| 1. |
- Olsson, Ella, et al.
(författare)
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Urdarbrunnen: Towards an AI-enabled mission system for Combat Search and Rescue operations
- 2023
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Ingår i: Proceedings of the 35th Annual Workshop of the Swedish Artificial Intelligence Society (SAIS 2023). - : Linköping University Electronic Press. - 9789180752749 ; , s. 38-45
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Konferensbidrag (refereegranskat)abstract
- The Urdarbrunnen project is a Saab-led exploratory initiative that aims to develop an operator-assisted AI-enabled mission system for basic autonomous functions. In its first iteration, presented in this project paper, the system is designed to be capable of performing the search task of a combat search and rescue mission in a complex and dynamic environment, while providing basic human machine interaction support for remote operators. The system enables a team of agents to cooperatively plan and execute a search mission while also interfacing with the WARA-PS core system that allows human operators and other agents to monitor activities and interact with each other. The aim of the project is to develop the system iteratively, with each iteration incorporating feedback from simulations and real-world experiments. In future work, the capability of the system will be extended to incorporate additional tasks for other scenarios, making it a promising starting point for the integration of autonomous capabilities in a future air force.
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| 2. |
- Tiger, Mattias, 1989-, et al.
(författare)
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On-Demand Multi-Agent Basket Picking for Shopping Stores
- 2023
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Ingår i: 2023 IEEE International Conference on Robotics and Automation (ICRA). - : IEEE. - 9798350323658 - 9798350323665 ; , s. 5793-5799
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Konferensbidrag (refereegranskat)abstract
- Imagine placing an online order on your way to the grocery store, then being able to pick the collected basket upon arrival or shortly after. Likewise, imagine placing any online retail order, made ready for pickup in minutes instead of days. In order to realize such a low-latency automatic warehouse logistics system, solvers must be made to be basketaware. That is, it is more important that the full order (the basket) is picked timely and fast, than that any single item in the order is picked quickly. Current state-of-the-art methods are not basket-aware. Nor are they optimized for a positive customer experience, that is; to prioritize customers based on queue place and the difficulty associated with picking their order. An example of the latter is that it is preferable to prioritize a customer ordering a pack of diapers over a customer shopping a larger order, but only as long as the second customer has not already been waiting for too long. In this work we formalize the problem outlined, propose a new method that significantly outperforms the state-of-the-art, and present a new realistic simulated benchmark. The proposed method is demonstrated to work in an on-line and real-time setting, and to solve the on-demand multi-agent basket picking problem for automated shopping stores under realistic conditions.
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