Online path planning for unmanned aerial vehicles to maximize instantaneous information

Date

2021-01-01

Author

Ergezer, Halit
Leblebicioğlu, Mehmet Kemal

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In this article, an online path planning algorithm for multiple unmanned aerial vehicles (UAVs) has been proposed. The aim is to gather information from target areas (desired regions) while avoiding forbidden regions in a fixed time window starting from the present time. Vehicles should not violate forbidden zones during a mission. Additionally, the significance and reliability of the information collected about a target are assumed to decrease with time. The proposed solution finds each vehicle’s path by solving an optimization problem over a planning horizon while obeying specific rules. The basic structure in our solution is the centralized task assignment problem, and it produces near-optimal solutions. The solution can handle moving, pop-up targets, and UAV loss. It is a complicated optimization problem, and its solution is to be produced in a very short time. To simplify the optimization problem and obtain the solution in nearly real time, we have developed some rules. Among these rules, there is one that involves the kinematic constraints in the construction of paths. There is another which tackles the real-time decision-making problem using heuristics imitating human-like intelligence. Simulations are realized in MATLAB environment. The planning algorithm has been tested on various scenarios, and the results are presented.

URI

https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85107212923&origin=inward
https://hdl.handle.net/11511/91105

Journal

International Journal of Advanced Robotic Systems

DOI

https://doi.org/10.1177/17298814211010379

Collections

Department of Electrical and Electronics Engineering, Article

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Citation Formats

H. Ergezer and M. K. Leblebicioğlu, “Online path planning for unmanned aerial vehicles to maximize instantaneous information,” International Journal of Advanced Robotic Systems, pp. 0–0, 2021, Accessed: 00, 2021. [Online]. Available: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85107212923&origin=inward.