In this paper, two greedy auction-based algorithms are proposed for the allocation of heterogeneous tasks to a heterogeneous fleet of UAVs. The tasks set is composed of parcel delivery tasks and charge tasks, the latter to guarantee service persistency. An optimization problem is solved by each agent to determine its bid for each task. When considering delivery tasks, the bidder aims at minimizing the energy consumption, while the minimization of the flight time is adopted for charge tasks bids. The algorithms include a path planner that computes the minimum risk path for each task-UAV bid exploiting a 2D risk map of the operational area, defined in an urban environment. Each solution approach is implemented by means of two auction strategies: single-item and multiple-item. Considerations about complexity and efficiency of the algorithms are drawn from Monte Carlo simulations.

Auction-based Task Allocation for Safe and Energy Efficient UAS Parcel Transportation / Rinaldi, Marco; Primatesta, Stefano; Guglieri, Giorgio; Rizzo, Alessandro. - In: TRANSPORTATION RESEARCH PROCEDIA. - ISSN 2352-1465. - ELETTRONICO. - 65:(2022), pp. 60-69. ((Intervento presentato al convegno 11th International Conference on Air Transport – INAIR 2022, Returning to the Skies tenutosi a Bratislava (SK) nel 9-10 November 2022 [10.1016/j.trpro.2022.11.008].

Auction-based Task Allocation for Safe and Energy Efficient UAS Parcel Transportation

Rinaldi, Marco;Primatesta, Stefano;Guglieri, Giorgio;Rizzo, Alessandro
2022

Abstract

In this paper, two greedy auction-based algorithms are proposed for the allocation of heterogeneous tasks to a heterogeneous fleet of UAVs. The tasks set is composed of parcel delivery tasks and charge tasks, the latter to guarantee service persistency. An optimization problem is solved by each agent to determine its bid for each task. When considering delivery tasks, the bidder aims at minimizing the energy consumption, while the minimization of the flight time is adopted for charge tasks bids. The algorithms include a path planner that computes the minimum risk path for each task-UAV bid exploiting a 2D risk map of the operational area, defined in an urban environment. Each solution approach is implemented by means of two auction strategies: single-item and multiple-item. Considerations about complexity and efficiency of the algorithms are drawn from Monte Carlo simulations.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2973194