A Modified Particle Swarm Optimization for Autonomous UAV Path Planning in 3D Environment

Nayeem, Golam Moktader; Fan, Mingyu; Li, Shanjun; Ahammad, Khalil

doi:10.1007/978-3-030-52856-0_14

Cited by 7 publications

(1 citation statement)

References 12 publications

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“…Finding the optimal path is a non-deterministic problem due to the differences in the effects of multiple constraints on the path, Golam Moktader et al [10] proposed an algorithm called dynamic neighbourhood n-PSO, which was simulated in a 3D environment and compared with the widely used PSO, which has more diversity of initial particles and helps to improve the path quality, and presented a path planning algorithm for time cost analysis. Abhishek et al [11] proposed a hybrid algorithm of the harmony search method, PSO and genetic algorithm collection.…”

Section: Introductionmentioning

confidence: 99%

3D path planning problem for fighter aircraft with multiple constraints

Yang,

Xiao,

Chen

et al. 2023

The Journal of Engineering

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Path planning is a crucial component for ensuring the safety and efficiency of flight missions, especially for fighter aircraft. To enhance the combat effectiveness of fighter aircraft, it is important to consider how to avoid danger sources an terrain obstacles, reduce fuel consumption, and utilize the aircraft's own performance to accomplish the mission objectives. In the modern battlefield environment, the shortest path is not the only criterion for planning, but also other factors such as the threat level to the aircraft, fuel consumption, mission completion time, and minimum turning radius. In this paper, the authors propose a multi‐constraint path planning method for fighter aircraft that incorporates these factors into an improved particle swarm algorithm. The authors transform the constraints of three‐dimensional terrain, threat source, fuel consumption, and mission time into an aggregated fitness function. The authors construct a limit curvature matrix to evaluate the feasibility of the generated path. The authors also introduce an adaptive adjustment strategy based on the activation function for the parameters in the particle swarm algorithm. The weights of each constraint are determined according to the actual demand. The experiment results show that the authors’ method can efficiently plan the optimal path that satisfies the requirements. Compared with other improved particle swarm algorithms, the authors’ method has higher optimal search efficiency and better convergence effect. The authors also provide optimal values for important parameters such as mission energy consumption, mission time, flight speed and others to support the overall mission planning. The authors’ method has a certain practical application value.

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Section: Introductionmentioning

confidence: 99%

3D path planning problem for fighter aircraft with multiple constraints

Yang,

Xiao,

Chen

et al. 2023

The Journal of Engineering

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Analysis of Path Finding Techniques for Flying Robots through Intelligent Decision-Making Algorithms in Quantum Inspired Computing Environment

Maity,

Mishra,

Pattnaik

2024

Wireless Pers Commun

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Path planning is one of the most significant and challenging parts in the development of unmanned aerial vehicles. Over years many path-planning techniques are proposed and are being successfully used in various fields. Intelligent algorithms can be used for building autonomous drones. Though a number of algorithms haven been proposed in past few years but there is lack of research papers which compares different path planning algorithm and to find the optimal one by considering important parameters required for path planning of flying robot. Here we have used five varieties of algorithms ie ABC, ACO, PSO Quantum PSO, and hybrid algorithm which is a combination of ABC and PSO for path planning of our developed fixedwing type flying robot for operating inside a closed room environment. We have used the quantum-inspired computing method as its search performance is better as compared to classical techniques. Then we tried to compare and find the best algorithm for our flying robot out of the above five algorithms using multi-criteria decision making (MCDM) and TOPSIS where the following parameters like minimum cost, the shortest path traveled, and the least time taken were considered to find the most relevant results for autonomous flying robot path planning.

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Area Partitioning by Intelligent UAVs for effective path planning using Evolutionary algorithms

Jothi

Priyadarsini

2021

2021 International Conference on Computer Communication and Informatics (ICCCI)

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A Modified Particle Swarm Optimization for Autonomous UAV Path Planning in 3D Environment

Cited by 7 publications

References 12 publications

3D path planning problem for fighter aircraft with multiple constraints

3D path planning problem for fighter aircraft with multiple constraints

Analysis of Path Finding Techniques for Flying Robots through Intelligent Decision-Making Algorithms in Quantum Inspired Computing Environment

Area Partitioning by Intelligent UAVs for effective path planning using Evolutionary algorithms

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