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Optimal Collaborative Transportation for Under-Capacitated Vehicle Routing Problems using Aerial Drone Swarms

Sreedhara, Akash Kopparam ; Padala, Deepesh ; Mahesh, Shashank ; Cui, Kai ; Li, Mengguang ; Koeppl, Heinz (2024)
Optimal Collaborative Transportation for Under-Capacitated Vehicle Routing Problems using Aerial Drone Swarms.
2024 IEEE International Conference on Robotics and Automation (ICRA). Yokohama, Japan (13.05.2024-17.05.2024)
doi: 10.26083/tuprints-00028686
Conference or Workshop Item, Secondary publication, Postprint

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Item Type: Conference or Workshop Item
Type of entry: Secondary publication
Title: Optimal Collaborative Transportation for Under-Capacitated Vehicle Routing Problems using Aerial Drone Swarms
Language: English
Date: 16 December 2024
Place of Publication: Darmstadt
Year of primary publication: 2024
Place of primary publication: Washington, DC.
Publisher: IEEE
Book Title: 2024 IEEE International Conference on Robotics and Automation (ICRA)
Event Title: 2024 IEEE International Conference on Robotics and Automation (ICRA)
Event Location: Yokohama, Japan
Event Dates: 13.05.2024-17.05.2024
DOI: 10.26083/tuprints-00028686
Corresponding Links:
Origin: Secondary publication service
Abstract:

Swarms of aerial drones have recently been considered for last-mile deliveries in urban logistics or automated construction. At the same time, collaborative transportation of payloads by multiple drones is another important area of recent research. However, efficient coordination algorithms for collaborative transportation of many payloads by many drones remain to be considered. In this work, we formulate the collaborative transportation of payloads by a swarm of drones as a novel, under-capacitated generalization of vehicle routing problems (VRP), which may also be of separate interest. In contrast to standard VRP and capacitated VRP, we must additionally consider waiting times for payloads lifted cooperatively by multiple drones, and the corresponding coordination. Algorithmically, we provide a solution encoding that avoids deadlocks and formulate an appropriate alternating minimization scheme to solve the problem. On the hardware side, we integrate our algorithms with collision avoidance and drone controllers. The approach and the impact of the system integration are successfully verified empirically, both on a swarm of real nano-quadcopters and for large swarms in simulation. Overall, we provide a framework for collaborative transportation with aerial drone swarms, that uses only as many drones as necessary for the transportation of any single payload.

Status: Postprint
URN: urn:nbn:de:tuda-tuprints-286868
Classification DDC: 300 Social sciences > 380 Commerce, communications, transportation
600 Technology, medicine, applied sciences > 621.3 Electrical engineering, electronics
Divisions: 18 Department of Electrical Engineering and Information Technology > Institute for Telecommunications > Bioinspired Communication Systems
18 Department of Electrical Engineering and Information Technology > Self-Organizing Systems Lab
Date Deposited: 16 Dec 2024 14:00
Last Modified: 16 Dec 2024 14:00
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/28686
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