Deep-Reinforcement-Learning-based Path Planning for Industrial Robots using Distance Sensors as Observation

TL;DR

This paper introduces a deep reinforcement learning approach for industrial robot path planning that outperforms traditional sampling-based methods in complex environments, reducing computation time and path length.

Contribution

The paper presents a novel deep reinforcement learning-based motion planner that generalizes to unknown environments and improves planning efficiency for robotic manipulators.

Findings

Outperforms RRT in path length and execution time

Effective in complex, unknown environments

Reduces planning time compared to traditional methods

Abstract

Industrial robots are widely used in various manufacturing environments due to their efficiency in doing repetitive tasks such as assembly or welding. A common problem for these applications is to reach a destination without colliding with obstacles or other robot arms. Commonly used sampling-based path planning approaches such as RRT require long computation times, especially in complex environments. Furthermore, the environment in which they are employed needs to be known beforehand. When utilizing the approaches in new environments, a tedious engineering effort in setting hyperparameters needs to be conducted, which is time- and cost-intensive. On the other hand, Deep Reinforcement Learning has shown remarkable results in dealing with unknown environments, generalizing new problem instances, and solving motion planning problems efficiently. On that account, this paper proposes a Deep-Reinforcement-Learning-based motion planner for robotic manipulators. We evaluated our model against state-of-the-art sampling-based planners in several experiments. The results show the superiority of our planner in terms of path length and execution time.