Research On Path Planning For Mobile Robot Based On Reinforcement Learning

With the development of robot technology, the robot has begun to be applied to the unknown environment now, compared with the research on the path planning in the known environment, the unknown environment brings new challenges to the path planning of environment exploration for mobile robot. Ineluctably, mobile robot will encounter a variety of obstacles when exploring because there is no prior knowledge of the environment for robot. Therefore, the mobile robot which can obstacle avoidance and has a flexible planning in an unknown environment has a very important practical significance.In this paper, we use reinforcement learning algorithm to study the path planning for mobile robot based on the exploration research in unknown environment. The reinforcement learning algorithm Q-learning algorithm and algorithm can achieve mobile robot path planning, but these two algorithms are difficult to achieve the desired results, especially in the large and complex environment, the biggest drawbacks of which are the long time to learn and slow rates of convergence. Aiming at the problem of slow convergence and long learning time for Q-learning based mobile robot path planning, a state-chain sequential feedback Q-learning algorithm based on the idea of backtracking is proposed in this paper for quickly searching for the optimal path of mobile robots in complex unknown static environments. The state chain is built during the searching process. After one action is chosen and the reward is received, the Q-values of the state-action pairs on the previously built state chain are sequentially updated with one-step Q-learning. With the proposed algorithm, the single robot can solve the problem of obstacle avoidance and multiple robots can solve the collision avoidance during the path planning in unknown environment. Extensive simulations validate the efficiency of the newly proposed approach for mobile robot path planning in complex environments. The results show that the new approach has a high convergence speed and that the robot can find the collision-free optimal path in complex unknown static environments with much shorter time.Firstly, the paper analyzes the research background and significance of the mobile robot path planning, sum up the research and development of the mobile robot path planning at home and abroad, as well as the main problems. Then the main content and chapters framework of this paper are described brief.Secondly, this part introduces the main type of mobile robot path planning technology, and present the global path planning algorithm and local path planning algorithm in detailed; as for the reinforcement learning algorithm, this section introduces the research, development trend and the existence of the problem of the reinforcement learning algorithm. Besides, the basic concepts, principles, methods and application of reinforcement learning algorithm are described in this part.The third part aims at the problem of long learning time, slow convergence and difficulty to apply to the larger, more complex environment for Q-learning algorithm and Q(λ) algorithm based mobile robot path planning, a state-chain sequential feedback Q-learning algorithm based on the idea of using backtracking to update the state data is proposed for quickly searching for the optimal path of mobile robots in complex unknown static environments. The extensive simulations of different environment show that the new approach has a high convergence speed and that the robot can find the collision-free optimal path in complex unknown static environments with much shorter time state chain is built during the searching process. so it provides a new method for mobile robot path planning.The fourth part studys the multiple mobile robot system based on the proposed high-performance reinforcement learning algorithm, each robot can solve the problems of avoidance and collision with other robots by learning exploring strategies in an uncertain environment path planning which can improve the efficiency of the target point is reached.Finally, conclusions are given with recommendation for future work.