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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Akash_et_al_2024_Optimal_Collaborative_Transportation_for_Under-Capacitated_Vehicle_Routing_Problems_using_Aerial_Drone_Swarms.pdf Copyright Information: In Copyright. Download (3MB) |
Item Type: | Conference or Workshop Item |
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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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