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Energy-Conscientious Trajectory Planning for an Autonomous Mobile Robot in an Asymmetric Task Space

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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.

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Authors and Affiliations

  1. Department of Mechanical Engineering, The University of Texas at Austin, Austin, TX, 78712, USA

    Soovadeep Bakshi, Zeyu Yan & Dongmei Chen

  2. Apple Inc., 1 Apple Park Way, Cupertino, CA, 95014, USA

    Tianheng Feng

  3. Citadel Securities, Bank of America Center, 700 Louisiana St. Suite 4600, Houston, TX, 77002, USA

    Zheren Ma

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Appendix: AMR and Problem Parameters

Appendix: AMR and Problem Parameters

The parameters of the AMR model are shown below:

Parameter

Symbol

Value

Wheelbase

L

1.5 m

Vehicle Mass

m

500 kg

Wheel Radius

rw

0.2 m

Motor Constant

Km

1.24 N.m/A

Motor Inertia

Jm

0.2 kg.m2

Motor Resistance

Rm

0.35 Ω

Max. Velocity

\(v_{\max \limits }\)

15 km/hr

Max./Min. SOC

\(SOC_{\min \limits ,\max \limits }\)

[0.8, 0.3]

Min. Turn Radius

\(R_{t,\min \limits }\)

3 m

Max. Voltage Input

\(E_{\max \limits }\)

38 V

Max. Brake Torque

\(\tau _{b,\max \limits }\)

981 N.m

Efficiency

η

0.912

Battery Capacity

Q

25 A.h

For the recharge scheduling test cases as mentioned in Section ??, the same parameters of the vehicle can be used, but with lower battery capacity (2 A.h) in order to simulate faster drop in SOC levels.

The structure of the cost function used in this paper is as shown in Eq. (??), with the weights w1 = 1, w2 = 1 and w3 = 0. There is no weight on the \(\dot {\psi }^{2}\) term as it is desired that the performance comparison is made purely in terms of energy consumption from the battery for analysis.

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Bakshi, S., Feng, T., Yan, Z. et al. Energy-Conscientious Trajectory Planning for an Autonomous Mobile Robot in an Asymmetric Task Space. J Intell Robot Syst 101, 18 (2021). https://doi.org/10.1007/s10846-020-01288-9

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