Abstract
Aiming at the characteristics of high efficiency and smoothness in the motion process of collaborative robot, a multi-objective trajectory planning method is proposed. Firstly, the kinematics model of the collaborative robot is established, and the trajectory in the workspace is converted into joint space trajectory using inverse kinematics method. Secondly, seven-order B-spline functions are used to construct joint trajectory sequences to ensure the continuous position, velocity, acceleration and jerk of each joint. Then, the trajectory competitive multi-objective particle swarm optimization (TCMOPSO) algorithm is proposed to search the Pareto optimal solutions set of the robot’s time-energy-jerk optimal trajectory. Further, the normalized weight function is proposed to select the appropriate solution. Finally, the algorithm simulation experiment is completed in MATLAB, and the robot control experiment is completed using the Robot Operating System (ROS). The experimental results show that the method can achieve effective multi-objective optimization, the appropriate optimal trajectory can be obtained according to the actual requirements, and the collaborative robot is actually operating well.
Highlights
Industrial robots have been used widely in industrial production fields
The experimental results show that the method can achieve effective multi-objective optimization, the appropriate optimal trajectory can be obtained according to the actual requirements, and the collaborative robot is operating well
In order to verify the effectiveness of the proposed multi-objective trajectory planning method, the collaborative robot AUBO-I5 is used for the experiment
Summary
Industrial robots have been used widely in industrial production fields. with the development of society, traditional industrial robots have been unable to meet people’s needs for safe collaboration and flexible deployment. Alessandro et al [7] proposed a trajectory planning method of cubic spline curve This algorithm has fewer constraints and a faster calculation speed, but the acceleration curve has jitter, resulting in greater wear of the robot. Kong et al [8] proposed a cubic b-spline trajectory interpolation method, which obtained a relatively smooth trajectory curve, but could not independently specify the initial and final values of acceleration and jerk. Gasparetto et al [13] transformed the time-jerk objective into a single objective using weight coefficients, and used sequence quadratic programming (SQP) to achieve trajectory optimization This method is difficult to distribute weights reasonably, the diversity of solutions is insufficient, and it may fall into a local optimal solution. We selected three crucial objectives in the operation of the robot, and used the improved multi-objective optimization algorithm proposed in this paper to obtain the Pareto optimal solution set.
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