional guidance mechanism further accelerates the merging of the two trees by fully utilizing newly generated node information. Then, a path optimization (PO) method is proposed to improve the length and smoothness of the initial path and obtain the key nodes of the path. In the second layer, the key nodes obtained from the first layer are used as dynamic subtargets, and the safe dynamic window approach (SDWA) is used to achieve the dynamic obstacle avoidance. To further enhance safety, an offset guidance method is proposed to flexibly steer the robot around dynamic obstacles. Extensive experiments show that the average planning time of PB-rapidly exploring random tree (RRT) is reduced by 67.7% compared with Bi-RRT, while the path quality is improved and the path length is reduced by 27.1%. The proposed method also effectively avoids dynamic obstacles in environments with obstacle densities exceeding 60% and achieves the maximum of minimum distance to dynamic obstacles, validating the feasibility and safety of the method.