Time-Jerk Optimal Trajectory Planning of Industrial Robot based on Hybrid Particle Swarm Optimization Algorithm

Gao, Ying; Xie, Weinan; Li, Qinghua; Li, Xinnian; Hu, Meiling; Zhao, Lei

doi:10.1109/cac53003.2021.9727456

2021 China Automation Congress (CAC) 2021

DOI: 10.1109/cac53003.2021.9727456

|View full text |Cite

Time-Jerk Optimal Trajectory Planning of Industrial Robot based on Hybrid Particle Swarm Optimization Algorithm

Ying Gao

Weinan Xie

Qinghua Li

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2023

2024

Publication Types

Select...

Article2

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

(1 citation statement)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The particle swarm optimization (PSO) approach has been employed in the navigation of various types of robots, including underwater robots, aerial robots in 3D unknown environments [98], humanoid robots [99], and industrial robots [100]. Notably, these robots have demonstrated successful navigation outcomes.…”

mentioning

confidence: 99%

Classical and Heuristic Approaches for Mobile Robot Path Planning: A Survey

Abdulsaheb

Kadhim

2023

Robotics

View full text Add to dashboard Cite

The most important research area in robotics is navigation algorithms. Robot path planning (RPP) is the process of choosing the best route for a mobile robot to take before it moves. Finding an ideal or nearly ideal path is referred to as “path planning optimization.” Finding the best solution values that satisfy a single or a number of objectives, such as the shortest, smoothest, and safest path, is the goal. The objective of this study is to present an overview of navigation strategies for mobile robots that utilize three classical approaches, namely: the roadmap approach (RM), cell decomposition (CD), and artificial potential fields (APF), in addition to eleven heuristic approaches, including the genetic algorithm (GA), ant colony optimization (ACO), artificial bee colony (ABC), gray wolf optimization (GWO), shuffled frog-leaping algorithm (SFLA), whale optimization algorithm (WOA), bacterial foraging optimization (BFO), firefly (FF) algorithm, cuckoo search (CS), and bat algorithm (BA), which may be used in various environmental situations. Multiple issues, including dynamic goals, static and dynamic environments, multiple robots, real-time simulation, kinematic analysis, and hybrid algorithms, are addressed in a different set of articles presented in this study. A discussion, as well as thorough tables and charts, will be presented at the end of this work to help readers understand what types of strategies for path planning are developed for use in a wide range of ecological contexts. Therefore, this work’s main contribution is that it provides a broad view of robot path planning, which will make it easier for scientists to study the topic in the near future.

show abstract

mentioning

confidence: 99%

Classical and Heuristic Approaches for Mobile Robot Path Planning: A Survey

Abdulsaheb

Kadhim

2023

Robotics

View full text Add to dashboard Cite

show abstract

A Novel Multi-Objective Trajectory Planning Method for Robots Based on the Multi-Objective Particle Swarm Optimization Algorithm

Wang,

Zhang,

Zhu

et al. 2024

Sensors

View full text Add to dashboard Cite

The three performance indexes of the space robot, travel time, energy consumption, and smoothness, are the key to its important role in space exploration. Therefore, this paper proposes a multi-objective trajectory planning method for robots. Firstly, the kinematics and dynamics of the Puma560 robot are analyzed to lay the foundation for trajectory planning. Secondly, the joint space trajectory of the robot is constructed with fifth-order B-spline functions, realizing the continuous position, velocity, acceleration, and jerk of each joint. Then, the improved multi-objective particle swarm optimization (MOPSO) algorithm is used to optimize the trajectory, and the distribution uniformity, convergence, and diversity of the obtained Pareto front are good. The improved MOPSO algorithm can realize the optimization between multiple objectives and obtain the trajectory that meets the actual engineering requirements. Finally, this paper implements the visualization of the robot’s joints moving according to the optimal trajectory.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Time-Jerk Optimal Trajectory Planning of Industrial Robot based on Hybrid Particle Swarm Optimization Algorithm

Cited by 2 publications

References 5 publications

Classical and Heuristic Approaches for Mobile Robot Path Planning: A Survey

Classical and Heuristic Approaches for Mobile Robot Path Planning: A Survey

A Novel Multi-Objective Trajectory Planning Method for Robots Based on the Multi-Objective Particle Swarm Optimization Algorithm

Contact Info

Product

Resources

About