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.
SEAR: A Polynomial- Time Multi-Robot Path Planning Algorithm with Expected Constant-Factor Optimality Guarantee
