This paper addresses the challenge of simultaneous arrival for UAV clusters and proposes a route planning method based on an enhanced Non-dominated Sorting Genetic Algorithm III (NSGA-III). Initially, the paper defines the simultaneous arrival problem and formulates the corresponding mathematical model, considering the complexity of multi-objective optimization in UAV clusters. A novel path generation framework is introduced, which incorporates multiple optimization objectives—such as time coordination, threat mitigation, and resource consumption—aimed at improving flight safety, efficiency, and resource management. To enhance the algorithm’s search performance, a hybrid approach combining the Artificial Bee Colony (ABC) algorithm with NSGA-III is proposed. This improved NSGA-III strategy overcomes the limitations of the original algorithm in managing complex constraints and multi-objective optimization problems, resulting in significant improvements in search accuracy and convergence speed. Finally, the performance of the improved algorithm is evaluated through simulations and compared with traditional methods. The results show that the proposed approach optimizes flight time, reduces resource consumption, and effectively mitigates threats, all while ensuring the simultaneous arrival of UAV clusters.

This study proposes a heterogeneous fusion path planning framework for unmanned aerial vehicles (UAVs) operating in complex emergency rescue and civil environments. Existing single-mechanism metaheuristics—including Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Genetic Algorithms (GAs)—suffer from fundamental limitations in three-dimensional kinematic path planning: PSO converges rapidly but stagnates at local optima due to population variance collapse; ACO offers robust local exploitation but incurs prohibitive cold-start overhead; GAs maintain diversity at the cost of expensive crossover operations. To address these complementary deficiencies simultaneously, the proposed framework introduces a spherical coordinate representation that reduces computational complexity and naturally enforces UAV kinematic constraints, combined with adaptive weight factors and a serial PSO-ACO fusion strategy, and subsequently incorporates adaptive weight factors. A serial fusion strategy is then introduced, wherein the sub-optimal trajectory generated by the Spherical PSO phase is mapped into the ACO pheromone field via a Gaussian Kernel Density Mapping (GKDM) mechanism, enabling the ACO phase to perform fine-grained local exploitation within a kinematically feasible corridor. Various constraints along the flight path are formulated into distinct cost functions, which cover aircraft track length, pitch angle variation, altitude difference variation, obstacle avoidance, and smoothness; the core task of the algorithm is to find the flight path with the minimum total cost. The proposed algorithm is dedicated to UAV path planning in complex emergency rescue environments (disaster-stricken areas, hazardous zones) and is further applicable to civil low-altitude logistics delivery, industrial facility inspection, ecological environment monitoring and urban air mobility (UAM) scenarios with complex obstacle constraints. It can effectively improve the safety and efficiency of UAVs in reaching rescue points, delivering emergency supplies, conducting disaster surveys, and completing various civil low-altitude operation tasks.

Three-dimensional UAV operations require path planning methods that can jointly maintain route efficiency, threat avoidance, and trajectory smoothness under spatially distributed and time-varying constraints. To address this problem, this paper develops an integrated Dual-Layer Adaptive T-perturbation and Opposition-based Multi-Objective Particle Swarm Optimization framework, termed DATO-MOPSO, for 3D UAV path planning in complex threat environments. The method integrates a dual-layer adaptive inertia-weight and velocity-regulation mechanism with symmetric T-perturbation, an elite quasi-opposition-based learning strategy for diversity recovery and feasible local exploitation, and an archive-driven simulated annealing rule for stagnation-aware personal-best updating. A three-objective model minimizing path length, threat exposure, and path smoothness is established, and comparative experiments against MOPSO, ZAMOPSO, NSGA-II, and SPEA2 are conducted in both static and dynamic environments, together with statistical and ablation analyses. In the static scenario, DATO-MOPSO achieved the highest mean HV and stable repeated-run performance, but its IGD was comparable to ZAMOPSO with higher computational cost. In the dynamic scenario, DATO-MOPSO showed its main advantage, achieving the highest mean HV and the lowest mean IGD with statistically significant HV and IGD improvements over all baselines. Overall, DATO-MOPSO is most advantageous in time-varying complex threat environments, whereas its static-scenario advantages are accompanied by higher computational cost.

The integration of unmanned aerial vehicles (UAVs) into low-altitude urban airspace for parcel delivery and last-mile logistics is emerging as a key component of future urban air mobility, yet its operational viability depends on autonomous path planning that is simultaneously safe, reliable, and efficient under dense obstacles and limited sensing. Deep reinforcement learning offers a promising planning paradigm, but its deployment in this transport setting is severely constrained by sparse task-level success signals, low sample efficiency, and a poorly regulated exploration–exploitation trade-off. To address these issues, this paper proposes an improved Soft Actor–Critic (SAC) algorithm named SIL-CAT-SAC, which integrates self-imitation learning (SIL) and a curiosity-adaptive temperature mechanism. Specifically, a curiosity-augmented target entropy adjustment (CAT) module based on enhanced random network distillation is designed to dynamically regulate the exploration intensity according to state novelty, achieving instance-level adaptive temperature control; a linearly annealed curiosity coefficient further prevents intrinsic rewards from overriding the delivery objective near convergence. A self-imitation learning auxiliary loss combined with prioritized experience replay is introduced to selectively amplify genuine successful trajectories, eliminating the interference of pseudo-success samples from hindsight experience replay and markedly improving the utilization of rare positive samples. In addition, a continuous reward function incorporating distance increment, potential shaping, and obstacle warning terms is constructed to alleviate gradient vanishing under sparse success feedback. Comprehensive experiments are conducted in a $35\times 35$ grid-based urban path planning environment across three obstacle densities, against two state-of-the-art SAC variants, REDQ and M-SAC. All reported metrics are averaged over three independent training seeds; where the dispersion across seeds is meaningful, the cross-seed standard deviation is reported together with the mean. The results show that, in a frozen policy evaluation against the fixed goal, SIL-CAT-SAC attains the highest success rate at each of the three obstacle densities, and the exact Friedman test rejects the null hypothesis of equal performance at the two higher densities. It reaches its operating success level in fewer training episodes than either baseline, and it exhibits the largest average robustness index among the three algorithms. A factorial ablation at the lowest density, where none of the four configurations reaches the success ceiling, separates the component combinations: removing either component lowers the success rate and delays convergence, while the joint effect is no larger than the sum of the individual effects, so both components contribute and no positive CAT-SIL interaction is claimed. The study is conducted in a two-dimensional grid-based abstraction with static obstacles; three-dimensional flight dynamics, dynamic obstacles, wind disturbance, sensing uncertainty, and communication constraints are outside the scope of the present evaluation, and experiments extending the framework to three-dimensional environments are underway and will be reported in a follow-up paper. Within this simulated two-dimensional grid-based setting, the results support a success rate and convergence efficiency advantage for the proposed method at every density, as well as a cross-density robustness advantage at the intermediate density; the advantage is attributed to the two components acting in combination rather than to a demonstrated positive interaction between them. Validation in three-dimensional airspace with dynamic obstacles, sensor uncertainty, and realistic vehicle dynamics remains necessary before operational deployment can be claimed.