Trajectory Planning using Transfer Learning and Wireless Sensor Network for Target Search Mobile Robot

Abstract

Trajectory planning is the core technology in the field of mobile robot research. However, the traditional trajectory planning algorithm has the problems of low accuracy and high complexity. Therefore, we propose a trajectory planning algorithm for target search mobile robot using transfer learning (TL) and wireless sensor network. First, the mobile robot motion model is constructed and converted into a linear motion model; Second, the robot kinematic constraints such as velocity, acceleration and position are analyzed; Then, the ultrasonic sensor is constructed to sense the robot's self and environmental information, and the path planning is performed by the combination of global planning and local planning. Finally, a combination of global planning and local planning is used for path planning, and the knowledge is learned from global planning based on TL, and the relevant factors of the global planning model are applied to local planning as the initial motion state factors of the robot to realize the trajectory planning of the target-search mobile robot. The results show that the fitting of the proposed algorithm and the optimal trajectory planning results is high, the reward value of robot training is higher, the trajectory planning velocity curve is smoother, and the space complexity of the proposed algorithm is much lower than other algorithms, the highest is only 23%, and the accuracy of trajectory planning is as high as 95.6%, which has practical applicability.