Laser Radar And Neural Networks-based Mobile Robot Local Path Planning

Path planning is one of the most important issues of mobile robot. This paper concentrates on the local path planning of the mobile robot in uncertain environment.Artificial potential field imitates the concept of the tendency field in physics. Supposed the environment exert some strength on the robot, the direction of the strength is the advancing direction of the robot. This algorithm is convenient to be realized, but there are inherent limitations, such as trap situations due to local minima (cyclic behavior), no passage between closely spaced obstacles, oscillations in narrow passages. For the local minima, an additional strength towards the free area is used in the direction of the robot forwarding.Q-learning offers the intelligence system a kind of learning ability by utilizing the experienced movement array to choose the optimum movement in the environment of Malkov. The robot action is selected by simulated annealing algorithm. Each action's value is adjusted based on resembling among actions to improve the adaptive capacity of robot in environment. However, the Q-learning has some disadvantages such as calculation inconveniently, longer planning cycle (1.5 times compared to the artificial potential field) and lager memory space (4 times to the artificial potential field).A synthesized method of the local path planning, which can realize the mutual supplement with advantages of these two algorithms, is proposed in this thesis. BP neural network is introduced to classify the robot environment. This environment is divided into four kinds: closely spaced obstacles, corridor, U-shape area and other. In the last kind of environment, the algorithm of artificial potential field is used and is shown computation simpleness. The Q-learning works in the environment of closely spaced obstacles and corridor in which the artificial potential field can't work well enough. The local path planning can't guaranteepassing obstacles for U-shape, thus the robot avoid the obstacles along the relatively near U-shape border.The laser range finder has very long range, high precision and quick transmission speed, and is suitable for avoiding obstacles in real time environment. Real-time environment information is obtained through laser radar, and acts as the input for the classified BP network, artificial potential field and Q-learning.It is proved that the method can realize mutual supplement with artificial potential field and Q-learning each other by test and simulation. Through the integrated application of these two algorithms, the robot can find the almost optimum route under the uncertain environment, prevent collisions with fixed obstacles or dynamic ones, and reach the destination safely.