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

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

doi:10.1016/j.trpro.2022.11.008

Cited by 5 publications

(1 citation statement)

References 19 publications

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“…The proposed approach is particularly significant in civilian applications where a UAV has to perform a task at a specific location in populated environments. Tasks may include parcel delivery [34], firefighting, picking or placing objects, acquiring specific measurements, rescue operations, or surveillance. Safely navigating to the task location is key to the success of the mission and can be autonomously achieved using the proposed approach.…”

Section: B Paper Contributionsmentioning

confidence: 99%

Learning-Based Navigation and Collision Avoidance Through Reinforcement for UAVs

Azzam

Chehadeh

Hay

et al. 2024

IEEE Trans. Aerosp. Electron. Syst.

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Reinforcement learning (RL) has been proven to enable the automation of tasks involving complex sequential decision-making. The simulation to reality (sim2real) gap, however, poses a major challenge in most engineering applications. In this work, we propose a learning approach combining RL based navigation and collision avoidance scheme with low-level advanced control to bridge the sim2real gap for unmanned aerial vehicle (UAV) applications. The proposed approach puts the RL agent at the top of the control hierarchy to focus on behavioral intelligence. We demonstrate the transferability of the RL policy trained in simulation to a real UAV without randomization of the system's dynamic parameters. The direct transfer is enabled by (1) the use of deep neural networks with the modified relay feedback test (DNN-MRFT) to identify the parameters of the UAV, and (2) formulating a reward function to penalize excessive actor actions. Particularly, the RL agent generates high-level velocity actions to achieve the sought task, while the low-level controller minimizes any unwanted disturbances and model discrepancies. The proposed approach has been tested and validated using computer simulations and real-world experiments. The real-world experimental results demonstrated the agent's capability to achieve the navigation task with a 90% success rate. The experimental results can be found in this video: https://youtu.be/I1BF4mhJLLs.

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Section: B Paper Contributionsmentioning

confidence: 99%

Learning-Based Navigation and Collision Avoidance Through Reinforcement for UAVs

Azzam

Chehadeh

Hay

et al. 2024

IEEE Trans. Aerosp. Electron. Syst.

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Multi-Auctioneer Market-based Task Scheduling for Persistent Drone Delivery

Rinaldi

Primatesta

Guglieri

et al. 2023

2023 International Conference on Unmanned Aircraft Systems (ICUAS)

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Application of Task Allocation Algorithms in Multi-UAV Intelligent Transportation Systems: A Critical Review

Rinaldi,

Wang,

Geronel

et al. 2024

BDCC

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Unmanned aerial vehicles (UAVs), commonly known as drones, are being seen as the most promising type of autonomous vehicles in the context of intelligent transportation system (ITS) technology. A key enabling factor for the current development of ITS technology based on autonomous vehicles is the task allocation architecture. This approach allows tasks to be efficiently assigned to robots of a multi-agent system, taking into account both the robots’ capabilities and service requirements. Consequently, this study provides an overview of the application of drones in ITSs, focusing on the applications of task allocation algorithms for UAV networks. Currently, there are different types of algorithms that are employed for task allocation in drone-based intelligent transportation systems, including market-based approaches, game-theory-based algorithms, optimization-based algorithms, machine learning techniques, and other hybrid methodologies. This paper offers a comprehensive literature review of how such approaches are being utilized to optimize the allocation of tasks in UAV-based ITSs. The main characteristics, constraints, and limitations are detailed to highlight their advantages, current achievements, and applicability to different types of UAV-based ITSs. Current research trends in this field as well as gaps in the literature are also thoughtfully discussed.

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

Cited by 5 publications

References 19 publications

Learning-Based Navigation and Collision Avoidance Through Reinforcement for UAVs

Learning-Based Navigation and Collision Avoidance Through Reinforcement for UAVs

Multi-Auctioneer Market-based Task Scheduling for Persistent Drone Delivery

Application of Task Allocation Algorithms in Multi-UAV Intelligent Transportation Systems: A Critical Review

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