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Communication and Control in Collaborative UAVs: Recent Advances and Future Trends

Shumaila Javaid, Nasir Saeed, Zakria Qadir, Hamza Fahim, Bin He, Houbing Song, Muhammad Bilal

arXiv:2302.12175v1eess.SPcs.MAcs.RO

TL;DR

Collaborative UAV swarms face communication, control, and collective-decision challenges that constrain autonomy, robustness, and reliability. This review synthesizes communication and control requirements, collaborative tasks, urban applications, and use cases, while identifying future research directions; one reviewed disaster-management comparison reports SFOA at 109.42 s and 10.971 km.

  • Problem

    Communication, control, and collective decision-making challenges limit the autonomy, robustness, and reliability of collaborative UAV swarms.

  • Method

    The paper comprehensively reviews collaborative UAV communication, requirements, cooperative tasks, urban applications, use cases, and future research directions.

  • Results

    In a complex disaster-management case, SFOA outperforms PSO, FPA, and GHO with a computation time of 109.42 s and distance covered as 10.971 km.

  • Takeaways & Limitations

    The review identifies collaborative communication, control, task coordination, and future research directions as central to advancing collaborative UAV systems.

Abstract

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The recent progress in unmanned aerial vehicles (UAV) technology has significantly advanced UAV-based applications for military, civil, and commercial domains. Nevertheless, the challenges of establishing high-speed communication links, flexible control strategies, and developing efficient collaborative decision-making algorithms for a swarm of UAVs limit their autonomy, robustness, and reliability. Thus, a growing focus has been witnessed on collaborative communication to allow a swarm of UAVs to coordinate and communicate autonomously for the cooperative completion of tasks in a short time with improved efficiency and reliability. This work presents a comprehensive review of collaborative communication in a multi-UAV system. We thoroughly discuss the characteristics of intelligent UAVs and their communication and control requirements for autonomous collaboration and coordination. Moreover, we review various UAV collaboration tasks, summarize the applications of UAV swarm networks for dense urban environments and present the use case scenarios to highlight the current developments of UAV-based applications in various domains. Finally, we identify several exciting future research direction that needs attention for advancing the research in collaborative UAVs.

I. INTRODUCTION

UAV applications increasingly require collaborative swarms, but communication, control, and coordination challenges limit autonomous, reliable multi-UAV operation. This review addresses the gap by synthesizing collaborative communication requirements, tasks, applications, and future directions.

  • UAV swarms support military, civilian, and commercial applications that require multiple vehicles to coordinate rather than operate independently.
  • Collaborative communication lets UAVs share information and responsibilities, improving operational speed, reliability, and robustness when individual vehicles fail.
  • Existing studies target service time, energy efficiency, coverage, and communication performance through collaborative schemes such as beamforming.
  • Research addresses collaborative communication through channel characterization, resource management, data communication, and 5G/6G integration.
  • Earlier surveys cover related UAV networking, wireless-network enhancement, channel models, WSN collaboration, IoT, machine learning, and multi-UAV communication requirements.
  • The review fills a stated gap by comprehensively focusing on collaborative UAV communication, including requirements, use cases, challenges, and future research.

B. Contributions and Organization

The paper organizes its contribution around the requirements, tasks, applications, use cases, and future directions of collaborative UAV systems. It also situates multi-UAV cooperation against single-UAV limitations and describes the paper’s section structure.

  • B. Contributions and Organization: The review summarizes fundamental communication, control, and cooperation requirements and challenges for UAV collaboration.
  • B. Contributions and Organization: It surveys collaborative tasks including joint completion, trajectory formation, cooperative localization, data collection, and cooperative decisions, alongside urban swarm-network applications.
  • B. Contributions and Organization: It presents collaborative-UAV use cases in agriculture, environmental monitoring, remote sensing, surveillance, and disaster management.
  • B. Contributions and Organization: The paper identifies future research directions intended to advance collaborative UAVs.
  • B. Contributions and Organization: The paper proceeds from communication and control requirements to urban applications, use cases, future directions, and concluding findings.
  • II. COLLABORATION IN UAVS: AN OVERVIEW: Compared with isolated single-UAV operation, multi-UAV cooperation supports common objectives such as high-resolution disaster mapping and gas-line localization.

A. Intelligent UAVs

Intelligent collaborative UAVs combine sensing, communication, control, and computation to support distributed operations and autonomous decisions. Their communication architecture includes UAV-to-infrastructure and UAV-to-UAV links, each with distinct capabilities and constraints.

  • A. Intelligent UAVs: A conventional UAV comprises sensing, communication, control, and computational units.
  • A. Intelligent UAVs: Sensors support tasks such as high-resolution assessment, temperature estimation, light detection, and antenna configuration, while communication enables information exchange.
  • A. Intelligent UAVs: Intelligent UAVs use collaborative communication for distributed operations and independent decisions requiring coordination with devices, machines, robots, drones, and people.
  • A. Intelligent UAVs: A lack of efficient autonomous UAV-to-UAV communication hinders independent flight, trajectory formation, target localization, and data-manipulation decisions.
  • A. Intelligent UAVs: Multi-UAV communication uses UAV-to-infrastructure and UAV-to-UAV modes to exchange data and maintain connectivity for collaborative communication.
  • 1) UAV-to-Infrastructure:: UAV-to-infrastructure links can use terrestrial systems, HAPs, or satellites, with UAVs serving as relays, users, or base stations.
  • 1) UAV-to-Infrastructure:: Terrestrial links face interference, control-message QoS requirements, LoS issues, and disaster-related damage to communication entities and backhaul networks.
  • 1) UAV-to-Infrastructure:: Satellite links provide global and beyond-LoS coverage but face high propagation loss and delay, while HAP links offer broad coverage and reliability but require spectrum, safety, privacy, and security solutions.

2) UAV-to-UAV Communications:

UAV-to-UAV communication can use satellite, Wi-Fi, UHF, cellular, LoRaWAN, and FSO links, each balancing range, reliability, bandwidth, latency, interference, or mobility constraints. These links support collaborative swarm operations but remain bounded by application and environmental requirements.

  • Satellite, Wi-Fi, UHF, cellular, LoRaWAN, and FSO are presented as approaches for UAV-to-UAV communication.The section surveys these link types as alternatives for supporting swarm coordination and data exchange.
  • Wi-Fi links provide short-range communication but suffer high interference, while ground-control relays can introduce delay.The relay configuration supports beyond-line-of-sight communication but reduces effectiveness for mission-critical applications.
  • UHF links support longer-distance communication and near-line or non-line-of-sight operation, but their licensed frequency allocation remains unspecified.The cited example uses a point-to-point 400 MHz UHF link.
  • Cellular links alleviate range, networking, and resource limitations by reusing existing cellular base stations without dedicated UAV infrastructure.The section frames cellular connectivity as a cost-effective option for civil and commercial UAV applications.
  • LoRaWAN offers long-distance line-of-sight communication, high connectivity, expanded coverage, and low energy dissipation, but operates under severe interference and path-loss conditions.The section identifies interference mitigation as necessary for reliable LoRaWAN UAV-to-UAV communication.
  • FSO links provide long-range, high-bandwidth point-to-point communication, yet UAV mobility causes transmitter–receiver misalignment and atmospheric effects degrade the link.The section calls for channel and mobility modeling to address these constraints.
  • Satellite links provide secure, stable, and reliable coverage for long-term operations, but Doppler shift and high latency constrain time-critical applications.The cited limitation follows from UAV motion and the resulting high relative velocity.

C. Control Requirements

UAV control systems must support flexible movement and trajectory tracking across takeoff, landing, hovering, maneuverability, altitude control, localization, and collision avoidance. The reviewed requirements span platform-specific launch behavior, six-degree-of-freedom motion control, state-based flight control, and obstacle avoidance.

  • UAV control systems must enable takeoff, landing, hovering, maneuverability, altitude control, localization, and collision avoidance.These requirements are motivated by the need for cost-effective control systems in small, low-cost UAVs.
  • Landing and Takeoff: Fixed-wing UAVs require runways for takeoff and landing, whereas rotary-wing UAVs can take off and land vertically.A hybrid VTOL solution is described as combining fixed-wing and rotary-wing features.
  • Controlled Movement and Hovering: Rotor-based control coordinates roll, thrust, pitch, and yaw across six degrees of freedom for maneuvering and hovering.Existing models also analyze throttle movements, state information, and onboard sensing for stable flight.
  • In-flight Control: Position and velocity states guide in-flight control for precision operations such as landing and object tracking.RAMA additionally uses altitude, angular rates, and position, while PID controllers support autonomous UAV operations.
  • Collision avoidance: Collision avoidance combines GPS-guided navigation, obstacle sensors, accurate location estimation, and trajectory planning.Infrared, pressure, and height sensors can estimate obstacle distance for movement control.

D. Collaborative Tasking

Collaborative tasking distributes information, responsibilities, control, and learning across UAV swarms to improve task completion, coverage, efficiency, coordination, and network performance. Reviewed approaches include routing and clustering, human-supervised self-organization, mobile-network coordination, reinforcement learning, and distributed resource management.

  • Collaborative tasking lets multiple UAVs share information and responsibilities while performing tasks in a distributed manner.The section associates this architecture with improved flexibility, robustness, fault tolerance, service time, energy efficiency, coverage, and communication performance.
  • A fuzzy genetic algorithm addresses clustering and routing for polygon-visiting multiple traveling salesman problems in UAV swarms.The described approach uses distance for cluster formation and a cost function.
  • Human operators can control UAV swarms at different levels to support self-organization for monitoring and surveillance.This approach combines human supervision with autonomous swarm organization.
  • Mobile-network coordination enables UAVs to fly in swarm or patrol modes, capture images, and transmit them to a ground station.The cited system integrates smartphones into UAVs for collaborative movement and coordination.
  • Multiagent reinforcement learning supports cooperative target tracking by using past and present target states for flight decisions.The section identifies reinforcement learning as a recurring approach for path planning, navigation, and control in complex environments.
  • Geometric and interference-aware reinforcement learning approaches are used for path planning, navigation, energy efficiency, coverage, and connectivity.One scheme selects candidate points through a reward matrix, while another addresses cellular-connected UAV path planning.
  • Distributed reinforcement-learning protocols improve UAV coordination for collaborative trajectory control, sensing, communication, and resource management.A multiagent framework treats each UAV as a learning agent for resource allocation.

3) Trajectory Formation:

The review covers collaborative trajectory formation, cooperative localization, search and rescue, and UAV–WSN–IoT data collection, alongside cooperative decision-making under uncertainty. These tasks use optimized paths, consensus, heterogeneous sensing, predefined formations, and cloud processing to support coordinated operations.

  • 3) Trajectory Formation:: Collaborative trajectory formation seeks paths from starting points to targets while reducing localization cost, improving maneuver decisions, and supporting collision avoidance.The section identifies trajectory formation as an emerging UAV research area.
  • 3) Trajectory Formation:: Optimized artificial potential fields support dynamic step adjustment and climb strategies for safer, more stable paths with reduced collision probability.The method is described as a dynamic trajectory-planning approach.
  • 3) Trajectory Formation:: Figure 3 depicts trajectory optimization using collaborative UAVs.The figure is associated with the trajectory-formation discussion.
  • 3) Trajectory Formation:: Cooperative relative localization enables infrastructure-free UAV communication and flight formation through consensus-based fusion of direct and indirect target-location estimates.Each UAV participates in consensus-based fusion for localization.
  • 4) Cooperative Target Localization:: Collaborative communication supports faster target identification and better position accuracy, while cooperative localization methods aim to reduce communication delay and packet loss.Accurate localization is linked to target indication, aerial filming, data sensing, and air-to-ground attacks.
  • 4) Cooperative Target Localization:: Heterogeneous bearing-only and range-only sensors reduce cooperative-maneuver complexity and shorten data-collection time.The cited cooperative maneuver uses two UAVs equipped with different sensor types.
  • 4) Cooperative Target Localization:: Post-disaster cooperative search and rescue assigns UAVs localization, inspection, path-planning, and navigation tasks to identify houses and survivors.The operation uses multiple UAVs with differentiated collaborative roles.
  • 5) Data Collection:: UAV–WSN–IoT architectures support remote data collection through predefined trajectories, agricultural sensing, device localization, cloud storage, and data processing.In agriculture, multiple UAVs search for IoT devices; in post-disaster management, UAVs operate in predefined clusters.

6) Cooperative Decisions:

UAV swarm networks support smart-city transportation, monitoring, and IoT-enabled services, while collaborative architectures address connectivity, latency, scalability, and large-area sensing demands.

  • UAVs support smart-city applications including transportation, surveillance, infrastructure monitoring, networking, and disaster relief.
  • Urban transportation systems face connectivity problems from vehicle mobility, obstacles, bridges, and tall buildings.
  • Hybrid vehicular ad hoc and UAV swarm networks seek reliable routing, path-expiration tracking, and fast data delivery.
  • UAVs equipped with sensors and communication devices can independently monitor large areas using high-resolution, multispectral, hyperspectral, and thermal imagery.
  • Environmental monitoring requires UAV swarms to cooperate with ground sensing devices for broad-area coverage, resource sharing, and data processing.
  • UAV-IoT frameworks and UAV-fog architectures support continuous monitoring while targeting lower latency, greater scalability, and real-time communication.

C. Intelligent surveillance

Urban surveillance uses UAVs for security monitoring, but advanced persistent surveillance exceeds the resources of single UAVs and requires collaborative sensing, computation, and communication.

  • UAV surveillance systems support urban security by tracking intruders and monitoring unsafe activities with integrated high-definition cameras.
  • Reinforcement-learning control and neural-network localization have been used for persistent cooperative surveillance and improved target identification in unknown urban areas.
  • Long-term surveillance generates extensive video data that must be communicated, managed, and analyzed for smart decision-making.
  • UAV-enabled mobile edge computing reduces energy consumption by placing computation near IoT devices for local processing and resource management.
  • Further work is needed to optimize communication among IoT devices and UAVs for task offloading and resource sharing.

IV. USE CASES OF COLLABORATIVE UAVS

Collaborative UAVs extend applications across agriculture, environmental monitoring, surveillance, remote sensing, and disaster management by combining sensing, processing, and coordinated operation.

  • Collaborative UAVs are presented as use cases spanning agriculture, environmental monitoring, remote sensing, surveillance, and disaster management.
  • Agricultural UAVs support soil analysis, planting-pattern scheduling, irrigation planning, and fertilizer-quantity estimation.
  • UAV swarms can combine with ground IoT systems to cover large agricultural fields faster while collecting, processing, and acting on crop data.
  • Single UAVs cannot satisfy advanced surveillance demands because prolonged flight, city-scale scanning, and video analytics exceed their resources and computational power.
  • Multiple UAVs with heterogeneous cameras, radar, sensors, storage, and computing can divide surveillance tasks across commercial, civil, and military applications.
  • Cooperative aerial imaging combines images captured from different viewpoints to create an overall image for applications such as security, fire monitoring, and wildlife tracking.

D. Disaster Management

Collaborative UAVs assist disaster response through coordinated rescue, imaging, and path planning, while UAV swarms also provide connectivity in underserved regions.

  • UAV swarms support disaster risk reduction, preparedness, response, recovery, relief, and rehabilitation by assigning distinct rescue and relief tasks.
  • High-definition cameras and thermal sensors help collaborative UAVs locate survivors under rubble and in inaccessible spaces.
  • Four metaheuristic algorithms—SFOA, GHO, FPA, and PSO—were compared for obstacle-free UAV-swarm paths in complex disaster environments.
  • 109.42 s computation time and 10.971 km distance covered are reported for SFOA, which outperformed the other algorithms in the complex case.
  • UAV swarms can create flying ad hoc networks to provide coverage in unconnected rural and underserved regions.

V. FUTURE RESEARCH DIRECTIONS

The review identifies unresolved challenges in coordinated UAV tasking and outlines future directions spanning urban trajectory planning, resource-aware federated learning, and UAV-integrated connectivity.

  • Future research directions: The review summarizes these future research directions as part of its broader focus on communication, control, and collaboration requirements for coordinated UAV tasking.The directions are presented in Table IV.
  • Urban trajectory optimization: Dense urban trajectory planning must account for multipath interference, obstruction, attenuation, and low-altitude flight among natural and man-made obstacles.These factors affect the cost and time associated with reaching a destination.
  • Federated learning: Resource-aware federated learning is needed to address computational complexity, limited onboard memory, and high latency in collaborative UAVs.Future work also needs to connect federated learning and trajectory optimization while managing energy constraints.
  • UAV-integrated coverage: UAV-integrated cellular coverage can improve connectivity in dense urban areas affected by buildings, bridges, and tunnels, and support faster-route selection for VANETs.The passage also identifies disaster scenarios in which communication infrastructure is destroyed or unavailable.

D. Self-organization

Self-organization could let UAV swarms independently support monitoring, coverage, and disaster relief, but distributed, hybrid, and centralized approaches remain challenging in confined environments. Collaborative task offloading and energy management are additional constraints for practical coordination.

  • D. Self-organization: Self-organizing UAV swarms can aggregate and manage data, preserve privacy, optimize paths, and operate with fewer instructions from a central base station.Examples include monitoring agricultural fields and providing cellular coverage or disaster relief.
  • D. Self-organization: Achieving self-organization through distributed, hybrid, or centralized methods remains challenging, especially when UAVs must hover, collect data, and make collaborative decisions in confined environments.Collision avoidance, obstacle avoidance, and data-related issues also require further attention.
  • E. Collaborative Task Offloading: Task offloading can cause performance delays and network congestion, while collaborative edge computing can reduce computational costs and securely transfer data between edge devices.UAV-based offloading can improve computational efficiency and QoS, but complex environments introduce signal-coverage problems.
  • Energy constraints: Energy scarcity constrains collaborative UAV networks because transmitting data and retrieving information consume battery power, particularly during long-term surveillance.Long-duration placement can generate substantial video data that requires considerable power to transmit.
  • Scope and motivation: The review frames collaborative UAVs as important for advancing autonomy and coordination across a wide range of applications while emphasizing unresolved communication, control, and decision-making challenges.Its scope includes trajectory formation, target localization, data collection, cooperative decisions, and real-world monitoring applications.
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