Obstacle Avoidance Path Planning for the Dual-Arm Robot Based on an Improved RRT Algorithm

In the future of automated production processes, the manipulator must be more efficient to complete certain tasks. Compared to single-arm robots, dual-arm robots have a larger workspace and stronger load capacity. Coordinated motion planning of multi-arm robots is a problem that must be solved in the process of robot development. This paper proposes an obstacle avoidance path planning method for the dual-arm robot based on the goal probability bias and cost function in a rapidly-exploring random tree algorithm (GA_RRT). The random tree grows to the goal point with a certain probability. At the same time, the cost function is calculated when the random state is generated. The point with the lowest cost is selected as the child node. This reduces the randomness and blindness of the RRT algorithm in the expansion process. The detection algorithm of the bounding sphere is used in the process of collision detection of two arms. The main arm conducts obstacle avoidance path planning for static obstacles. The slave arm not only considers static obstacles, but also takes on the role of the main arm at each moment as a dynamic obstacle for path planning. Finally, MATLAB is used for algorithm simulation, which proves the effectiveness of the algorithm for obstacle avoidance path planning problems for the dual-arm robot.