Abstract
This work is aimed to demonstrate a multi-objective joint trajectory generation algorithm for a 7 degree of freedom (DoF) robotic manipulator using swarm intelligence (SI)—product of exponentials (PoE) combination. Given a priori knowledge of the end-effector Cartesian trajectory and obstacles in the workspace, the inverse kinematics problem is tackled by SI-PoE subject to multiple constraints. The algorithm is designed to satisfy finite jerk constraint on end-effector, avoid obstacles, and minimize control effort while tracking the Cartesian trajectory. The SI-PoE algorithm is compared with conventional inverse kinematics algorithms and standard particle swarm optimization (PSO). The joint trajectories produced by SI-PoE are experimentally tested on Sawyer 7 DoF robotic arm, and the resulting torque trajectories are compared.
Highlights
Intelligence Trajectory Generation for Trajectory generation and motion planning is an important part of robot control, which most often is carried out with end-effector’s position and orientation in mind
In cases where there is no such solution, the process of obtaining joint trajectories or inverse kinematics (IK) becomes a challenging task, especially in the presence of obstacles or when the effort minimization is of importance as well
The inverse kinematics (IK) problem has been a hot topic in robotics field for a long time, and many different approaches were demonstrated to generate joint trajectories that satisfy a specific end-effector Cartesian trajectory
Summary
Intelligence Trajectory Generation for Trajectory generation and motion planning is an important part of robot control, which most often is carried out with end-effector’s position and orientation in mind. This is not problematic when the closed-form analytical solution is available. In cases where there is no such solution, the process of obtaining joint trajectories or inverse kinematics (IK) becomes a challenging task, especially in the presence of obstacles or when the effort minimization is of importance as well. The inverse kinematics (IK) problem has been a hot topic in robotics field for a long time, and many different approaches were demonstrated to generate joint trajectories that satisfy a specific end-effector Cartesian trajectory. Opting for a machine learning (ML), artificial neural networks (ANNs), or SI algorithms to handle such highly non-linear problem looks very attractive, which is evident by the recent interest in using SI/PSO algorithms to tackle the IK problem
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