Path planning algorithm of robot arm based on improved RRT* and BP neural network algorithm

Abstract

To address the issues of slow motion planning, low efficiency, and high path calculation cost of the six-degrees of freedom manipulator in three dimensional multi-obstacle narrow space, a path planning method of the manipulator based on Back Propagation (BP) neural network and improved Rapidly expanding Random Tree* (RRT*) algorithm is proposed (referred to as BP-RRT*). Due to the spherical envelope of the obstacle, this method evaluates the connection between the path and obstacle in space using the triangular function and identifies the collision-free path in 3D space. Then, using the sampling space division, obstacles discretization, and distance weight function, the adaptive node sampling probability method of RRT* algorithm in space is proposed, to reduce unnecessary sampling nodes and optimize the sampling efficiency; because the sampling nodes might fall into the area with dense obstacles, which results in significant increase in the search time. A stepwise sampling method is proposed to modify the global search into a phased local search, train the BP neural network model, forecast the number of node samples in the local search at each stage, automatically guide the algorithm into the next stage to complete the search, and improve the path optimization efficiency. Finally, the simulation experiment of the improved BP-RRT* algorithm is executed on the Python and Robot Operating System, and the physical experiment is done on the Baxter manipulator. The effectiveness and superiority of the improved algorithm are determined by comparing it with the existing algorithms.

Keywords

• Robotic arm
• Path planning
• BP-RRT* algorithm
• Sampling space partitioning
• Region probability
• Staged local search