Multi-Objective Swarm Intelligence Trajectory Generation for a 7 Degree of Freedom Robotic Manipulator

• Published in: Robotics (Basel) Vol. 10; no. 4; p. 127
• Main Authors: Malik, Aryslan, Henderson, Troy, Prazenica, Richard
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.12.2021
• Subjects: Algorithms; Cartesian coordinates; Degrees of freedom; End effectors; Inverse kinematics; Kinematics; machine learning; Manipulators; Methods; Multiple objective analysis; Obstacle avoidance; Particle swarm optimization; PoE; Robot arms; robot-manipulation; Robotics; Robots; swarm; Swarm intelligence; Tracking control; trajectory generation
• ISSN: 2218-6581; 2218-6581
• DOI: 10.3390/robotics10040127

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.