公开(公告)号：US20240219923A1

公开(公告)日：2024-07-04

申请号：US17923114

申请日：2021-12-13

Applicant: SOUTHEAST UNIVERSITY

Inventor： Hua ZHANG ,  Na SU ,  Junbo WANG

IPC: G05D1/644 ,  B63B79/40 ,  G05D101/15 ,  G05D109/30

CPC classification number: G05D1/644 ,  B63B79/40 ,  G05D2101/15 ,  G05D2109/30

Abstract: The present invention discloses an intention-driven reinforcement learning-based path planning method, including the following steps: 1: acquiring, by a data collector, a state of a monitoring network; 2: selecting a steering angle of the data collector according to positions of surrounding obstacles, sensor nodes, and the data collector; 3: selecting a speed of the data collector, a target node, and a next target node as an action of the data collector according to an ε greedy policy; 4: determining, by the data collector, the next time slot according to the selected steering angle and speed; 5: obtaining rewards and penalties according to intentions of the data collector and the sensor nodes, and updating a Q value; 6: repeating step 1 to step 5 until a termination state or a convergence condition is satisfied; and 7: selecting, by the data collector, an action in each time slot having the maximum Q value as a planning result, and generating an optimal path. The method provided in the present invention can complete the data collection path planning with a higher probability of success and performance closer to the intention.