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Collaborative Mission Planning and Control Techniques for Unmanned Aerial Vehicle (UAV) Swarm Systems

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Aerospace Science and Engineering".

Deadline for manuscript submissions: 10 July 2025 | Viewed by 691

Special Issue Editors

School of Astronautics, Northwestern Polytechnical University, Xian 710072, China
Interests: hypersonic vehicles; modeling; scramjet engine; aerodynamic analysis; propulsion/flight dynamics
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Guest Editor
College of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410073, China
Interests: wind turbines; vortex; hypersonics; drag; vorticity; numerical simulation; flow; aerodynamics; aircraft; drag reduction
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Swarm intelligence technology is a new technology combining unmanned system technology, network information technology, and artificial intelligence technology, and it has become a research hotspot. Due to the large scale, diverse tasks, and flight uncertainty of unmanned system clusters, they pose great challenges to UAVS swarm mission planning and control. There is an urgent need to conduct technical research on collaborative mission planning and distributed cooperative control for UAVS swarm. Topics of interest for this Special Issue include, but are not limited to, the following:

  • (1)Swarm distributed situation awareness and cognitive technology:
    • a)Modeling of distributed situation awareness capability of multi-agents in uncertain environments; 
    • b)Cooperative situation awareness method under multi field coupling;
    • c)Situation awareness consistency assessment method.
  • (2)Swarm intelligent decision technology in strong confrontation environment:
    • a) Swarm autonomous decision-making method based on a decision rules base;
    • b) Penetration maneuver decision making of high-speed vehicle swarms;
    • c) Intelligent decision making of high-speed aircraft cluster attack.
  • (3)Swarm collaborative planning technology in a complex battlefield environment:
    • a) Evaluation and system optimization framework design of swarm task planning;
    • b) swarm collaborative dynamic mission planning technology in uncertain environments;
    • d) Collaborative mission planning technology for swarm penetration.
  • (4)Swarm strike cooperative task planning technology under multi constraint and strong coupling conditions:
    • a)Autonomous control technology of high-speed vehicle swarms;
    • b)Research on autonomous control method and control strategy of swarm;
    • c)High-speed aircraft swarm control technology for topology switching;
    • d)Robust adaptive control technology for high-speed vehicle swarms;
    • e)Cooperative attack terminal guidance technology under multiple constraints and strong coupling.
  • (5)Verification system of key technologies of swarm intelligent planning and autonomous control:
    • a) Design and integration of full digital simulation verification platform for intelligent planning and autonomous control of high-speed vehicle swarms;
    • b) Hardware in the loop simulation verification system of a collaborative planning controller;
    • c) Verification of aircraft swarm flight test in typical scenarios.
  • (6)Other relevant theories, methods, technologies, systems and platforms.

Dr. Dong Zhang
Prof. Dr. Wei Huang
Guest Editors

Manuscript Submission Information

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Keywords

  • unmanned aerial vehicle
  • swarm distributed situation awareness and cognitive technology
  • swarm intelligent decision technology in strong confrontation environment
  • swarm collaborative planning technology in a complex battlefield environment
  • swarm strike cooperative task planning technology under multi constraint and strong coupling condition
  • verification system of key technologies of swarm intelligent planning and autonomous control

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Published Papers (1 paper)

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Research

29 pages, 16739 KiB  
Article
Advancing Multi-UAV Inspection Dispatch Based on Bilevel Optimization and GA-NSGA-II
by Yujing Liu, Chunmei Chen, Yu Sun and Shaojie Miao
Appl. Sci. 2025, 15(7), 3673; https://doi.org/10.3390/app15073673 - 27 Mar 2025
Viewed by 200
Abstract
In multi-UAV collaborative power grid inspection, the system efficiency of existing methods is limited by the performance of both task assignment and path planning, which is critical in large-scale task scenarios, resulting in a huge computational cost and a high possibility to local [...] Read more.
In multi-UAV collaborative power grid inspection, the system efficiency of existing methods is limited by the performance of both task assignment and path planning, which is critical in large-scale task scenarios, resulting in a huge computational cost and a high possibility to local optimality. To address these challenges, a bilevel optimization framework based on GA-NSGA-II and task segmentation is proposed to balance the total inspection distance and the distance standard deviation of UAVs, where the outer optimization employs the NSGA-II to assign task units to each UAV evenly, while the inner optimization deploys an adaptive genetic algorithm with an elite retention strategy to optimize the inspection direction and order in each task domain to obtain a Pareto-optimal solution set under constraints. To avoid the dimensionality disaster, the massive inspection points are combined into task units based on the UAV’s endurance. In scenarios with 284 tower task points, the proposed algorithm has reduced the standard deviation of UAV flight distances by 41.91% to 84.63% and the longest flight distance by 29.41% to 43.98% compared to the GA-GA bilevel optimization. Against task-adaptive clustering optimization, it decreased the standard deviation by 18.25% to 94.93% and the longest flight distance by 15.97% to 37.33%. Applying it to 406 tower task points also confirmed the GA-NSGA-II bilevel optimization’s effectiveness in minimizing the total inspection distance and balancing UAV workloads. Full article
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