关键词: 路径规划, 机械臂, 空间体素化, 动态目标引导边界框, 自适应步长, 机械臂避障

Abstract: It addresses the challenges of strong search randomness,slow convergence,and high computational complexity in robotic arm 3D path planning by proposing the Spatial Cell-Dynamic Target Guided Bounding box-Adaptive Step-BI-RRT^*,(SC-DTGB-AS-BI-RRT^*) algorithm. It combines adaptive step sizes with a Bidirectional Rapidly-exploring Random Tree (BI-RRT^*) approach,enhanced by space voxelization and dynamic target-guided bounding boxes.The algorithm′s multi-level optimization strategy first uses space voxelization to discretize 3D space,reducing computational load and boosting environmental perception accuracy. A dynamic target-guiding and bounding-box strategy adjusts the search focus across iterations: global exploration initially,a balance of exploration and convergence mid-way,and rapid path convergence later.The dynamic bounding box adapts the sampling range based on obstacle density,shrinking in cluttered areas and expanding in open spaces to enhance sampling efficiency. An adaptive step-size mechanism allows for large steps in free spaces and small steps near obstacles or during path convergence.Finally,cubic B-spline curves smooth the planned paths,reducing robotic arm joint jitter and improving energy efficiency. The algorithm′s effectiveness is validated through 3D comparative experiments. It is then integrated with a robotic arm′s obstacle-avoidance function and deployed on the PUMA 560 robotic arm. Accurate obstacle-avoidance trajectory planning is achieved using the MATLAB R2022a simulation platform.

Key words: path planning, robotic arm, spatial voxelization, dynamic target-guided bounding box, adaptive step size, mechanical arm obstacle avoidance