RTAW: An Attention Inspired Reinforcement Learning Method for Multi-Robot Task Allocation in Warehouse Environments
Author:
Affiliation:
1. University of Maryland,The Department of Computer Science,College Park,MD,USA
Funder
ARO
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10160211/10160212/10161310.pdf?arnumber=10161310
Reference36 articles.
1. A Comparative Study between Optimization and Market-Based Approaches to Multi-Robot Task Allocation
2. Rapid Randomized Restarts for Multi-Agent Path Finding Solvers
3. Multi-Objective Optimization for Order Assignment in Food Delivery Industry with Human Factor Considerations
4. Searching with Consistent Prioritization for Multi-Agent Path Finding
5. Complex Task Allocation in Mobile Surveillance Systems
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1. Dynamic Coalition Formation and Routing for Multirobot Task Allocation via Reinforcement Learning;2024 IEEE International Conference on Robotics and Automation (ICRA);2024-05-13
2. An efficient two-stage evolutionary algorithm for multi-robot task allocation in nuclear accident rescue scenario;Applied Soft Computing;2024-02
3. Multi-robot Task Allocation in E-Commerce Warehouses: A Comparative Analysis of Distance Minimization and Priority-Based Approaches;Lecture Notes in Mechanical Engineering;2024
4. Simulation Analysis of a Reinforcement-Learning-Based Warehouse Dispatching Method Considering due Date and Travel Distance;2023 Winter Simulation Conference (WSC);2023-12-10
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