Real-time obstacle avoidance algorithm for robots based on BP neural network

To address the problem of obstacle avoidance and path planning of intelligent robots in two-dimensional static environment, a real-time obstacle avoidance algorithm based on BP neural network is proposed. Firstly, multiple sectors are used to represent the environment around the robot, and lidar is utilized to detect the distance information of obstacles in each sector. With the distance information of obstacles in each sector, BP neural network is used to calculate the score of the sector selected as obstacle avoidance direction. Then, the Euclidean distance between the mid-point coordinate of each sector and the mid-point coordinate of the closest sector to the obstacle at the current moment is used to calculate the conditional probability. Each sector is selected as the direction of obstacle avoidance under the current pose of the robot. Finally, the sector with the largest product of score and conditional probability is taken as the obstacle avoidance direction of the robot. Experimental results show that the convergence time of the proposed algorithm is 50% less than that of the grid method, and the obstacle avoidance trajectory of the robot is shorter than that of the artificial potential field method. It can be better applied to complex multi-obstacle scenarios. © 2019, Science Press. All right reserved.