基于空间体素化的动态目标引导边界框的自适应步长BI-RRT*机械臂路径规划OA

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 optimi-zation strategy first uses space voxelization to discretize 3D space,reducing computational load and boosting environmental per-ception accuracy.A dynamic target-guiding and bounding-box strategy adjusts the search focus across iterations:global explora-tion initially,a balance of exploration and convergence mid-way,and rapid path convergence later.The dynamic bounding box a-dapts the sampling range based on obstacle density,shrinking in cluttered areas and expanding in open spaces to enhance sam-pling 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.