Design Of Control System Of Adaptive Intelligent Walking Device Based On ROS

This thesis was mainly aimed at the problems of poor adaptability and low mapping efficiency of the walking device in indoor large-scale planting environment.The wheeled intelligent walking device control system was developed by ROS(Robot Operating System)to improve the openness of the control system.The characteristics of high efficiency of the Marginalized Particle Filter improved the effectiveness of mapping in the path planning of intelligent walking devices.At the same time,GPS(Global Positioning System)/BDS(BeiDou Navigation Satellite System)dual-mode positioning module has been used to realize the positioning of the intelligent walking device.This thesis mainly completed the following research contents:(1)In terms of overall system design,this topic adopted and selected the ROS as the development platform for walking devices.Due to the characteristics of the distributed architecture,the design of the control system was open.By modifying the open source code,the compatibility of the intelligent walking device and the load execution device were improved,and the design of the wheel-based walking device control system was completed.The walking device was mainly composed of a four-wheeled walking chassis,a lidar measurement module and a dual-mode positioning module.(2)In terms of application scenarios,this topic drawed up an environmental scene of the Chinese character Tian-style’ planting ecological greenhouse,in which the operation positioning of the walking device was realized.Therefore,through the performance comparison of GPS/BDS and dual-mode positioning,the dual-mode positioning was finally used in the environmental scenario of the Chinese character Tian-style’ planting intelligent ecological greenhouse to complete the application of satellite positioning technology.(3)In terms of real-time mapping,this paper described the principles of the traditional particle filter Particle Filter algorithm and Marginalized Particle Filter Marginalized Particle Filter algorithm,then compared the superiority of the two algorithms.The final decision used the Marginalized Particle Filter algorithm.It extended the Kalman algorithm to obtain the posterior distribution in the Markov algorithm in specific situations,and then used particle filters to complete the subsequent processing,which can effectively reduce the sampling dimension and simplify the complexity of the original algorithm.At the same time,the algorithm can not only reduce the sampling dimension and the sample size required in complex terrain,simplify the complexity of the original algorithm effectively,but also can reduce the computer jamming phenomenon when processing redundant information.Under the same operating technical conditions,the effectiveness of environmental mapping was improved.(4)In terms of path planning,this topic applied existing path planning algorithms,analyzed the differences between global planning and local planning,and described the basic principles of Dijkstra and A*algorithms under global planning conditions.Dijkstra’s algorithm was not specific enough,and it was calculated by a more complicated‘graph’ search method,resulting in a large amount of calculation.Therefore,heuristic ideas had been introduced to optimize the path formed by specific target points through A*algorithm.At the same time,I adopted the Dynamic Window Approach avoid-obstacle strategy in local planning to complete the path planning requirements during the construction process.(5)After the positioning method is determined,the simulation effect test of the walking device was carried out with the ROS.The performance test of the walking device was carried out,starting from the ’S ’ and ’U’ bending simulation effects,the time of the ’Tian-style’ map construction,and the effectiveness of autonomous navigation.The final test results showed that the walking device in the dual-mode positioning had the ability to carry other load actuators and can effectively pass the ’S’ and ’U’ bends.The minimum turning radius was 0.46m,and the turning angle was 0～360°.The construction time of 10×10m and 10× 5m ’Tian-style’ planting ecological greenhouses was 14.7min and 7.5min respectively,and the efficiency of the construction of the two maps at the same time reaches 100%.The accuracy value of planning to the target point was 0.1m.