| With the continuous development of science and technology,the application of indoor tracking robots in the commercial field is gradually increasing,and the research prospects are also increasingly broad.However,high component costs and poor environmental adaptability have become important factors restricting development.At the same time,it is difficult for robots to achieve both stability and speed in the delivery process,and high energy consumption makes robots frequently charge,reducing operational efficiency.Therefore,how to choose cheaper accessories,reduce interference caused by environmental factors such as lighting and color difference transformation,take into account the stability and timeliness of indoor tracking robots,and reduce energy consumption is the focus of this article.The research content of this article is as follows:(1)Design the hardware and software structure of the tracking robot,determine the hardware options,and finally design the software flow for robot control and build an indoor tracking robot platform.(2)A motion trajectory optimization algorithm based on the Frenet coordinate system is proposed.By optimizing the reference path,more secure and reliable path lines can be obtained and the target point can be reached smoothly,ensuring the efficiency and accuracy of planning.At the same time,attention should be paid to lateral and longitudinal acceleration in planning to ensure smoothness and comfort during movement.(3)A motion control method for indoor tracking robots based on visual sensors is proposed.Firstly,image preprocessing is performed through methods such as brightness contrast change,adaptive histogram equalization,and gamma correction to improve image recognition accuracy.At the same time,in order to achieve visual perception and motion control of indoor tracking robots,an End-to-End autonomous driving depth learning algorithm based on front facing cameras is proposed,which achieves autonomous movement of the robot through training decisions,and improves training accuracy through optimizing models during the training process.Finally,hybrid PID control is used to solve the problem of insufficient steering during the robot’s movement,and achieve motion control for the tracking robot.(4)Design experiments to test the optimization results of indoor tracking robots.By adjusting the motor output and comparing the fused Frenet algorithm with the original control algorithm,and using acceleration,speed,and energy consumption tests,the advantages of indoor tracking robots fused in the Frenet coordinate system at different speeds in terms of stability,timeliness,and energy efficiency are explored. |
