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Fuzzy-based Three-dimensional Resolution Algorithm for Collision Avoidance of Fixed-wing UAVs Optimized using Genetic Algorithm.

Rauniyar, Shyam
http://orcid.org/0000-0001-5805-6267
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1703173752503219

Abstract Details

2023, MS, University of Cincinnati, Engineering and Applied Science: Aerospace Engineering.
Fixed-wing Unmanned Aerial Vehicles (UAVs) cannot fly at speeds lower than critical stall speeds. As a result, hovering during a potential collision scenario, like with rotary-wing UAVs, is impossible. Moreover, hovering is not an optimal solution for Collision Avoidance (CA), as it increases mission time and is innately fuel inefficient. This work proposes a decentralized Fuzzy Inference System (FIS)-based resolution algorithm that modulates the point-to-point mission path while ensuring the continuous motion of UAVs during CA. A simplified kinematic guidance model with coordinated turn conditions is considered to control the UAVs. The model employs a proportional-derivative control of commanded airspeed, bank angle, and flight path angle. The commands are derived from the desired path, characterized by airspeed, heading, and altitude. The desired path is, in turn, obtained using look-ahead points generated for the target point. The FIS aims to mimic human behavior during collision scenarios, generating modulation parameters for the desired path to achieve CA. Notably, it is also scalable, which makes it easy to adjust the algorithm parameters, as per the required missions, and factors specific to a given UAV. A genetic algorithm was used to optimize FIS parameters so that the distance traveled during the mission was minimized despite path modulation. The proposed algorithm was optimized using a pairwise conflict scenario. The effectiveness of the algorithm was evaluated through various pairwise conflict scenarios as well as a Monte Carlo simulation of random conflict scenarios involving multiple UAVs operating in a confined space. It was found that the overall number of collisions decreased by an average of 98% using the proposed optimized algorithm, thereby, supporting its effectiveness.
Donghoon Kim, Ph.D. (Committee Chair)
Daegyun Choi, Ph.D. (Committee Member)
Anoop Sathyan, Ph.D. (Committee Member)
Ou Ma, Ph.D. (Committee Member)
Kelly Cohen, Ph.D. (Committee Member)
100 p.
Aerospace Engineering
collision avoidance; fixed-wing; UAVs; fuzzy logic; genetic algorithm

Recommended Citations

Citations

  • Rauniyar, S. (2023). Fuzzy-based Three-dimensional Resolution Algorithm for Collision Avoidance of Fixed-wing UAVs Optimized using Genetic Algorithm. [Master's thesis, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1703173752503219

    APA Style (7th edition)

  • Rauniyar, Shyam. Fuzzy-based Three-dimensional Resolution Algorithm for Collision Avoidance of Fixed-wing UAVs Optimized using Genetic Algorithm. 2023. University of Cincinnati, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1703173752503219.

    MLA Style (8th edition)

  • Rauniyar, Shyam. "Fuzzy-based Three-dimensional Resolution Algorithm for Collision Avoidance of Fixed-wing UAVs Optimized using Genetic Algorithm." Master's thesis, University of Cincinnati, 2023. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1703173752503219

    Chicago Manual of Style (17th edition)

ucin1703173752503219
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This open access ETD is published by University of Cincinnati and OhioLINK.