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

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 th...

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Published inRobotics (Basel) Vol. 10; no. 4; p. 127
Main Authors Malik, Aryslan, Henderson, Troy, Prazenica, Richard
Format Journal Article
LanguageEnglish
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
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ISSN2218-6581
2218-6581
DOI10.3390/robotics10040127

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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.
AbstractList 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.
Author Prazenica, Richard
Malik, Aryslan
Henderson, Troy
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  surname: Henderson
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  surname: Prazenica
  fullname: Prazenica, Richard
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CitedBy_id crossref_primary_10_1016_j_fusengdes_2023_114145
crossref_primary_10_3390_app12199512
crossref_primary_10_3390_pr10040640
crossref_primary_10_3390_robotics11020044
crossref_primary_10_3390_a16080393
crossref_primary_10_1155_2022_6202237
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ContentType Journal Article
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Snippet This work is aimed to demonstrate a multi-objective joint trajectory generation algorithm for a 7 degree of freedom (DoF) robotic manipulator using swarm...
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StartPage 127
SubjectTerms 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
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Title Multi-Objective Swarm Intelligence Trajectory Generation for a 7 Degree of Freedom Robotic Manipulator
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