Energy-Conscientious Trajectory Planning for an Autonomous Mobile Robot in an Asymmetric Task Space
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Energy-Conscientious Trajectory Planning for an Autonomous Mobile Robot in an Asymmetric Task Space Soovadeep Bakshi1
· Tianheng Feng2 · Zeyu Yan1 · Zheren Ma3 · Dongmei Chen1
Received: 31 December 2019 / Accepted: 16 November 2020 © Springer Nature B.V. 2020
Abstract Autonomous Mobile Robots (AMRs) have become extremely popular in the manufacturing domain, especially for processes involving large factory floors where these robots are used for transporting materials from one location to another. In an environment where there are multiple prioritized tasks to be completed by a school of AMRs, the overall planning problem can be broken down into three sequential steps: task allocation for the school of AMRs, task scheduling for each AMR, and trajectory planning for each individual AMR. This paper focuses on the trajectory generation procedure for each AMR. Unlike traditional approaches that only consider the location an AMR has to travel to during path planning, here, energy efficiency of the AMR is also considered. We present the physics-based model of the AMR as well as an optimal control formulation for energy-conscientious trajectory generation for the AMR. Methods to numerically solve this problem are discussed, and results are presented for each proposed algorithm on approximately 100 test cases, comparing both performance and computational efficiency. The results show that the presented energy-conscientious methods perform better in terms of energy usage (5-10%) compared to commonly-used shortest path techniques while maintaining similar computational and operational efficiency. Keywords Real-time task space planning · Automated manufacturing · Energy-based optimal control · Autonomous mobile robots
1 Introduction With the advent of advanced algorithms and techniques in robotics, the use of Autonomous Mobile Robots (AMRs) for automated manufacturing has garnered a lot of attention, both in industry and academia. Integrating AMRs into the manufacturing has allowed numerous tasks to become automated, and therefore, more efficient. In this research, we focus on the planning of a class of AMRs used to transport raw materials or tools from one location to another on the factory floor or in a large storage facility. Most of these AMRs are battery-powered. For a fleet of electric robots/vehicles, energy-based planning methods are important for efficient operation. An optimized energy conscientious planning technique could reduce the total energy consumption by about 5 − 15% [29]. Soovadeep Bakshi
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The on-demand planning of a school of AMRs can be defined as the following problem. Given a set of tasks that have to be completed by a school of AMRs, where each task has a start point s, an end point e and a priority rank p, the overall planning problem involves finding an optimal allocation of tasks for the AMRs, determining the optimal task schedule for each AMR while considering task priorities, and designing ener
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