Research On Motion Planning Of Mobile Grasping Manipulator Based On Vision Guidance

Robotics has become an essential aspect in advancing the era of intelligence,thanks to the rapid advancement of science and technology.Due to difficulties such as low agility,restricted working space,and difficulty in real-time obstacle avoidance,the standard fixed manipulator cannot match the job demands in an unstructured and unfamiliar environment.Aiming at such problems,this paper conducts an in-depth study on the motion planning of the mobile grasping manipulator based on machine vision.First,taking a small 6-joint manipulator as the research object,the coordinate model of each link of the manipulator is constructed by the D-H method,and the four parameters of the joint angle,torsion angle,link length and offset distance are obtained.Based on the parameters,matrix transformation analysis is used.Methods The forward and inverse kinematics model of the mobile manipulator was established.To verify the accuracy of the model,the Monte Carlo method was used to simulate the maximum operable range of the manipulator.Second,an obstacle avoidance route planning technique for the mobile manipulator is suggested,which is based on the modified RRT algorithm,to overcome the problems of multiple turning points,long search time,lack of guidance,and non-optimal pathways in the path planning process of the mobile manipulator.The new node generation technique is constrained,the obstacle factor is established,and the path’s turning points are smoothed to achieve path trajectory optimization.The algorithm is simulated by Matlab to achieve smooth and stable obstacle avoidance motion,meet the working requirements of the manipulator,and provide a theoretical basis for the dynamic obstacle avoidance of the follow-up mobile manipulator.Then,the trajectory tracking control strategy based on improved particle swarm algorithm is proposed due to the slow tracking speed,low precision and unstable system of the joint trajectory of the manipulator.The dynamic model of the manipulator is constructed by using the basic laws of Lagrange,and the radial basis function neural network is combined with the improved nonlinear jammer,which greatly improves the trajectory tracking efficiency of the manipulator and solves the problem of system jitter.By simulating the first two joints of the manipulator,the stable operation of the manipulator is ensured and the loss of the motor of the manipulator joints is reduced.Finally,an experimental platform for obstacle avoidance and grasping is built,and the camera parameters are calibrated by Zhang Zhengyou’s calibration method,and the internal and external parameters of the camera are optimized.The algorithm studied in this paper is verified by the obstacle avoidance grasping experiment in different scenarios,which enables the robotic arm to find the optimal path faster and more efficiently and achieve barrier-free grasping.