Abstract
In order to plan the robot path in 3D space efficiently, a modified Rapidly-exploring Random Trees based on heuristic probability bias-goal (PBG-RRT) is proposed. The algorithm combines heuristic probabilistic and bias-goal factor, which can get convergence quickly and avoid falling into a local minimum. Firstly, PBG-RRT is used to plan a path. After obtaining path points, path points are rarefied by the Douglas-Peucker algorithm while maintaining the original path characteristics. Then, a smooth trajectory suitable for the manipulator end effector is generated by Non-uniform B-spline interpolation. Finally, the effector is moving along the trajectory by inverse kinematics solving angle of joint. The above is a set of motion planning for the manipulator. Generally, 3D space obstacle avoidance simulation experiments show that the search efficiency of PBG-RRT is increased by 217%, while search time is dropped by 168% compared with P-RRT (Heuristic Probability RRT). After rarefying, the situation where the path oscillated around the obstacle is corrected effectively. And a smooth trajectory is fitted by spline interpolation. Ultimately, PBG-RRT is verified on the ROS (Robot Operating System) with the Robot-Anno manipulator. The results reveal that the validity and reliability of PBG-RRT are proofed in obstacle avoidance planning.
Highlights
Motion planning is divided into path planning and trajectory planning [1]–[3]
The Rapidly-exploring Random Trees (RRT)algorithm is a fast algorithm of path planning based on random sampling
Several improvements have been proposed for shortcomings of RRT and manipulator motion planning optimization
Summary
Motion planning is divided into path planning and trajectory planning [1]–[3]. Path planning focuses on generating a path, and trajectory planning gives information of time to a path [4]. The Rapidly-exploring Random Trees (RRT)algorithm is a fast algorithm of path planning based on random sampling. It is widely used by researchers because RRT can. C. Yuan et al.: Heuristic Rapidly-Exploring Random Trees Method for Manipulator Motion Planning probability-based sampling, which improved the search efficiency of the basic RRT algorithm. Error interval; ‘‘Reached’’ means that the new node reaches the error interval of the goal node, that is, the path planning is completed; ‘‘Trapped’’ represents a collision during the expansion process, the expansion fails; ‘‘Graph’’ represents the generation of the search tree path map [23]. The algorithm can trace the path, a lot of computing resources and time are consumed
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