S. Ramasamy, K. Eriksson, Saptha Peralippatt, Balasubramanian Perumal, F. Danielsson
{"title":"Optimized Online Path Planning Algorithms Considering Energy","authors":"S. Ramasamy, K. Eriksson, Saptha Peralippatt, Balasubramanian Perumal, F. Danielsson","doi":"10.1109/ETFA45728.2021.9613457","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":312498,"journal":{"name":"2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA )","volume":"46 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA )","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ETFA45728.2021.9613457","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
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.
Plug - and - production演示处理行业中的多个过程,适当的路径规划是必不可少的,同时越来越强调更可持续的过程。为了确保这些过程的可持续性和自动化,需要优化路径规划。我们提出了一种路径规划算法的实现,该算法创建了一个平滑的无碰撞路径并考虑了能量使用。在本文中,我们在模拟环境中演示了PRM(概率路线图)路径规划和基于Dijkstra的优化算法的实现,然后在实际的plug and production演示器中进行了测试。为了验证仿真结果,通过信号分析仪在线测量了实际能量。本文概述的测量结果包括:计算时间,沿路径移动时间,以及不同负载下的能量使用。实验和结果表明,PRM和Dijkstra两种算法的结合可以产生无碰撞的优化路径。这里我们将距离作为Dijkstra优化算法的代价函数,并测量了无碰撞优化路径的能量。这项研究的实际意义是作为任何一种应用的推动者,其中有很大的变化的订单,例如,装配技术在制造业的操作。