A Neural Network Model and Reinforcement Learning for Dynamic Formation Moving and Obstacle Avoidance of Autonomous Mobile Robot

자율이동로봇의 동적 편대 헝성과 장애물 회피를 위한 신경망 구조 및 강화학습

The objective of this paper is, based upon the principles of artificial life, to induce emergent behaviors of multiple autonomous mobile robots which form from simple local rules to complex global intelligence. Here, we propose an architecture of neural network learing with reinforcement signals which perceives the neighborhood information and decides the direction and the velocity of movement as mobile robots navigates in a group. As results of the simulations, the optimum weights are obtained in real time, which not only prevent from the collisions between agents and obstacles in the dynamic environment, but also have the mobile robots move and keep in various patterns.