| Autonomous following robots have a wide range of applications.For example,real-time tracking of fish makes it possible to study their habits underwater,and tracking also can be used for combat surveillance or for underwater operations to complete dangerous work.On land,combining following robots with luggage can help users to carry heavy luggage while freeing their hands and combining following robots with traditional industrial robots can improve farmers' work efficiency greatly when they engage in planting,watering,or other work.Tracking technology has a variety of means,such as radio frequency,laser radar,and machine vision.They all have flaws;however,we choose visual tracking tech-nology in comprehensive consideration,because the IEEE/CVF Conference on Com-puter Vision and Pattern Recognition(CVPR),International Conference on Computer Vision(ICCV),and European Conference on Computer Vision(ECCV),the three top computer vision conferences,promote the development of machine vision effectively.Visual-tracking technology has poor robustness when facing illumination change,fast motion,motion blur,occlusion,etc.It is also a challenge to meet the requirements for accuracy,real-time operation,and robustness of following robots.The design of a following robot for practical applications is proposed.First,the construction of a following robot system based on the investigation of domestic and overseas robot status is necessary.The robot's functional requirements were analyzed,and the key technology of the research was clarified.Then,the overall architecture,including the software platform,hardware platform,and mechanical structure of the robot,was proposed based on requirements.Simultaneously,a Blue-tooth alarm module circuit schematic was designed,and a Bluetooth distance meas-urement experiment was accomplished.Second,the principle and workflow of the kernelized correlation filter algorithm was introduced,and existing problems of the algorithm related to the correlation filter algorithm were analyzed.The following algorithm of blending artificial beacon and histogram of oriented gradients(HOG)features was proposed to solve these problems.The method of artificial color beacon feature extraction was elaborated,and the ex-periment was completed.The effect of HOG features was improved by adjusting the parameters and introducing trilinear interpolation.The proposed adaptive learning factor was used to adjust the template update rate.Then,the kinematics analysis of the robot,which includes kinematics modeling,kinematics constraints of driven wheels,and smoothing of turning,was carried out.According to the kinematics model and proportional-integral-derivative(PID)control algorithm principle,a program was written on the Arduino digital controller to com-plete the PID control experiment.An in situ rotation experiment for the robot was performed.It proved that the velocity fluctuation of the control system is reasonable.Finally,the TurtleBot2 open-source robot was rebuilt,and the following experi-ment was completed.The robustness of the algorithm was confirmed by adding sys-tem interference.The following trajectory of the robot was obtained by using OptiTrack's motion capture system and cubic spline interpolation.This research con-firms the expected effect of the following robot system by calculating the average ve-locities of robot and target. |
