The Research On Motion Control Of A Robotic Arm Based On Model Design

Motion control of robotic arms is a crucial area of study in robotics technology.The essence of robotic arm motion control is to solve the forward and inverse dynamics of the robotic arm and trajectory generation/path generation are also important research directions..The forward and inverse kinematics compute the joint angles of the robotic arm to achieve the desired motion,while trajectory planning involves planning the position,velocity,and acceleration of the arm during motion to ensure smoother and more stable movement.This reduces vibrations and impacts,while improving the reliability and efficiency of the robotic arm.In this paper,we focus on the Wobot＿vg6 six-degree-of-freedom robotic arm,and use DH modeling to verify the correctness of the kinematics solutions and trajectory planning.Additionally,we explore the feasibility and effectiveness of improving particle swarm optimization algorithms.The main contents of this paper are as follows:This paper introduces the application of model-based design in the field of robotic arm motion control.By utilizing the spatial pose representation and homogenous transformation matrix principle of robotic arms,an improved D-H parameter modeling method is applied to establish a model of the Wobot＿vg6 robotic arm and validate its forward and inverse kinematics solutions.The results demonstrate that model-based design is a reliable and effective approach for modeling and analyzing robotic arms.This paper proposes a trajectory planning method for robotic arm motion control using the "3-5-3" polynomial interpolation and an improved particle swarm optimization algorithm.The "3-5-3" polynomial interpolation method utilizes a cubic polynomial to interpolate near the interpolation points,ensuring the smoothness and accuracy of the interpolation function around the points.A quintic polynomial is used to interpolate in regions further away from the interpolation points,ensuring the overall fitting accuracy of the interpolation function.This approach avoids overfitting and oscillation while maintaining high fitting accuracy in regions far from the interpolation points,thus avoiding the limitations of cubic and quintic polynomials while retaining their advantages.The improved particle swarm optimization algorithm uses an adaptive learning factor strategy to replace the fixed learning factor in the standard particle swarm algorithm.The adaptive learning factor can dynamically adjust the size of the learning factor based on the current state of the particle swarm and search process,adapting to different search spaces and target functions.This approach prevents the algorithm from getting stuck in local optimal solutions and improves the global search capability and convergence speed.In the simulation experiments of robotic arm trajectory planning,this paper used the aforementioned methods and compared the displacement,angular velocity,and angular acceleration of each joint angle in the robotic arm trajectory optimization process before and after the improvements.The results showed that the improved particle swarm optimization algorithm can quickly reach the global optimal solution.This indicates that by using the improved particle swarm optimization algorithm to optimize the "3-5-3" polynomial interpolation method,it is possible to achieve optimal time trajectory planning and smooth motion control of robotic arms.These findings demonstrate the practical value of this research.