Path Planning for Manipulator Obstacle Avoidance Based on Improved RRT?Algorithm
Aiming at the problems of high randomness,low efficiency,and non-smooth paths in path plan-ning for manipulator obstacle avoidance using the rapidly exploring random tree∗(RRT∗)algorithm,an improved RRT∗ algorithm based on a goal-directed strategy and incorporating bidirectional expansion is proposed.A target bias function was incorporated on the basis of the traditional RRT∗algorithm to increase the sampling probability of the target point.Simultaneously,a bidirectional expansion mechanism was intro-duced to accelerate the expansion process.During the expansion of new nodes,duplicate node detection and removal are performed.After finding the path for the first time,an ellipsoidal subspace sampling strategy is employed to reduce the sampling space.Finally,a path shortening strategy and B-spline optimization are ap-plied.Simulation results in MATLAB demonstrate that compared to the RRT∗ algorithm,the proposed ap-proach achieves a 76.9%improvement in search time,a 5.6%reduction in planned path length,an 86.87%decrease in sampled nodes,and a 45.45%decrease in average path nodes.The robotic arm successfully a-chieves smooth obstacle avoidance,with no sudden changes in joint parameters during motion.Importantly,the proposed algorithm ensures successful obstacle avoidance for the manipulator,maintains smooth joint pa-rameters during motion.Further validates its feasibility through physical experiments with a real manipulator.
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