| Full text | |
| Author(s): |
Rinaldi, Marco
;
Wang, Sheng
;
Geronel, Renan Sanches
;
Primatesta, Stefano
Total Authors: 4
|
| Document type: | Journal article |
| Source: | BIG DATA AND COGNITIVE COMPUTING; v. 8, n. 12, p. 23-pg., 2024-12-01. |
| Abstract | |
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. (AU) | |
| FAPESP's process: | 23/07834-6 - Digital Twins: an integrated approach for modeling and controlling Flexible Manipulators |
| Grantee: | Renan Sanches Geronel |
| Support Opportunities: | Scholarships in Brazil - Post-Doctoral |