DroneTalk: An Internet-of-Things-Based Drone System for Last-Mile Drone Delivery
Abstract
At present, drone delivery systems usually function only in global positioning system (GPS)-friendly environments and thus cannot deliver goods into customers’ houses, which is an element of urban last-mile delivery. In this study, we investigated solutions for enabling drones to fly autonomously in mixed indoor–outdoor environments. We propose a novel Internet of Things (IoT)-based drone delivery system, namely DroneTalk, for mail delivery. DroneTalk combines a GPS, an inertial measurement unit, and visual information to achieve mixed indoor–outdoor autopilot operation. Furthermore, DroneTalk integrates an autonomous drone control system with an IoT device management platform to enable a drone to achieve automatic online weather awareness. Most importantly, DroneTalk is a novel work considering communication, processing, and control latencies simultaneously during autopilot operation to avoid problems related to drone collisions. These problems have always existed but become more serious when drones are flying close to buildings. Simulation results indicate that the proposed system can attain a high user-defined flight success rate (i.e., 99% in our simulations) without any collisions; thus, the proposed system can be feasibly used in real-world environments.
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When new types of operations and objects are not labeled or known in advance, unsupervised deep learning techniques can be explored to improve object classification and recognition.
“Indeed, as mentioned in [114,157], when new types of operations and objects are not labeled or known in advance, unsupervised deep learning techniques can be explored to improve object classification and recognition.”
From A Survey of Video Surveillance Systems in Smart City (2023) · cites this paper as [157]
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A Survey of Video Surveillance Systems in Smart City
Myagmar-Ochir, Y., & Kim, W. (2023) · Electronics · doi:10.3390/electronics12173567 · Cited by 134 · see citing works
“Indeed, as mentioned in [114,157], when new types of operations and objects are not labeled or known in advance, unsupervised deep learning techniques can be explored to improve object classification and recognition.”
Background
- Authors
- Chen, K.-W., Xie, M.-R., Chen, Y.-M., Chu, T.-T., & Lin, Y.-B.
- Affiliations
- National Yang Ming Chiao Tung University (TW)
- Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
- Venue
- IEEE Transactions on Intelligent Transportation Systems
- Published
- 2022-09
- Volume
- 23
- Issue
- 9
- Pages
- 15204-15217
- ISSN
- 1524-9050 (ISSN-L)
- 1558-0016
- OpenAlex
- W4210290495
- Cited by
- 88
- References
- 30
- Citations by year
- 2026: 11 · 2025: 28 · 2024: 23 · 2023: 23 · 2022: 3
- FWCI
- 8.56
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Intelligent Transportation Systems (publishedVersion)
- Funders
- Ministry of Science and Technology of Taiwan (MOST 110-2628-E-A49-012-, MOST 110-2634-F-009-018-, MOST 110-2634-F-A49-006-, MOST 110-2622-8-A49-005-, MOST 109-2221-E-009-087-MY2)
- Center for Open Intelligent Connectivity from the Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education in Taiwan
- Ministry of Economic Affairs (110-EC-17-A-02-S5-007)
- Topics
- Physical Sciences › Engineering › Aerospace Engineering › UAV Applications and Optimization
- Physical Sciences › Engineering › Building and Construction › Urban and Freight Transport Logistics
- Physical Sciences › Engineering › Aerospace Engineering › Air Traffic Management and Optimization
- Keywords
- drone delivery, last-mile delivery, Internet of Things, autonomous drones, indoor and outdoor navigation, inertial measurement unit, GPS, visual information, autopilot control, IoT device management, collision avoidance, communication delay
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