Research On Path Planning And Tracking Method Of Mobile Robot For Oilfield Inspection

In recent years,science and technology are constantly developing,and the control technology of mobile inspection robot is also making rapid progress.Advanced robots have gradually replaced manual operations.However,at this stage,all kinds of stations in the oilfield still mainly rely on manual inspection,which has the problem of poor inspection accuracy,and there are safety hazards in the inspection under bad weather.Mobile robot path planning and path tracking are the key technologies in the process of robot autonomous inspection.Whether the motion path is smooth or not affects the motion effect of the robot.It has received extensive attention due to the complex and changeable working environment of the oilfield,and has more stringent requirements for the motion control and stability of the mobile robot.Therefore,how to improve the mobile robot in the face of complex and changeable environment can still produce feasible path to meet the requirements of vehicle kinematics and can realize the inspection path smooth tracking has become the focus of attention of scholars.Firstly,when the mobile robot calculates the path under different conditions,the conventional path planning methods have the problems of too many inflection points,uneven path,slow convergence speed,easy to fall into local optimum and long search time.This paper presents a path planning method based on Spherical Vector Particle Swarm Optimization(SPSO).By formulating a cost method,the path planning problem is transformed into an optimization problem,which covers the feasibility of mobile robot driving,as well as the conditions and limitations of safety.The SPSO algorithm is used to effectively search the configuration space of the robot through the correspondence between the particle position and the speed,turning angle and climbing angle of the robot,so as to find the optimal path that meets the minimum cost function.The simulation results of path planning in typical scenarios show that compared with the traditional particle swarm path planning algorithm,the path planning algorithm designed in this paper has the characteristics of fewer path inflection points,smooth path curve,fast convergence speed,easy to fall into local optimum and long search time.The problem has been improved.Secondly,for the problem that the path tracking in the mobile robot system has only manual adjustment parameters and no autonomous adjustment ability,a path tracking control algorithm based on adaptive robot particle swarm optimization(AERPSO)to optimize linear quadratic regulator(LQR)is proposed.This paper also develops a path tracking controller based on the tracked mobile robot,and derives a kinematic model with the difference of motor speed on both sides of the tracked robot system as the main control variable,so as to determine the error state equation of the path tracking system and design the LQR controller of the path tracking.In the design of the path tracking control algorithm,the adaptive robot particle swarm optimization design method is used to optimize the parameters of the LQR controller,and the path tracking control algorithm based on AERPSO-LQR is designed.The performance of the traditional LQR control algorithm and the control algorithm based on AERPSO-LQR is compared by simulation software.It is proved that the path tracking algorithm based on AERPSO-LQR is obviously superior to the traditional LQR control algorithm in path tracking accuracy,and improves the safety of the tracked mobile robot in the path tracking process.Finally,in order to verify the SPSO algorithm path planning algorithm and the AERPSO-LQR path tracking control algorithm designed in this paper,the raspberry crawler mobile robot is used as the system algorithm verification platform,and the real vehicle test and algorithm verification in various scenarios are completed.Through the analysis of experimental tests and data results,the method has higher tracking accuracy,and has the adaptability to complex linear and independent optimization ability,and verifies the effectiveness and practicability of the proposed algorithm.