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Optimized Path Planning by Adaptive RRT* Algorithm for Constrained Environments Considering Energy
University West, Department of Engineering Science, Division of Production Systems. (PTW)ORCID iD: 0000-0002-4091-7732
University West, Department of Engineering Science, Division of Production Systems. (PTW)ORCID iD: 0000-0001-8962-0924
University West, Department of Engineering Science, Division of Production Systems. (PTW)
University West, Department of Engineering Science, Division of Production Systems. (PTW)
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2021 (English)In: Proceedings 2021: 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), IEEE conference proceedings, 2021, p. 1-8Conference paper, Published paper (Refereed)
Abstract [en]

Optimized path planning of robots are necessary for the industries to thrive towards greater flexibility and sustainability. This paper proposes an implementation of a collision-free path with the shortest distance. The novelty of the work presented is the new ARRT*(Adaptive Rapidly exploring Random Tree Star) algorithm, which is modified from the RRT*(Rapidly exploring Random Tree Star). In a constraint environment, RRT* algorithms tend to fail when searching for suitable collision-free paths. The proposed ARRT* algorithm gives an optimized feasible collision-free paths in a constraint environment. The feasibility to implement RRT* and ARRT* in a Multi Agent System as a path agent for online control of robots is demonstrated. We have created a digital twin simulated environment to find a collision-free path based on these two algorithms. The simulated path is tested in real robots for feasibility and validation purpose. During the real time implementation, we measured the following parameters: the algorithm computation time for generating a collision-free path, move along time of the path in real time, and energy consumed by each path. These parameters were measured for both the RRT* and the ARRT* algorithms and the test results were compared. The test results were showing that ARRT* performs better in a constrained environment. Both algorithms were tested in a Plug and Produce setup and we find that the generated paths for both algorithms are suitable for online path planning applications.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2021. p. 1-8
Keywords [en]
algorithms, robots
National Category
Manufacturing, Surface and Joining Technology
Research subject
Production Technology
Identifiers
URN: urn:nbn:se:hv:diva-18027DOI: 10.1109/etfa45728.2021.9613699Scopus ID: 2-s2.0-85122921776ISBN: 978-1-7281-2989-1 (electronic)OAI: oai:DiVA.org:hv-18027DiVA, id: diva2:1626854
Conference
26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA ) 7-10 sept 2021 Vasteras, Sweden
Funder
Vinnova, SmoothITKnowledge Foundation, PoPCoRN
Note

The work was carried out at the Production Technology Centre at University West, Sweden supported by the Swedish Governmental Agency for Innovation Systems (Vinnova) under the project named SmoothIT and KK Foundation under the project named PoPCoRN. Their support is gratefully acknowledged.

Available from: 2022-01-12 Created: 2022-01-12 Last updated: 2022-05-23Bibliographically approved

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Ramasamy, SudhaEriksson, Kristina M.Danielsson, Fredrik

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