A novel hybrid path planning method based on q-learning and neural network for robot arm

Path planning for robot arms to reach a target and avoid obstacles has had a crucial role in manufacturing automation. Although many path planning algorithms, including RRT, APF, PRM, and RL-based, have been presented, they have many problems: a time-consuming process, high computational costs, slowness, non-optimal paths, irregular paths, failure to find a path, and complexity. Scholars have tried to address some of these issues. However, those methods still suffer from slowness and complexity. In order to address these two limitations, this paper presents a new hybrid path planning method that contains two separate parts: action-finding (active approach) and angle-finding (passive approach). In the active phase, the Q-learning algorithm is used to find a sequence of simple actions, including up, down, left, and right, to reach the target cell in a gridded workspace. In the passive phase, the joints angles of the robot arm, with respect to the found actions, are obtained by the trained neural network. The simulation and test results show that this hybrid approach significantly improves the slowness and complexity due to using the simplified agent-environment interaction in the active phase and simple computing the joints angles in the passive phase.