Optimized Online Path Planning Algorithms Considering EnergyShow others and affiliations
2021 (English)In: Proceedings: 2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA ), IEEE conference proceedings, 2021, p. 1-08Conference paper, Published paper (Refereed)
Abstract [en]
Plug and produce demonstrators handles multiple processes in the industry, appropriate path planning is essential and at the same time there is an increasing emphasis on more sustainable processes. To ensure the sustainability and automate these processes optimized path planning is required. We present an implementation of a path planning algorithm, which creates a smooth collision free path and considers energy use. In the paper, we demonstrated the implementation of PRM (Probabilistic Road Map) path planning and Dijkstra based optimization algorithm in a simulation environment and thereafter test in a real plug and produce demonstrator. To validate the simulated results the real energy was measured through the signal analyzer online. The measured results outlined in this paper includes; computational time, move along path time, and energy use with different loads. From the experiments and results we conclude that the combination of the two algorithms, PRM with Dijkstra, can be used to generate a collision free optimized path. Here we have considered the distance as the cost function for Dijkstra optimization algorithm and measured the energy of the collision free optimized path. The practical implication of this research is as an enabler for any kind of application where there are large variations of orders e.g., kitting techniques in assembly operations for manufacturing industry.
Place, publisher, year, edition, pages
IEEE conference proceedings, 2021. p. 1-08
Keywords [en]
Optimized Path Planning, PRM algorithm, Dijkstra algorithm, Energy, Plug and Produce, Collision-free path.
National Category
Production Engineering, Human Work Science and Ergonomics Learning
Research subject
Work Integrated Learning; Production Technology
Identifiers
URN: urn:nbn:se:hv:diva-18033DOI: 10.1109/ETFA45728.2021.9613457ISBN: 978-1-7281-2989-1 (electronic)OAI: oai:DiVA.org:hv-18033DiVA, id: diva2:1627573
Conference
2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA )
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.
2022-01-132022-01-132022-05-23Bibliographically approved