Decentralized Collision Avoidance and Motion Planning for Multi-Robot Deformable Payload Transport Systems

Sirineni, Yahnit; Tallamraju, Rahul; Rawat, Abhay; Karlapalem, Kamalakar

doi:10.1109/ssrr50563.2020.9292636

2020 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR) 2020

DOI: 10.1109/ssrr50563.2020.9292636

|View full text |Cite

Decentralized Collision Avoidance and Motion Planning for Multi-Robot Deformable Payload Transport Systems

Yahnit Sirineni

Rahul Tallamraju

Abhay Rawat

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2021

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Intelligent Multi-Robot System for Collaborative Object Transportation Tasks in Rough Terrains

Choi

Kim

2021

Electronics

View full text Add to dashboard Cite

Human missions on other planets require constructing outposts and infrastructures, and one may need to consider relocating such large objects according to changes in mission spots. A multi-robot system would be a good option for such a transportation task because it can carry massive objects and provide better system reliability and redundancy when compared to a single robot system. This paper proposes an intelligent and decentralized approach for the multi-robot system using a genetic fuzzy system to perform an object transportation mission that not only minimizes the total travel distance of the multi-robot system but also guarantees the stability of the whole system in a rough terrain environment. The proposed fuzzy inference system determines the multi-robot system’s input for transporting an object to a target position and is tuned in the training process by a genetic algorithm with an artificially generated structured environment employing multiple scenarios. It validates the optimality of the proposed approach by comparing the training results with the results obtained by solving the formulated optimal control problem subject to path inequality constraints. It highlights the performance of the proposed approach by applying the trained fuzzy inference systems to operate the multi-robot system in unstructured environments.

show abstract

Intelligent Multi-Robot System for Collaborative Object Transportation Tasks in Rough Terrains

Choi

Kim

2021

Electronics

View full text Add to dashboard Cite

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.

Decentralized Collision Avoidance and Motion Planning for Multi-Robot Deformable Payload Transport Systems

Cited by 1 publication

References 11 publications

Intelligent Multi-Robot System for Collaborative Object Transportation Tasks in Rough Terrains

Intelligent Multi-Robot System for Collaborative Object Transportation Tasks in Rough Terrains

Contact Info

Product

Resources

About