Quantum-Inspired Algorithms Teach Fixed-Wing Drone Swarms to Rescue Boats at Sea

When a vessel goes down in open water, minutes decide who survives. Rescue coordinators must scatter life rafts, emergency supplies, and spotters around a target that is itself drifting with the waves and wind, all while the aircraft they depend on fight the stubborn physics of flight. A new study published in the International Journal of Aeronautical and Space Sciences proposes a way to make that chaos orderly: a two-layer framework that lets a team of heterogeneous fixed-wing unmanned aerial vehicles (FW-UAVs) decide, on their own, who delivers what, from which direction, and exactly how to get there at the same moment. The work, led by Muhammad Imran Baig of Nanjing University of Aeronautics and Astronautics together with Ziyang Zhen and Umair Javaid of Ningbo University of Technology, tackles one of the most stubborn coordination problems in autonomous aviation.

The difficulty begins with the aircraft themselves. Unlike multirotor drones, which can hover, pivot in place, and creep toward a target, fixed-wing UAVs must keep moving forward to stay aloft. They cannot stop, and their turning radii are bounded by aerodynamic reality: bank too sharply and the aircraft stalls or exceeds structural limits. That means every approach to a rescue zone is a commitment. A drone that lines up from the wrong heading may need a wide, time-consuming arc to correct itself, and in a rescue scenario, those seconds compound. Add a moving target, several aircraft with different capabilities and payloads, and the requirement that resources arrive from multiple directions simultaneously to maximize the chance of reaching survivors spread across the water, and the problem becomes a combinatorial nightmare.

Formally, the researchers frame each rescue task as a specific approach direction, or AD, for an individual FW-UAV to deploy its resources. Assigning those directions across a heterogeneous fleet is a high-dimensional optimization problem with a multimodal landscape, meaning the search space is studded with many locally good solutions that can trap conventional algorithms. Classical genetic algorithms and similar evolutionary methods often stagnate in these traps, converging on a decent but far-from-optimal assignment plan while better configurations sit unexplored elsewhere in the solution space. For a time-critical operation like maritime rescue, stagnation is not just an inefficiency; it is a direct cost in human survival probability.

To break out of those traps, the team built an adaptive quantum-inspired genetic algorithm, or AQIGA, as the upper layer of their framework. Quantum-inspired evolutionary algorithms, first popularized in the early 2000s, borrow a mathematical vocabulary from quantum mechanics without using any actual quantum hardware. Instead of encoding candidate solutions as fixed strings, they encode each element as a pair of probability amplitudes, conceptually akin to a qubit existing in a superposition of the zero and one states. A population of these probabilistic representations is measured to produce concrete solutions, evaluated, and then updated by rotation gates that shift the probabilities toward promising regions. The effect is that a single individual can implicitly represent multiple candidate configurations at once, giving the search a richer exploration dynamic than a standard genetic algorithm of the same population size.

The adaptive part of AQIGA is where the new work earns its name. The algorithm continuously monitors two statistics of its population: entropy, a measure of how varied the solutions are, and diversity, a measure of how spread out the population is across the search space. A dynamic feedback loop based on these metrics regulates how aggressively the algorithm explores versus exploits. When the population collapses toward uniformity, a signature of premature convergence, the feedback mechanism pushes the probability updates to broaden the search again; when a healthy spread is present, it tightens focus around the best candidates. This self-regulation is designed specifically to prevent the local optima stagnation that plagues cooperative task assignment problems with many near-equivalent solutions.

Deciding who goes where is only half the battle. The lower layer of the framework is a vector field guidance model, VFGM, that converts each abstract assignment into a flyable, collision-free trajectory. Vector field guidance has a respected lineage in autonomous flight: Lyapunov vector fields, described in seminal work in the Journal of Guidance, Control, and Dynamics in 2008, have long been used for standoff tracking of moving targets, where an aircraft follows a smooth vector field that spirals it onto a desired orbit. The VFGM here extends that idea to a cooperative arrival problem. It steers each FW-UAV along its assigned approach direction while respecting the aircraft’s continuous-forward-motion requirement and limited turning radius, deconflicting the trajectories so that multiple drones converge on the rescue area from different headings at the same time without crossing paths dangerously close to one another or to the distressed vessel.

The simultaneous multidirectional arrival is the crux of the rescue logic. Survivors in the water scatter, and the drifting life raft or the distressed vehicle may have people clinging to different sides. Resources dropped from a single heading cover only a limited arc, and in rough seas, timing matters as much as placement. By synchronizing arrival from multiple directions, the fleet blankets the area rather than lining it. The decentralized nature of the VFGM also matters operationally: rather than a single ground station computing every path for every aircraft, each UAV executes its own guidance in real time, which makes the system more robust to communication dropouts, a serious concern over open ocean where satellite links are the only option and bandwidth is precious.

To find out whether the design actually works, the researchers tested it in simulated maritime rescue scenarios featuring moving target vehicles, and benchmarked the AQIGA against conventional quantum-inspired algorithms under the same conditions. The results were consistent and, in places, striking. On average, the full AQIGA-VFGM framework improved solution fitness by 13.23 percent, meaning the task assignments it produced were substantially better by the combined objectives of the mission. It cut convergence time by 37.36 percent, which in a rescue context translates directly into faster planning when every minute of drift separates survivors from rescuers. And it improved what the authors call excess resource fairness by 10.88 percent, a metric reflecting how evenly the burden of resource deployment is distributed across the fleet, preventing scenarios where one or two aircraft are overloaded while others cruise underutilized.

Those three numbers tell a coherent story. Fitness improvement means the plans are better; convergence improvement means they arrive sooner; fairness improvement means the fleet’s collective capacity is actually used. The authors argue that together these gains make the framework promising and practically feasible for time-critical multi-UAV maritime rescue operations. It is worth noting, as the study itself does, that the validation so far is in simulation. Moving from simulated moving-target scenarios to real fixed-wing aircraft over real seas will demand field trials that stress communication links, sensor error, weather, and the safety case for autonomous drops near people in the water. The lineage of the underlying guidance methods, however, is well established in flight trials elsewhere, which gives the simulation results a credible foundation.

The broader significance stretches beyond maritime rescue. The core architecture, a quantum-inspired adaptive optimizer handing assignments to a decentralized vector field guidance layer, is modular by design. The same pattern could apply to coordinated wildfire suppression drops, distributed package delivery, disaster inspection with mixed drone fleets, or any mission where heterogeneous fixed-wing vehicles must divide spatially structured tasks and execute them in concert against moving targets. The authors’ own bibliography traces a decade of progress in cooperative task assignment, from multi-type genetic algorithms for heterogeneous UAV teams to consensus-based dynamic assignment under uncertainty, and their contribution slots neatly into that trajectory by attacking the two constraints that fixed-wing platforms impose and that rotorcraft-centric research has often been able to ignore. As drone fleets grow larger and missions grow more complex, frameworks like this one, which marry a principled optimization scheme with flight-physics-aware execution, may become the standard way that swarms of ordinary, aerodynamically constrained aircraft manage to behave like an extraordinary, coordinated team.

Subject of Research: Cooperative task assignment and execution for heterogeneous fixed-wing UAV swarms in maritime rescue

Article Title: Cooperative Task Assignment and Execution for Heterogeneous Fixed-Wing UAVs in Maritime Rescue

Article References: Baig, M. I., Zhen, Z., & Javaid, U. (2026). Cooperative Task Assignment and Execution for Heterogeneous Fixed-Wing UAVs in Maritime Rescue. International Journal of Aeronautical and Space Sciences. https://doi.org/10.1007/s42405-026-01273-w

Image Credits: AI Generated

DOI: 10.1007/s42405-026-01273-w

Keywords: fixed-wing UAVs, maritime rescue, quantum-inspired genetic algorithm, vector field guidance, task assignment, multi-UAV coordination, heterogeneous drone fleet, simultaneous arrival, optimization, autonomous flight, search and rescue, trajectory planning