Deep Reinforcement Learning for Path Planning by Cooperative Robots: Existing Approaches and Challenges

• Published in: 2021 28th Conference of Open Innovations Association (FRUCT) Vol. 28; no. 1; pp. 349 - 357
• Main Authors: Othman, Walaa, Shilov, Nikolay
• Format: Conference Proceeding Journal Article
• Language: English
• Published: FRUCT 01.01.2021
• Subjects: Collision avoidance; cooperative robots; deep reinforcement learning; multi-agents; Path planning; Reinforcement learning; Robot sensing systems; Task analysis; Technological innovation; Unmanned vehicles
• ISSN: 2305-7254; 2305-7254; 2343-0737
• DOI: 10.23919/FRUCT50888.2021.9347628

Cooperative robots became a very important research topic due to their capability to accomplish tasks fast and more efficiently. In order for cooperative robots to accomplish their tasks, they need to be able to plan their paths in the environment without any collision. Path planning for cooperative robots with deep reinforcement learning is a new research topic in robotics and artificial intelligence. Path planning via deep reinforcement learning is an end to end method. The robot directly receives the data from the sensor (usually high dimensional images) and generates an optimal policy that plans a safe path to the target. In this paper, we first present an overview of reinforcement learning, deep learning, and deep reinforcement learning. Then, we introduce methods used for path planning. Finally, we discuss the challenges of deep reinforcement learning path planning for cooperative robots.