Multi-agent unmanned aerial vehicle (UAV) teaming becomes an essential part in science mission, modern warfare surveillance, and disaster rescuing. This paper proposes a decentralized UAV swarm persistent monitoring strategy in realizing continuous sensing coverage and network service. A two-layer (high altitude and low altitude) UAV teaming hierarchical structure is adopted in realizing the accurate object tracking in the area of interest (AOI). By introducing the UAV communication channel model in its path planning, both centralized and decentralized control schemes would be evaluated in the waypoint tracking simulation. The UAV swarm network service and object tracking are measured by metrics of communication link quality and waypoints tracking accuracy. UAV swarm network connectivity are evaluated over different aspects, such as stability and volatility. The comparison of proposed algorithms is presented with simulations. The result shows that the decentralized scheme outperforms the centralized scheme in the mission of persistent surveillance, especially on maintaining the stability of inner UAV swarm network while tracking moving objects.

The map recovery and fusion is a key issue in the application of large scale and long-term AR scenarios. However, they are still not addressed well in an efficient and precise way, especially for complex industrial environments. In this study, we propose a map recovery and fusion strategy based on VI SLAM. We first develop a heuristic strategy that can fast search and match map points among multiple maps, and can be used for efficient map fusion. For map recovery, we leverage the inertial sensors for short time motion estimation, and transform the previous lost map to the current map. Based on this, a novel framework for collaborative AR is implemented and parallelly runs in multiple mobile devices in real time. Extensive experiments have been carried out on a public dataset. The results show that the proposed method can recovery and fuse multiple maps with high completeness and precision.

Quadrotor unmanned aerial vehicles (UAVs), commonly known as quadcopters, pose a fundamental control challenge: they are underactuated, open-loop unstable, strongly coupled across six degrees of freedom, and highly susceptible to aerodynamic disturbances and payload uncertainty. Classical model-based controllers degrade significantly when the vehicle mass shifts due to battery discharge or payload pickup, or when wind gusts produce forces comparable to the available thrust margin—an especially acute problem for nano-scale quadcopters. This paper proposes an active disturbance rejection control (ADRC) to address these challenges. Rather than attempting to model every source of uncertainty, we employ an extended state observer (ESO) to estimate a single total disturbance signal—comprising unmodeled dynamics, parametric errors, nonlinear coupling, and external disturbances—in real time from sensor measurements alone, and cancel it before applying a simple feedback law. Building on this principle, we derive a cascaded linear ADRC (LADRC) architecture that governs all six degrees of freedom of the quadcopter and formulate the quadrotor-specific total-disturbance structure for each control channel. Simulations demonstrate that the proposed controller maintains small, bounded position-tracking RMS errors under 30% mass uncertainty combined with a sustained lateral wind-gust disturbance, while delivering the correct hover thrust automatically without prior knowledge of the true mass and without integrator wind-up.

Unmanned 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 interagent 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 regions, 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, 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.