Abstract
Abstract. A method of end-effector trajectory planning in Cartesian space based on multi-objective optimization is proposed in this paper to solve the collision problem during the motion of the redundant manipulator. First, a cosine polynomial function is used to interpolate the trajectory of the end effector, enabling it to reach the desired pose at a specific time. Then, the joint trajectory of the manipulator is solved by inverse kinematics, and the null space term is introduced as the joint limit constraint in the inverse kinematics equation. During the operation of the manipulator, the collision detection algorithm is employed to calculate the distance between the obstacle and each arm in real time. Finally, a multi-objective, multi-optimization model of trajectory that considers the obstacle avoidance, joint velocity, joint jerk and energy consumption is established and optimized with a multi-objective particle swarm optimization algorithm. The simulation results demonstrate that the proposed method can effectively accomplish the trajectory planning task and avoid obstacles; the joint trajectories obtained are smooth and meet the limit constraints; the joint jerk and energy consumption are well suppressed.
Highlights
In many of today’s vastly complex and difficult manufacturing applications, manipulators may have more degrees of freedom (DOFs) than essential due to the need of executing intended complicated jobs like human arms
The end effector of redundant manipulator should satisfy a desired trajectory while its body avoids colliding with obstacles in the environment, contributing to finishing the job
To solve the trajectory planning problem considering obstacle avoidance, many researchers have focused on different methods
Summary
In many of today’s vastly complex and difficult manufacturing applications, manipulators may have more degrees of freedom (DOFs) than essential due to the need of executing intended complicated jobs like human arms. Han et al (2013) realized the obstacle avoidance path planning optimization of the robot arm in the environment with single or multiple obstacles based on genetic algorithms with execution time, space distance, and trajectory length as the optimization parameters. Chen and Zhang (2017) proposed a hybrid multi-objective scheme including the specified primary task for the end effector, obstacle avoidance, joint-physical limits avoidance, and repetitive motion of redundant robot manipulators and transformed it into a dynamic quadratic program (DQP) problem. In this paper, regarding obstacle avoidance, the change rate of joint torque, joint angular velocity, and energy consumption are minimized using the path planning method based on a multi-objective optimization algorithm.
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