Research On Node Monitoring And Charging Path Planning Of Wireless Rechargeable Sensor Network

At present,wireless sensor network(WSN)plays an important role in many fields.However,the problem of node power supply has always been a bottleneck restricting the development of WSN.In the traditional power supply method,dry battery and new energy supply will be restricted by battery capacity and environment.The deployment of many static charging devices in WSN will increase the cost of the system.In view of the shortcomings of the traditional node power supply method,the wireless power supply method is used to dynamically charge the mobile charging car to the node according to the planned route.monitor.Solve the long-term contact wear problem in the WSN power supply mode,and at the same time achieve the goal of scientific charging path and node status visualization.In the process of completing the system design,it mainly solves the problems of the hardware design of the node and the wireless charging platform,the software development of the embedded end of the node,the software configuration on the OceanConnect platform of the Internet of Things,and the realization of the path planning algorithm for the mobile charging car.To achieve the expected effect,the mobile charging car can charge the node normally.The node collects the battery voltage,power consumption,environmental temperature and humidity information and successfully uploads it to the OceanConnect platform for clear display.The OceanConnect platform can normally issue instructions.The node that receives the instruction will The collected GPS information is uploaded.After the particle swarm optimization algorithm provides the ant colony algorithm with initial pheromone guidance,the ant colony algorithm's global search capability is enhanced.Using the improved ant colony algorithm for the shortest Hamilton loop designed for mobile charging cars,the distance is significantly shorter than the basic ant colony algorithm.Build a robot platform according to the preset purpose of the experiment,and introduce the designed grid method,improved ant colony algorithm and dynamic window algorithm into the corresponding function package of the ROS system,as a scientific obstacle avoidance strategy,after testing the Turtlebot robot Can successfully avoid static dynamic obstacles.Finally,the improved ant colony algorithm is compared with the basic ant colony algorithm on the Matlab platform.It is concluded that the improved ant colony algorithm in the shortest path is shortened by 0.8%and the number of iterations is shortened by 79%.The group algorithm is more suitable as a path planning algorithm for mobile charging cars.The obstacle avoidance experiment during the travel of the mobile charging car is completed.The results show that the mobile charging car is sensitive to obstacles and can successfully avoid obstacles.This path planning diagram is generated in the ROS system.Figure[67]table[6]reference[71]...