Multi-Robot Path Planning Method Using Reinforcement Learning

Abstract

This paper proposes a noble multi-robot path planning algorithm using Deep q learning combined with CNN (Convolution Neural Network) algorithm. In conventional path planning algorithms, robots need to search a comparatively wide area for navigation and move in a predesigned formation under a given environment. Each robot in the multi-robot system is inherently required to navigate independently with collaborating with other robots for efficient performance. In addition, the robot collaboration scheme is highly depends on the conditions of each robot, such as its position and velocity. However, the conventional method does not actively cope with variable situations since each robot has difficulty to recognize the moving robot around it as an obstacle or a cooperative robot. To compensate for these shortcomings, we apply Deep q learning to strengthen the learning algorithm combined with CNN algorithm, which is needed to analyze the situation efficiently. CNN analyzes the exact situation using image information on its environment and the robot navigates based on the situation analyzed through Deep q learning. The simulation results using the proposed algorithm shows the flexible and efficient movement of the robots comparing with conventional methods under various environments.

Special Issue on Mobile Robots Navigation

“…In some applications, the collaboration between the members of a team of robots can be of interest. Bae et al [9] propose a multi-robot path planning algorithm that tries to overcome some of the shortcomings of conventional methods, such as the adaptation to complex and dynamic systems and environments. In multi-robot navigation, depending on the situation of the mission, each robot can be seen either as a moving obstacle which performs independent actions or as a cooperative robot that collaborates with other robots.…”

Path Planning for Multi-Arm Manipulators Using Deep Reinforcement Learning: Soft Actor–Critic with Hindsight Experience Replay

“…Due to this reason, an effective path planning algorithm for multi-arm manipulators has to be developed. In the literature about path planning, there are already some deep learning-based approaches implemented for robot applications such as mobile manipulation [ 20 , 21 ], unmanned ship [ 22 ] and even for multi-mobile robot [ 23 ]. These imply that deep learning-based approach can be promising in path planning for single arm manipulator [ 24 ] and also for multi-arm manipulators [ 25 ].…”

Motion Planning of Robot Manipulators for a Smoother Path Using a Twin Delayed Deep Deterministic Policy Gradient with Hindsight Experience Replay

“…Recently, deep learning-based control and operation of robot manipulators have drawn much attention. In [29,30], robot path planning methods are proposed using a deep Q-network algorithm with emphasis on learning efficiency. For path training, a stereo image is used to train DDPG (Deep Deterministic Policy Gradient) in [31].…”