In dynamic wireless power transmission (DWPT) system, the design of noncontact transformers plays a significant role in output power and its stability, which is closely related to the coupling coefficient. This article presents a design method for DWPT system based on automated guided vehicle (AGV), and takes into account W-type structure of noncontact transformer optimization. Therefore, through the adjustment of multiple physical parameters (the width of the primary coil, ferrite size, and spacing) the goal of 1.8 kW output power with minimal ferrite usage during the movement of the vehicle is achieved. Moreover, through the combination of finite element method and sensitivity analysis based on Latin hypercube sampling (LHS), a high-precision response surface is established, and the noncontact transformer is multiobjective optimized through particle swarm optimization algorithm. Finally, based on Pareto front of the optimization results, a platform based on the compensation topology of inductorcapacitorcapacitorseries(LCC-S) is built. Experimental results are highly consistent with the design, achieving 1.8 kW stable output power during AGV movement and the efficiency of the entire system reaches 92.7&#x0025;. 

A PSO-Based Optimization Design of W-Type Noncontact Transformer for Stable Power Transfer in DWPT System

Aiming at the problems of poor flexibility, complicated path maintenance and poor positioning performance in the current guidance technology of AGV, this paper designs and implements a new navigation system of automated guided vehicle (AGV) based on AprilTags2 auxiliary positioning. The system uses the Robot Operating System (ROS) as a platform to develop AGV navigation functions. The navigation system comprises two parts: the hardware and software layers. Firstly, hardware selection is performed after considering the actual requirements, performance, cost, and other factors in the hardware layer. Simultaneously, the AGV chassis and single-steering wheel walking mechanism are built to provide a stable and flexible operating platform for the software layer. Secondly, the software layer design includes two parts, namely the ROS navigation planning end and AprilTags2 detection. The ROS planning navigation end performs the design of four functional modules (i.e., AGV map construction, autonomous positioning, path planning, and path tracking), while the AprilTags2 detection part obtains the visual positioning pose of AGV by setting the AprilTags2 on each site and using the Kinect1 camera to detect. A more accurate AGV pose can be obtained by merging the former pose with the kinematic estimated pose. Finally, the two groups of positioning errors of the system before and after adding the AprilTags2 auxiliary positioning are tested and compared. The experimental results show that the navigation accuracy of the proposed system with AprilTags2 auxiliary positioning is significantly improved.

Yunyun Wang

Papers

AGV Navigation Based on AprilTags2 Auxiliary Positioning