A Classification of Heterogeneity in Uncrewed Vehicle Swarms and the Effects of Its Inclusion on Overall Swarm Resilience

Abhishek Joshi; Abhishek Phadke; Tianxing Chu; F. Antonio Medrano

cs.ROMar 30, 2026

A Classification of Heterogeneity in Uncrewed Vehicle Swarms and the Effects of Its Inclusion on Overall Swarm Resilience

Authors: Abhishek Joshi, Abhishek Phadke, Tianxing Chu, F. Antonio Medrano

Organizations: Department of Computer Science, Texas A&M University–Corpus Christi, Corpus Christi, Texas 78412 · School of Engineering and Computing, Christopher Newport University, Newport News, Virginia 23608 · Conrad Blucher Institute for Surveying and Science, Texas A&M University–Corpus Christi, Corpus Christi, Texas 78412

Abstract

Combining different types of agents in uncrewed vehicle (UV) swarms has emerged as an approach to enhance mission resilience and operational capabilities across a wide range of applications. This study offers a systematic framework for grouping different types of swarms based on three main factors: agent nature (behavior and function), hardware structure (physical configuration and sensing capabilities), and operational space (domain of operation). A literature review indicates that strategic heterogeneity significantly improves swarm performance. Operational challenges, including communication architecture constraints, energy-aware coordination strategies, and control system integration, are also discussed. The analysis shows that heterogeneous swarms are more resilient because they can leverage diverse capabilities, adapt roles on the fly, and integrate data from multidimensional sensor feeds. Some important factors to consider when implementing are sim-to-real-world transfer for learned policies, standardized evaluation metrics, and control architectures that can work together. Learning-based coordination, GPS (Global Positioning System)-denied multi-robot SLAM (Simultaneous Localization and Mapping), and domain-specific commercial deployments collectively demonstrate that heterogeneous swarm technology is moving closer to readiness for high-value applications. This study offers a single taxonomy and evidence-based observations on methods for designing mission-ready heterogeneous swarms that balance complexity and increased capability.

Figures & tables

Fig. 1: A high altitude fixed wing aircraft forms and maintains a communication link with a low level quadcopter swarm that performs closed range inspection

Fig. 2: Diagram highlighting multiple swarm configuration possibilities

Fig. 3: Simulation instance of quadcopter UAV swarm followed by a fixed-wing UAV

Ref	Research Contribution	Nature	Hardware	Single	Multi
[ 26 ]	Mission planning and monitoring for hetero swarms	$\checkmark$	$\checkmark$	$\checkmark$	$\checkmark$
[ 27 ]	Swarming of hetero swarms for SAR in hostile territory	$\checkmark$	$\checkmark$	$\checkmark$	—
[ 6 ]	Hetero swarms are used in the detection of chemical clouds	$\checkmark$	—	$\checkmark$	—
[ 28 ]	Swarming behavior of hetero vehicles for SAR	$\checkmark$	—	$\checkmark$	—
[ 29 ]	Multi-space hetero swarm communication and collaboration	—	$\checkmark$	—	$\checkmark$
[ 19 ]	UAV landing and recovery using UWSV	—	$\checkmark$	—	$\checkmark$

TABLE I: Classification of Heterogeneous Swarm Research

Fig. 4: Static time-series comparison of area coverage for homogeneous and heterogeneous UAV swarms over a 2D grid map. The top row shows a homogeneous swarm in which all UAVs move with the same speed. The bottom row shows a heterogeneous swarm in which at least 30% of the UAVs are fast agents.

Fig. 5: Coverage ratio as a function of time for the homogeneous and heterogeneous UAV swarms. The heterogeneous swarm achieves a faster rise in coverage because fast agents traverse unexplored regions more quickly, reducing the time required to observe the full grid.

Fig. 6: A heterogeneous robot combination working to establish EVLOS

Fig. 7: Static time-series comparison of obstacle mapping in a top-down environment. In the top row, a UAV-only system explores the map and gradually identifies obstacles. In the bottom row, a cooperative UAV+UGV team performs the same task, with the faster ground vehicle covering additional lower and middle regions of the map.

Fig. 8: Map knowledge quality as a function of time for the UAV-only and cooperative UAV+UGV configurations. The UAV-only case exhibits slower growth and lower final map quality, whereas the cooperative case improves more rapidly due to complementary sensing and distributed exploration.

Fig. 9: Static time-series comparison of communication-constrained swarm deployment. In the top row, all UAVs communicate directly with ground control, which limits how far the swarm can move from the base station. In the bottom row, two relay UAVs (blue) support multi-hop communication between the swarm and ground control, enabling the remaining UAVs (green) to extend farther into the mission space while preserving connectivity.

Fig. 10: Connected mission reach quality over time for the direct-communication swarm and the relay-assisted heterogeneous swarm. The relay-assisted configuration achieves greater communication-supported reach because the relay UAVs extend the effective connectivity region beyond the direct ground-control range.

Fig. 11: A UGV and UAV working together to perform a structural integrity check on an aircraft

Mission Characteristic	Recommended Heterogeneity Type
Uniform environment, dynamic roles	Nature-based
Hardware replacement speed critical	Nature-based
Multi-environment coverage required	Operational-space + Hardware
Sensing diversity needed	Hardware-based
Strict energy/logistics constraints	Nature-based or single-space hardware
Large geographic area, communication relay needed	Hardware-based (fixed-wing + rotorcraft)

TABLE II: Mission Characteristics to Heterogeneity Strategy Mapping

Reference	Study Contribution
[ 27 ]	Proposes a decentralized heterogeneous UAV swarm using APF and ACO with a Super-Agent class for target recognition in hostile airspace. Leader-led heterogeneous swarms achieve higher search rates and fewer unconfirmed targets than homogeneous swarms.
[ 38 ]	Investigates heterogeneous maritime swarms of high- and low-speed vehicles using a PSO-based adaptive neighbor topology for moving-target tracking. High-speed agents improve reacquisition speed but reduce optimal swarm connectivity.
[ 6 ]	Demonstrates that heterogeneous UAV teams outperform homogeneous swarms in chemical plume detection, with fast UAVs forming an outer layer and slow UAVs penetrating inward for better coverage and tracking.
[ 4 ]	Introduces the Apollonius Allocation (A2) algorithm for coordinating heterogeneous UAV pursuers to capture multiple evaders using dynamic divide-and-conquer task allocation. Guarantees finite-time capture and real-time agent reassignment under heterogeneous speed conditions.
[ 14 ]	Presents FortAttack, a mixed cooperative-competitive environment where GNN agents trained via PPO develop emergent heterogeneous behaviors such as deception, coordination, and role specialization. Demonstrates that heterogeneous strategies can arise naturally among architecturally homogeneous agents.
[ 36 ]	Proposes an Improved Multi-objective Quantum-behaved PSO (MOQPSO) with GA-inspired operators for mission planning in heterogeneous anti-radar UAV formations. Yields more stable and diverse Pareto-optimal trade-offs than standard MOPSO across time, cost, and reward objectives.

TABLE III: Summary of Heterogeneous Multi-Robot Swarm Studies

Failure Mode	Most Vulnerable Swarm Type	Heterogeneity Mitigation	Key References
Agent loss	Hardware-specialized swarms	Dynamic role reassignment (nature-based)	[ 6 ] , [ 7 ] , [ 35 ]
Communication jamming	Centralized, flat topologies	Relay hierarchy; learned jamming-resistant policies	[ 17 ] , [ 44 ] , [ 55 ]
GPS denial	Vision/GPS-dependent systems	Multi-modal SLAM; sensor-diverse hardware	[ 7 ] , [ 9 ] , [ 58 ]
Sensor degradation	Single-modality systems	Cross-modal sensing redundancy	[ 7 ] , [ 9 ] , [ 59 ]
Energy depletion	Aerial-only swarms	Surface/ground charging platforms	[ 8 ] , [ 19 ] , [ 60 ] , [ 61 ]

TABLE IV: Failure Mode to Heterogeneity Mitigation Mapping

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This paper presents a coordination architecture for heterogeneous UAV/UGV swarms that synthesises mission actions from uncertain, multi-modal sensor evidence while preserving hardware-enforced safety at the actuation boundary. The approach combines radar, RF, acoustic, and visual observations with Topic-Based Communication Space Petri Net (TB-CSPN) orchestration to support incremental mission formation under partial and evolving information. Consultant agents transform sensor outputs into temporally bounded semantic tokens, while supervisor agents provide authorisation and policy-governed release of mission transitions. This separation between interpretation, coordination, and execution yields auditable decision paths, constrains non-determinism within the coordination layer through guards and synchronisation, and enables bounded-time integration of heterogeneous evidence. To improve resilience in contested environments, including cyber compromise, spoofing, jamming, and communication loss, the digital coordination layer is complemented by independent analogue safety envelopes that clamp or veto unsafe actuator commands issued to individual vehicles. A coastal-surveillance case study illustrates how the proposed architecture enables dependable, governed, and physically safe swarm coordination under operational uncertainty.

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Multi-Robot Coordination Multi-Robot Systems Robot Safety

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Jul 5, 2026

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Uncrewed Aerial Vehicle (UAV) swarms have significant potential for applications such as Search and Rescue (SAR) and environmental monitoring, but their real-world deployment is limited by a lack of situational awareness, intermittent connectivity, and significant cybersecurity risks. Agentic Artificial Intelligence (AI) represents a shift from standalone Large Language Model (LLM) toward closed-loop cognitive architectures that integrate perception, memory, reasoning/planning, and action to enable adaptive, goal-directed swarm behavior. Within this framework, Agentic AI provides a unifying structure for autonomous and adaptive swarm operations while expanding the system attack surface compared to conventional AI systems. This paper proposes LLM-Centric Agentic AI for UAV Swarms (LAUS) and reviews key enabling technologies such as onboard and edge computing, 5G/6G connectivity, multimodal intelligence, and cybersecurity mechanisms, and analyzes threats such as Priority Manipulation Attacks (PMA) that can distort decision-making and degrade network performance. Finally, it identifies open research challenges, including hallucination-resistant reasoning, onboard LLM deployment under SWaP constraints, and standardized security benchmarks for perception-reasoning attacks in agentic UAV systems.

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Multi-Agent LLM Systems AI Agent Security Multi-Robot Coordination Multi-Agent System Security Agentic AI

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LAEI: Layered Autonomous Edge Intelligence Framework for Robust UAV Swarm Operations

Autonomous UAV swarms require scalable coordination mechanisms that maintain mission performance under limited communication, environmental uncertainty, and component failures. Centralized approaches provide global coordination but suffer from communication bottlenecks and single-node vulnerabilities, whereas fully decentralized methods often lack mission-level consistency. This paper presents Layered Autonomous Edge Intelligence (LAEI), a UAV-swarm framework that combines onboard learned policies with lightweight mission-level supervision. Each UAV performs local perception, obstacle avoidance, and action selection onboard, while the supervisory layer provides adaptive goal reassignment, fault-aware recovery, and context-dependent policy guidance without directly controlling low-level actions. LAEI further incorporates recovery strategies, including dynamic reassociation, backup supervisory support, and fallback local autonomy, to maintain mission continuity under representative failure scenarios. We evaluate LAEI in simulated UAV-swarm scenarios using mission completion time, collision rate, and coverage efficiency. The results show that LAEI reduces mission completion time and improves operational efficiency while maintaining collision-aware distributed UAV-level decision-making.

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School of Electrical Engineering Korea University Seoul 02841, Korea

Aerial Robotics Swarm Robotics Multi-Robot Coordination Multi-Agent Systems

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Abstract

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