Dynamic A* Algorithm to Improve Dynamic Path Planning of Unmanned Epidemic Prevention and Killing Vehicles

Abstract

Since personnel in complex regions or contaminated areas cannot enter to achieve epidemic prevention and killing operations, the application requirements for self-propelled epidemic prevention robots are becoming more and more extensive. The path planning algorithm is a key technology for robots in the eradication and epidemic prevention, but some node information in the original map will change in real time during the eradication process, which greatly reduces the robot's ability to work in epidemic prevention. This article first designed a relatively complex 900×900 point map and implemented the dynamic path planning of the Dynamic A * (D*) algorithm using the python language. The simulation results show that the algorithm greatly shortens the time of secondary path planning after encountering obstacles, and improves the reaction speed of the robot in epidemic prevention. Preliminary verification of the feasibility of D* algorithm in the path planning of self-propelled anti-epidemic robots.