SIMULATION ANALYSIS OF A DEEP REINFORCEMENT LEARNING APPROACH FOR TASK SELECTION BY AUTONOMOUS MATERIAL HANDLING VEHICLES

Maojia Patrick Li; Prashant Sankaran; Michael E. Kuhl; Amlan Ganguly; Andres Kwasinski; Raymond Ptucha

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SIMULATION ANALYSIS OF A DEEP REINFORCEMENT LEARNING APPROACH FOR TASK SELECTION BY AUTONOMOUS MATERIAL HANDLING VEHICLES

机译：自主材料处理车辆任务选择深增强学习方法的仿真分析

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The use of autonomous vehicles is a growing trend in the material handling and warehousing. Some challenges that face material handling include the navigation within a warehouse, precision localization and movement, and task selection decisions. In this paper, we address the issue of task selection. In particular, we develop a deep reinforcement learning methodology to enable a vehicle to select from among multiple tasks and move to the closest task in the context of material handling in a warehouse. To evaluate the deep reinforcement learning methodology, we conduct a simulation-based experiment to generate scenarios to first train and then test the capabilities of the method. The results of the experiment show that the method performs well under the given conditions.

机译：自主车辆的使用是材料处理和仓储的日益增长的趋势。面部物料处理的一些挑战包括仓库，精确定位和移动的导航以及任务选择决策。在本文中，我们解决了任务选择问题。特别地，我们开发了一个深度加强学习方法，以使车辆能够在多个任务中选择，并在仓库中的材料处理中移动到最近的任务。为了评估深度加强学习方法，我们进行了一种基于模拟的实验，以产生第一列车的场景，然后测试该方法的能力。实验结果表明该方法在给定的条件下表现良好。

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《Proceedings of the Workshop on Principles of Advanced and Distributed Simulation》 |2018年第cdarom1期|共11页
作者
Maojia Patrick Li; Prashant Sankaran; Michael E. Kuhl; Amlan Ganguly; Andres Kwasinski; Raymond Ptucha;
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作者单位

Industrial and Systems Engineering Department Rochester Institute of Technology;

Industrial and Systems Engineering Department Rochester Institute of Technology;

Industrial and Systems Engineering Department Rochester Institute of Technology;

Computer Engineering Department Rochester Institute of Technology;

Computer Engineering Department Rochester Institute of Technology;

Computer Engineering Department Rochester Institute of Technology;

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正文语种 eng
中图分类计算技术、计算机技术;
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入库时间 2022-08-20 06:37:00

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1. SIMULATION ANALYSIS OF A DEEP REINFORCEMENT LEARNING APPROACH FOR TASK SELECTION BY AUTONOMOUS MATERIAL HANDLING VEHICLES [J] . Maojia Patrick Li, Prashant Sankaran, Michael E. Kuhl, Proceedings of the Workshop on Principles of Advanced and Distributed Simulation . 2018,第CDaROM1期

机译：自主材料处理车辆任务选择深增强学习方法的仿真分析
2. Smart Autonomous Vehicles in High Dimensional Warehouses Using Deep Reinforcement Learning Approach [J] . Mohamed Rhazzaf Engineering Letters . 2021,第1期

机译：利用深增强学习方法在高维仓库中的智能自动车辆
3. Dispatch of autonomous vehicles for taxi services: A deep reinforcement learning approach [J] . Mao Chao, Liu Yulin, Shen Zuo-Jun (max) Transportation research . 2020,第Juna期

机译：为出租车服务派遣自动车辆：深增强学习方法
4. SIMULATION ANALYSIS OF A DEEP REINFORCEMENT LEARNING APPROACH FOR TASK SELECTION BY AUTONOMOUS MATERIAL HANDLING VEHICLES [C] . Maojia Patrick Li, Prashant Sankaran, Michael E. Kuhl, Winter Simulation Conference . 2018

机译：自主材料搬运车辆选择任务的深度强化学习方法的仿真分析
5. Design and Simulation Analysis of Deep Learning Based Approaches and Multi-Attribute Algorithms for Warehouse Task Selection [D] . Sankaran, Prashant. 2020

机译：基于深度学习方法和多属性算法的仓库任务选择的设计与仿真分析
6. Deep Reinforcement Learning Controller for 3D Path Following and Collision Avoidance by Autonomous Underwater Vehicles [O] . Simen Theie Havenstrøm, Adil Rasheed, Omer San 2020

机译：3D路径深度加强学习控制器跟踪和自动水下车辆的避免避免
7. Feature Analysis and Selection for Training an End-to-End Autonomous Vehicle Controller Using the Deep Learning Approach [O] . Yang, Shun, Wang, Wenshuo, Liu, Chang, 2017

机译：端到端自治系统训练的特征分析与选择使用深度学习方法的车辆控制器

SIMULATION ANALYSIS OF A DEEP REINFORCEMENT LEARNING APPROACH FOR TASK SELECTION BY AUTONOMOUS MATERIAL HANDLING VEHICLES

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