Vision-based interception using multicopters equipped strapdown camera is challenging due to camera-motion coupling and evasive targets. This article proposes a method integrating image-based visual servoing (IBVS) with proportional navigation guidance (PNG), reducing the multicopter’s overload in the final interception phase. It combines smoother trajectories from the IBVS controller with high-frequency target 2-D position estimation via a delayed Kalman filter (DKF) to minimize the impact of image processing delays on accuracy. In addition, a field-of-view (FOV) holding controller is designed for the stability of the visual servo system. Experimental results show a circular error probability (CEP) of 0.089 m (72.8% lower than the latest relevant IBVS work) in simulations and over 80% interception success under wind conditions below 4 m/s in real world. These results demonstrate the system’s potential for precise low-altitude interception of noncooperative targets.

We propose a high-quality and computationally efficient trajectory replanning method for Unmanned Aerial Vehicle (UAV) swarms, termed RHCR-Opt, which is designed to continuously and efficiently generate multiple collision-free trajectories in dense obstacle environments. RHCR-Opt consists of three layers. The first two layers are the rolling-horizon collision resolution (RHCR) algorithm based on the Conflict-Based Search (CBS), while the third layer focuses on trajectory generation and optimization using Minimum Control (MINCO) trajectories. Within the two-layer RHCR framework, the improved Lifelong Planning A* (LPA*) algorithm incorporating spatiotemporal constraints is proposed and employed as the low-level solver of CBS to satisfy the frequent search requirements for feasible paths under varying spatiotemporal constraints, thereby significantly improving computational efficiency. Furthermore, the rolling-horizon collision resolution concept is adopted in the high-level CBS framework, where only the discovery of collision-free paths within a finite time window is considered. This substantially reduces the computational burden associated with trajectory generation and optimization beyond the time window. At the third layer, a MINCO-based trajectory generation scheme is designed, and a swarm trajectory joint optimization framework with a finite time window is proposed to generate dynamically feasible and collision-free trajectories. In addition, for swarm missions requiring simultaneous arrival, a two-stage temporal coordination optimization method is developed. Extensive simulation experiments demonstrate that, compared with state-of-the-art (SOTA) algorithms on the proposed benchmark, RHCR-Opt achieves significant improvements in both trajectory quality and computational efficiency. In particular, when the swarm size becomes large, the computational efficiency is improved by at least 23.7%.

Rapid wildfire response requires UAV swarms to approach a fire region from dispersed initial states, coordinate their arrival with limited information exchange, and adapt terminal deployment to updated fireline geometry. This paper proposes a two-stage distributed guidance framework for time-coordinated fireline deployment. In Stage I, a regularized closing-time variable is combined with bounded-degree topological interaction, proximity-based collision avoidance, and persistent longitudinal speed regulation to reduce rendezvous-time dispersion. In Stage II, the updated fireline is discretized into service points and assigned using a geometry-aware structured strategy, followed by distance-aware terminal synchronization and local safety control. Conditional analysis establishes convergence of the reduced Stage-I coordination dynamics under admissible switching topologies and sufficient separation conditions for ideal Stage-II deployment. In the 12-UAV Stage-I comparison, the proposed method reduces the arrival-time spread to 1.34 s, compared with 16.12 s for fixed-topology cooperative guidance and 4.20 s without longitudinal timing correction. Stage-I tests are further extended to 100 UAVs, while Stage-II assignment is evaluated with up to 60 UAVs and maintains low computational cost. In matched three-dimensional tests involving measurement noise, wind disturbance, and bounded communication delay, the proposed method achieves the smallest terminal timing error among the compared strategies, with a mean Stage-II spread of 2.654 s over the four tested conditions. The results demonstrate effective and scalable time-coordinated deployment under the considered kinematic and communication conditions.