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Not AvailableUnmanned autonomous system (UAS) swarms empower groups of robotic agents (such as drones) to collectively accomplish complex missions by navigating intricate environments, but face significant challenges when operating in contested areas with electromagnetic jamming that disrupts inter-agent communication. This paper introduces an innovative hierarchical control strategy that enables a drone swarm to traverse complex terrains, including regions compromised by electromagnetic jamming intended to disrupt communications. The swarm is initially unaware of the existence or location of these jamming areas, making autonomous detection and avoidance critical for mission success. The proposed strategy synergistically combines communication-aware formation control, particle swarm optimization (PSO) for reactive navigation, and a global path planner for deadlock resolution to ensure the swarm maintains optimal communication quality and mission continuity. A gradient controller maintains the desired swarm formation by maximizing communication quality among agents, while a PSO-based movement controller navigates toward a destination, avoiding newly discovered jamming areas. To address potential deadlocks, a path planning algorithm is invoked to generate a globally optimal route. The framework operates without prior knowledge of jamming locations, learning threat positions through real-time environmental interaction. A comparative analysis of various path planning algorithms, including A*, Greedy Best-First Search, Breadth-First Search, Dijkstra’s algorithm, Jump Point Search, and Theta*, is presented through simulation. The results demonstrate that A* provides optimal performance for our integrated framework, facilitating efficient and robust navigation for the UAS swarms, while maintaining high communication quality among individual drones. The source code for this work is available at https://github.com/speccoud/Swarm-Control. 

This paper presents a formation control strategy for a swarm of unmanned aerial vehicles (UAV s) that navigate towards a destination while avoiding a jamming area. The proposed approach utilizes a gradient controller for formation control to maximize the communication quality within the swarm. A movement controller is used to go to a destination and avoid a jamming area without prior knowledge of its existence. A simulation is conducted to prove the effectiveness of this approach. The result highlights an efficient navigation strategy while maintaining sufficient communication quality for a multi-agent system. 

The advancement of wireless networking has significantly enhanced beamforming capabilities in Autonomous Unmanned Aerial Systems (AUAS). This paper presents a simple and efficient classical algorithm to route a collection of AUAS or drone swarms extending our previous work on AUAS. The algorithm is based on the sparse factorization of frequency Vandermonde matrices that correspond to each drone, and its entries are determined through spatiotemporal data of drones in the AUAS. The algorithm relies on multibeam beamforming, making it suitable for large-scale AUAS networking in wireless communications. We show a reduction in the arithmetic and time complexities of the algorithm through theoretical and numerical results. Finally, we also present an ML-based AUAS routing algorithm using the classical AUAS algorithm and feed-forward neural networks. We compare the beamformed signals of the ML-based AUAS routing algorithm with the ground truth signals to minimize the error between them. The numerical error results show that the ML-based AUAS routing algorithm enhances the accuracy of the routing. This error, along with the numerical and theoretical results for over 100 drones, provides the basis for the scalability of the proposed ML-based AUAS algorithms for large-scale deployments. 

Over the past few decades, unmanned aerial vehicle (UAV) technology has played a significant role in military and civilian applications. To meet the challenges of the future, in addition to improving the functionality and utility of individual aircraft, there is a need to consider how to develop more effective UAV management and organizations. Consequently, among the many developments in UAVs, formation control has become an important concept in recent years. Formation control requires multiple UAVs to adapt to the mission including generate a formation, stay in formation, and change formation. In this paper, we further constrain the formation controller model, not only estimate the desired separation with acceptable accuracy but also ensure a consensus among estimates. Thus, optimizes the overall communication performance of a dynamical multi-agent system. 

The emerging concept of drone swarms creates new opportunities with major societal implications. However, future drone swarm applications and services pose new communications and sensing challenges, particularly for collaborative tasks. To address these challenges, in this paper, we integrate sensor arrays and communication to propose a mathematical model to route a collection of autonomous unmanned aerial systems (AUAS), a so-called drone swarm or AUAS swarm, without having a base station of communication but communicating with each other using multiple spatio-temporal data. The theories of structured matrices, concepts in multi-beam beamforming, and sensor arrays are utilized to propose a swarm routing algorithm. We address the routing algorithm’s computational and arithmetic complexities, precision, and reliability. We measure bit-error-rate (BER) based on the number of elements in sensor arrays and beamformed output of the members of the swarm to authenticate and secure the routing for the decentralized AUAS networking. The proposed model has the potential to enable future drone swarm applications and services. Finally, we discuss future work on obtaining a machine-learning-based low-cost drone swarm routing algorithm.