Online path planning for unmanned aerial vehicles to maximize instantaneous information

Ergezer H., LEBLEBİCİOĞLU M. K.

INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS, cilt.18, sa.3, 2021 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 18 Sayı: 3
  • Basım Tarihi: 2021
  • Doi Numarası: 10.1177/17298814211010379
  • Dergi Adı: INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Applied Science & Technology Source, Communication Abstracts, Compendex, INSPEC, Directory of Open Access Journals
  • Anahtar Kelimeler: Path planning, UAV, assignment problem, optimization, HUNGARIAN METHOD, MULTIPLE UAVS
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

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.