The integration of unmanned aerial vehicles (UAVs) into shared airspace requires on-board safety mechanisms for self-separation without cooperation between platforms or centralised air-traffic services. This paper proposes the UAV Safety Bubble (USB), which is a six-layer geometric model that defines an adaptive safety envelope around the UAV. Each layer corresponds to a distinct physical contribution: platform dimensions, positioning uncertainty, communication performance, wind disturbance, detection-processing latency, and avoidance manoeuvrability. The model was evaluated through 5300 closed-loop Monte Carlo simulation instances across six conflict scenarios: static-obstacle avoidance; head-to-head encounters; 90°, 30°, and 60° approaches between two dynamic UAVs; and a non-reacting-intruder case. Platform, sensing, communication, and environmental parameters were sampled from uniform distributions across operational ranges. Under these simulation assumptions, no instance produced an intrusion into the fifth layer (USB5), which was the model’s operational separation boundary. The smallest clearance between the USB5 outer edge and the obstacle’s centre of mass was 35.36 m, recorded in the 90° approach scenario, and the one-sided 95% upper bound on the composite intrusion probability over the 1000 independent parameter sets was 0.299%. The adaptive envelope achieved this at per-scenario median route-length overheads of 15–46% relative to nominal straight-line routes. The USB complements downstream planners by providing an adaptive no-go region. The avoidance manoeuvres evaluated are restricted to the horizontal plane; full 3D avoidance is left for future work.
This study examines how multiple uncrewed aerial vehicles (UAVs) should allocate tasks and coordinate smoke deployment to interrupt line of sight under fast, spatially dispersed incoming threats. A three-dimensional simulation framework is developed that couples proportional-navigation-like reference threat motion, aer...
Zhuo Tan, Di Wu, Yi Peng et al.· IEEE Access· 0 citations
This paper proposes AERIS, an offline policy improvement framework for multi-UAV ISAC that learns from fixed flight logs under centralized training and decentralized execution and designs STAR-CRDT, an offline multi-agent RL algorithm that performs support-aware local action rectification and distills only trusted impr...
Ziyuan Wang, Yi-Fan Sui, Wei Wei et al.· 0 citations
A structured multi-UAV framework that separates mission optimisation into spatial, temporal, and safety layers is presented, indicating that integrating spatial planning, computation control, and safety coordination within a clearly separated layered architecture can provide an effective solution for multi-UAV disaster...
Rakan Armoush, Shidrokh Goudarzi, Muhammad Nadeem Khan et al.· Italian National Conference...· 0 citations
This paper investigates the pursuit-evasion game problem for multiple unmanned aerial vehicles (UAVs) and autonomous underwater vehicles (AUVs) in the presence of actuator faults and performance constraints. The main contribution is the development of a reinforcement learning (RL)-based fault-tolerant formation-surroun...
Hang Xiong, Ying Zhang· ISA transactions· 0 citations
Receding-Horizon Alternating Optimization (RHAO), a four-block per-slot algorithm that decomposes the problem into: charging admission via the Hungarian algorithm, MUAV trajectory and time-split via successive convex approximation, CUAV rendezvous, and WPT power allocation, with monotone convergence guarantees, is prop...
Masoud Shokrnezhad, T. Taleb, Amir Javadpour· 0 citations
Unmanned aerial vehicles (UAVs) are deployed across agriculture, disaster response, infrastructure inspection, and military operations, but flight testing is expensive, hazardous, and slow, which has made simulation the default environment for developing and validating UAV algorithms. Dozens of simulation platforms exi...