| Full text | |
| Author(s): |
de Oliveira, Fabiola M. C.
;
Bittencourt, Luiz F.
;
Bianchi, Reinaldo A. C.
;
Kamienski, Carlos A.
Total Authors: 4
|
| Document type: | Journal article |
| Source: | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS; v. 25, n. 5, p. 18-pg., 2023-11-14. |
| Abstract | |
As delivery companies continue to explore the use of drones, the need for efficient and safe operation in urban environments becomes increasingly critical. Market-wide versions of drone delivery services will necessarily spread many drones, especially in big cities. In this scenario, avoiding collisions with other drones or typical obstacles in urban spaces is fundamental. This paper proposes and evaluates, via simulation and analytical modeling, an aerial delivery service scenario and three autonomous geometric approaches for collision avoidance. We compare our approaches with three simple methods - DoNothing (not detouring), Random, and aviation-like Rightward - and two state-of-the-art geometric approaches. Simulation experiments consider different fleet sizes with constant and Poisson drone arrival rates and drones randomly choosing one of different altitudes for the cruise flight. Contrary to our expectations, the Random and Rightward approaches increase the collisions compared with DoNothing, making the latter our baseline. Our approaches significantly reduce collisions in all experiments and deal with more drones within the detection radius, showing that collisions are more complex to avoid. Comparing the collision rate, successful trips, and the number of flying drones reveals that the efficiency in avoiding collisions reduces the number of successful trips by increasing the number of active drones. Regardless of the expected reduction in collisions, more altitudes do not eliminate them. These results indicate the need for more sophisticated approaches to reduce or eliminate collisions. The analytical modeling using Markov Chains corroborates the simulation results by shedding some light on and helping explain the simulation results. (AU) | |
| FAPESP's process: | 20/14771-2 - Federated deep learning for IoT-enabled smart cities |
| Grantee: | Fabíola Martins Campos de Oliveira Genari |
| Support Opportunities: | Scholarships in Brazil - Technical Training Program - Technical Training |
| FAPESP's process: | 15/24485-9 - Future internet for smart cities |
| Grantee: | Fabio Kon |
| Support Opportunities: | Research Projects - Thematic Grants |
| FAPESP's process: | 14/50937-1 - INCT 2014: on the Internet of the Future |
| Grantee: | Fabio Kon |
| Support Opportunities: | Research Projects - Thematic Grants |