Research On Intelligent Vehicle Development And Path Planning Algorithm For Field Detection

The intelligent vehicle for field detection task and its deployment path planning algorithm are important research contents in the automatic collection and transportation of field experimental samples.In this study,an intelligent vehicle is developed to assist collection of experimental samples and transportation to solve the problem of human resource consumption in collecting experimental samples in the field environment.In view of the strong randomness of sampling point calibration and random direction of search tree expansion existing in the sampling-based global path planning algorithm deployed by intelligent vehicle,a improved path planning algorithm is applied to the developed intelligent vehicle.In order to verify the feasibility of developed intelligent vehicle structure and its improved path planning algorithm,thesis takes the collection of field soil samples as an example to carry out and design field soil heavy metal detection experiment.The relevant research contents and results are as follows:(1)In view of the complex field environment,a field sampling intelligent vehicle is developed to provide a hardware platform for automatic collection and transportation of field experimental samples.Aiming at the specific functions such as automatic sample collection and storage,arriving at the artificial calibration target point and feedback of the real-time position information of the intelligent vehicle,the sample collection device is developed,and the chassis structure of the intelligent vehicle is designed.And combined with the core controller,data transmission module and other hardware modules to build a field sampling intelligent vehicle.(2)An improvement based on the original fast expanding random tree(RRT)for intelligent vehicle development.Partition probability sampling is introduced and parent node selection strategy is improved to improve the overall operation efficiency of the original algorithm.In addition,the path compression algorithm and B-spline curve are used to solve the problem of redundant nodes in the planned path.Finally,simulation experiments were carried out based on the two maps,and performance comparisons were made with the original RRT and asymptotically optimal fast search random tree(RRT*)to verify the effectiveness of the improved algorithm in complex environments.The results show that compared with the original RRT,the number of sampling points are reduced by 32.01% and 42.93%,the running time is reduced by 25.89% and 33.33%,and the planned path length is reduced by 4.50% and 7.43%,respectively.Compared with RRT*,the number of sampling points are reduced by 34.89% and 47.47%,the running time is reduced by 52.29% and 52.94%,and the planned path length is reduced by 3.26%and 3.54%.(3)Experimental design of field soil heavy metal pollution detection.With the improved RRT as the software basis and the field sampling intelligent vehicle as the hardware platform,the soil pollution analysis experiment in the field was carried out in combination with the embedded control system.The experimental results show that the driving time of the field sampling intelligent vehicle in the five planned routes are 13.2s,8.5s,5.6s,6.1s and 18.5s respectively,and the total time of collecting and transporting experimental samples for the artificially calibrated sampling points is 165.84 s.In addition,the intelligent vehicle can successfully transport the experimental samples back to the monitoring site for follow-up detection work,which meets the requirements of this experiment.To sum up,the developed field sampling intelligent vehicle with an improved RRT algorithm is used in the collection and transportation of experimental samples in the field environment,which accords with the design goal,and its performance is good in the field detection experiment,which provides a new idea for the collection and transportation of field environmental samples,and is of great practical value.