Dynamic Obstacle Avoidance Of Mobile Robots Under Complicated Environments

In recent years,robots play an increasingly important role in human society,especially mobile robots are gradually integrating into people's daily life.However,whether it's a sweeping robot for home use,or a takeaway robot,express delivery robot,patrol robot,etc.for outdoor use,they are faced with obstacle avoidance problems.In their application scenarios,there are not only static obstacles,but also dynamic obstacles such as people,animals and other means of transportation.To avoid obstacles is the basic requirement for mobile robots,as well as one of the core issues in robotics research.In this paper,we first propose an end-to-end dynamic obstacle avoidance algorithm based on deep reinforcement learning for mobile robot in dynamic environment.This algorithm proposes a new policy network,which creatively combines the long short-term memory units of processing text sequence information with the convolution layer of processing image information to form a new feature extraction network.On this basis,this paper designs a new reward function,combined with the proximal policy optimization along with regularized term,and gets the optimized obstacle avoidance policy model after training.The input of the algorithm is lidar scanning data,target position coordinate parameters and the speed information of robot body.Based on these parameters,the speed instructions for robot flexible obstacle avoidance can be output directly.The integrated average obstacle avoidance success rate of the various complex simulation environments set in the simulation experiment part of this paper can reach more than 70%.In the hardware part,the navigation obstacle avoidance sensor system which combines 3D lidar and UWB positioning system is used innovatively.At the same time,the connection and power supply scheme of the sensor is optimized,so that the sensor can be successfully deployed on the Laikago dog robot.Then,a dynamic obstacle avoidance algorithm based on deep reinforcement learning is tested on the robot,which proves that the algorithm can avoid dynamic obstacles flexibly and has a strong practical value.A new dynamic obstacle avoidance algorithm based on dynamic window method is also proposed in this paper to solve the problem of poor interpretability of algorithm based on deep reinforcement learning and low success rate of traditional dynamic window method in dynamic obstacle environment.The algorithm adopts a new evaluation function and a new constraint condition which combines the distance increment from the robot body to the target point and the minimum obstacle distance,and improves the ability of dynamic window method to select the optimal speed in the speed space.According to the number of obstacles,the success rate of obstacle avoidance is increased by 5%-20%,and the time of robot reaching the target point is greatly shortened.In the hardware part,the navigation obstacle avoidance sensor system which combines depth camera and ultra wide-band positioning system is used.The algorithm is tested in real scene on Laikago dog robot,and the feasibility and practicability of the algorithm are verified.At the end of this paper,both the hardware kits and algorithms of these two dynamic obstacle avoidance systems are compared,analyzed,summarized and prospected.