Research On Obstacle Avoidance Path Planning And Control Of Dual Arm Collaborative Robot

With the continuous development of automation and intelligence of mechanical equipment,various forms of robots play a crucial role in modern industrial production.Dual arm collaborative robots exceed the traditional single arm robot greatly in terms of flexibility,coordinated operation,efficiency,and load,and have gradually developed into the research focus of robotics.The research object of this paper is a Kinova jaco 6-DOF robot,focusing on its motion path planning and control research,the main research contents are as follows:Firstly,the Kinova 6-DOF robot and its basic parameters are briefly introduced,then,the D-H coordinate method is used to build the kinematics model of the robot which is according to the provided size data,and meanwhile,the forward kinematics equation is solved,and the numerical method based on Jacobi iteration is used to obtain inverse kinematics equation of the robot.Furthermore,the solved forward and inverse kinematics is calculated and verified.Finally,the workspace and dynamic equation of the robot are obtained and analyzed by Monte Carlo method and Lagrange mechanics method,which provides theoretical support for the motion and control problem of the robotic manipulator.Secondly,the self-collision avoidance and coordinated operation of dual arm robot are studied.First of all,the artificial potential field method is used to obtain the attractive and repulsive functions of the robot,then the concepts of attractive velocity and repulsive velocity are introduced to convert the attractive and repulsive functions into attractive and repulsive velocity functions.The real-time distance between two robotic manipulators is calculated to ensure that the dual arm robot will not collide in the process of operation.Then,the dual arm coordinated operation of the shaft and hole assembly is analyzed,and the pose constraint relationship in the motion of robotic manipulators is studied,and the motion path of the shaft and hole assembly process of the robot is planned by using the B-spline interpolation method.Then,the motion control of dual arm robot is introduced.According to the characteristics of strong coupling and nonlinearity of robot system,a fuzzy PD iterative learning control method based on self-adjusting factor is proposed by combining with the advantages of fuzzy control and iterative learning control.The system error and error change rate are used to adjust the self-adjusting factor,quantization factor and scale factor in the fuzzy system,so as to update the fuzzy rules and adjust the PD parameters in the iterative learning control in real time.Finally,combined with the motion trajectory data of shaft and hole assembly,the coordinated control experiment is carried out in Simulink to verify the effectiveness of the above method.Finally,the ROS experimental platform of dual arm coordinated operation is introduced.Firstly,basic working principle of MoveIt! is introduced,and then use xacro file to describe the robot physical model and import it into MoveIt!.Furthermore,through the setup assistant to configure MoveIt! and generate the config function package.For the dual arm coordinated operation experiment,the communication between MoveIt! and Kinova robot is established,and the movement of the robot is planned by writing Python file,and two robotic manipulators of robot are driven to complete the shaft and hole assembly cooperation task.In the final,the motion data of the manipulator is recorded by rosbag and the parameter curve is drawn in MATLAB.The feasibility of the research is analyzed by physical experiment.