Cooperative Perception, Planning, and Control of Heterogeneous UAVs

A special issue of Drones (ISSN 2504-446X). This special issue belongs to the section "Artificial Intelligence in Drones (AID)".

Deadline for manuscript submissions: 15 June 2026 | Viewed by 991

Special Issue Editors


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Guest Editor
School of Astronautics, Northwestern Polytechnical University, Xi’an 710072, China
Interests: game theory; anti-disturbance control methods; reinforcement learning; pursuit and evasion games

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Guest Editor
The Institute of Engineering Thermophysics, Chinese Academy of Sciences, Beijing 100190, China
Interests: inertial navigation; integrated navigation; flight control

E-Mail Website
Guest Editor
School of Astronautics, Northwestern Polytechnical University, Xi’an 710072, China
Interests: game theory and its application to UAVs

Special Issue Information

Dear Colleagues,

Heterogeneous UAVs encompass two core types: model-heterogeneous UAVs and function-heterogeneous UAVs. Model-heterogeneous UAVs have distinct maneuverability and payload capacities, typical examples include high-speed fixed-wing UAVs and low-altitude multi-rotor UAVs. Function-heterogeneous UAVs integrate diverse sensors and payloads to enable functions such as reconnaissance, strike, damage assessment, and electronic jamming. Their synergistic operation has become indispensable for complex missions, ranging from military operations to civilian disaster rescue scenarios. In military scenarios, reconnaissance UAVs locate targets, strike UAVs execute tasks, and assessment UAVs verify effects—the civilian equivalent would involve long-endurance model-heterogeneous UAVs expanding coverage and function-heterogeneous UAVs equipped with thermal imagers detecting survivors.​

Yet, their cooperative perception, planning, and control face unique bottlenecks. Model heterogeneity leads to mismatched motion constraints; a typical case is that slow multi-rotor UAVs struggle to coordinate with fast fixed-wing UAVs. Function heterogeneity causes multi-source data inconsistency; for instance, reconnaissance images, jamming signals, and payload status all require unified fusion. Otherwise, dynamic disturbances, such as electromagnetic interference and sudden task changes, further undermine synergy robustness. Conventional homogeneous UAV collaboration methods fail to address these issues.​

This Special Issue aims to showcase cutting-edge research on bridging theory and practice in this field. Submissions from academia and industry are welcome.​ The Special Issue welcomes submissions related, but not limited to, the following topics:

  • Cooperative perception with heterogeneous sensors for UAVs;
  • Robust cooperative path planning under environmental uncertainties;
  • Cooperative control for heterogeneous dynamics consensus;
  • AI-driven dynamic cooperative decision-making for heterogeneous UAVs;
  • Navigation autonomy in GNSS-denied environments;
  • Heterogeneous multi-agent cross-domain synergy;
  • Safety-aware cooperative control.

We look forward to receiving your original research articles and reviews.

Prof. Dr. Yuan Yuan
Dr. Qian Zhang
Dr. Huanhuan Yuan
Guest Editors

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Keywords

  • model-heterogeneous UAVs
  • function-heterogeneous UAVs
  • cooperative control
  • cooperative planning
  • cooperative perception
  • multi-source data Fusion
  • distributed control system
  • formation control
  • synergy robustness
  • motion constraint matching

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Published Papers (1 paper)

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Research

33 pages, 3113 KB  
Article
Hierarchical Role-Based Multi-Agent Reinforcement Learning for UHF Radiation Source Localization with Heterogeneous UAV Swarms
by Yuanqiang Sun, Xueqing Zhang, Menglin Wang, Yangqiang Yang, Tao Xia, Xuan Zhu and Tonghe Cui
Drones 2026, 10(1), 54; https://doi.org/10.3390/drones10010054 - 12 Jan 2026
Viewed by 643
Abstract
With the continuous proliferation of radio frequency devices, electromagnetic environments in various regions are becoming increasingly complex. Effective monitoring of the electromagnetic environment and identification of interference sources have thus become critical tasks for maintaining order in the electromagnetic spectrum. In recent years, [...] Read more.
With the continuous proliferation of radio frequency devices, electromagnetic environments in various regions are becoming increasingly complex. Effective monitoring of the electromagnetic environment and identification of interference sources have thus become critical tasks for maintaining order in the electromagnetic spectrum. In recent years, rapid advances in UAV technology have spurred exploration of UAV-based electromagnetic spectrum monitoring as a novel approach. However, the limited payload capacity and endurance of UAVs constrain their monitoring capabilities. To address these challenges, we propose HMUDRL, a distributed heterogeneous multi-agent deep reinforcement learning algorithm. By leveraging cooperative operation between cluster-head UAVs (CH) and cluster-monitoring UAVs (CM) within a heterogeneous UAV swarm, HMUDRL enables high-precision detection and wide-area localization of UHF radiation source. Furthermore, we integrate a minimum-gap localization algorithm that exploits the spatial distribution of multiple CM to accurately pinpoint anomalous radiation sources. Simulation results validate the effectiveness of HMUDRL: in the later stages of training, the success rate of localizing target radiation sources converges to 96.1%, representing an average improvement of 1.8% over baseline algorithms; localization accuracy, measured by root mean square error (RMSE), is enhanced by approximately 87.3% compared to baselines; and communication overhead is reduced by more than 80% relative to homogeneous architectures. These results demonstrate that HMUDRL effectively addresses the challenges of data transmission control and sensing-localization performance faced by UAVs in UHF spectrum monitoring. Full article
(This article belongs to the Special Issue Cooperative Perception, Planning, and Control of Heterogeneous UAVs)
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