A dynamic path planning method for low-altitude Unmanned Aerial Vehicles (UAVs) tailored for urban inspection missions and constrains the average response latency for high-priority emergency tasks to within 40 s even under 50 concurrent dynamic tasks is proposed.
Abstract
Traditional target-oriented task allocation and path planning methods often struggle to balance real-time responsiveness to dynamic task alterations with multi-UAV cooperative operations in complex urban environments under meteorological disturbances. To address these challenges, this paper proposes a dynamic path planning method for low-altitude Unmanned Aerial Vehicles (UAVs) tailored for urban inspection missions. Integrating an improved Discrete Particle Swarm Optimization (DPSO) algorithm with a decoupled Soft Actor–Critic (SAC) and B-spline smoothing framework, the proposed approach optimizes upper-level task allocation and lower-level trajectory planning within a 3D joint meteorological-obstacle feasible region. For task scheduling, an improved DPSO algorithm embedded with a spatial topology guidance mechanism dynamically coordinates task flows governed by Poisson processes. effectively addressing the spatial blindness and fragmented route assignments typical of conventional discrete optimization. Concurrently, local trajectory replanning executes receding-horizon spatial exploration via SAC deep reinforcement learning, followed by B-spline refinement to strictly enforce UAV kinematic limits, systematically bridging continuous-space exploration with low-level flight compliance to overcome the kinematic infeasibility common in pure learning-based models. Validated through extensive Monte Carlo comparative simulations (N=50) and further verified by a high-fidelity AirSim dynamic physics engine, the results demonstrate that: (1) The improved DPSO constrains the average response latency for high-priority emergency tasks to within 40 s even under 50 concurrent dynamic tasks. (2) The lower-level replanning achieves an average execution time of 3.60±0.18 s and a path success rate of 95.8±1.2%, in numerical tests, while maintaining a 96.2% kinematic feasibility rate under realistic rigid-body inertia and aerodynamic drag. While the current 3.60 s latency presents a potential bottleneck for millisecond-level dynamic emergency reactions, the developed framework offers a highly effective and safe closed-loop dynamic scheduling solution that lays a rigorous computational foundation for low-altitude urban inspections.
A Multi-UAV Planning Framework for Task Allocation, Route Optimization and Trajectory Smoothing for static environments with known obstacle geometry is presented and results demonstrate that the proposed hierarchical formulation is computationally effective, physically consistent, and well suited to multi-UAV mission planning.
M. Nikolaiev, M. Novotarskyi· International Journal of Wir...· 0 citations
A hierarchical three-dimensional cooperative trajectory-planning framework integrating dynamic-risk-aware Ant Colony Optimization with cooperative Model Predictive Control–Gray Wolf Optimizer (MPC-GWO) is proposed, demonstrating satisfactory safety, coordination, adaptability, and real-time performance in small- to medium-scale simulated urban scenarios.
Yuhan Wang, Pengfei Zhang, Ya-Wen Li et al.· Technologies· 0 citations
To address scheduling failures and collision-avoidance safety issues caused by the coupling of unexpected orders and complex three-dimensional threats in dynamic low-altitude logistics delivery, this study investigates a bi-level multi-objective trajectory planning problem that coordinates cloud-based global scheduling with UAV-side local collision avoidance. A Dual-Track Collaborative Architecture (DTCA) for dynamic trajectory planning is proposed. The architecture establishes a global–local dual-track multi-objective trajectory evaluation model for dynamic demands and develops a three-dimensional spatiotemporal corridor-based collaborative scheduling mechanism supported by a Ground-based Air Traffic Control (G-ATC) platform, thereby enabling closed-loop coordination between cloud-based resource optimization and onboard collision avoidance. A FAST-SPEA2 global dynamic scheduling algorithm incorporating a fuzzy inference mechanism is developed to improve replanning capability in response to unexpected orders, while a multi-subpopulation balanced HMOWOA algorithm is designed to enhance local trajectory optimization and safe collision avoidance in complex built environments. Experimental results demonstrate that the DTCA and its core algorithms achieve significant advantages in dynamic order response, global scheduling optimization, and local collision avoidance. Specifically, compared with the UAV-side HMOWOA-only scheme, the dual-track collaborative architecture reduces the average global fitness value by 12.62%. Compared with MOPSO, the second-best performer among 11 benchmark algorithms, FAST-SPEA2(DTCA) reduces the global mean fitness by 47.89%, with the statistical significance of the performance differences confirmed by Wilcoxon rank-sum tests across the evaluation metrics. The HMOWOA algorithm further achieves an average reduction of 1.46% in the local mean fitness compared with seven benchmark algorithms. In addition, ablation experiments show that the complete FAST-SPEA2 and HMOWOA algorithms achieve superior overall performance compared with their respective baseline algorithms and four single-strategy variants (S1–S4). These results demonstrate the effectiveness of the proposed DTCA in dynamic demand response, global resource scheduling, and safe collision avoidance in complex low-altitude environments.
Jian Deng, Honghai Zhang, Zong-Bei Shi et al.· Journal of King Saud Univers...· 0 citations
Cooperative navigation of multiple unmanned aerial vehicles (UAVs) in disaster search-and-rescue scenarios is challenging due to dense obstacles, partial observability, and strong inter-agent coupling, which often result in path conflicts, collision risks, and limited policy generalization. To address these challenges, this paper proposes a Multi-Agent Deep Deterministic Policy Gradient framework with a Graph-Attention-based Staged Actor (GS-MADDPG). Under a centralized training and decentralized execution paradigm, GNNs are employed to model local interaction relationships among UAVs, enabling effective information aggregation and cooperative decision-making under partial observability. Furthermore, the Actor network is decomposed into perception, goal-guidance, and feature fusion subnetworks, allowing hierarchical decoupling and coordinated integration of local obstacle avoidance behaviors and global navigation objectives. Simulation results conducted in a complex three-dimensional urban environment demonstrate that, compared to traditional methods, GS-MADDPG improves the navigation success rate, robustness, and generalization performance. When the obstacle density reaches 50% and the number of UAVs increases from 2 to 10, the navigation success rate of GS-MADDPG is approximately 40% higher than that of the benchmark algorithm; even in cases with higher obstacle density, GS-MADDPG still achieves a relatively high success rate. This verifies its effectiveness in multi-UAV cooperative navigation for search and rescue tasks.
Li Tan, Hai-Xia Zhao, Jia-Qin Chai et al.· Unmanned Systems· 0 citations
Urban low-altitude unmanned aerial vehicle (UAV) planning is inherently spatiotemporal because route feasibility and cost depend on segment arrival times. Search-stage surrogates may therefore favor paths that fail execution-level checks as moving obstacles, temporary no-fly zones, wind-dependent energy use, and building-clearance constraints evolve. We address this search–execution mismatch with the Risk-Aware Artificial Lemming Algorithm (RA-ALA), a three-layer framework governed by a common arrival-time-recursive evaluator. Sequential temporal propagation aligns candidate generation with final assessment, while an energy-weighted A* (Energy-A*) warm start guides continuous waypoint search. The Top-K stage then re-evaluates path variants before feasibility-first selection and conditional recovery. Under prespecified algorithm-specific budgets across 10 High-complexity environments, RA-ALA achieved the highest observed evaluator-feasible rate (24/30, 80.0%), 20 percentage points higher than Energy-A* and space–time Energy-A* (ST-EA*). After Holm adjustment, these contrasts were nonsignificant, while differences against Informed-RRT* and Greedy were supported. Within jointly feasible environments, RA-ALA retained competitive composite scores. Same-cohort descriptive ablation associated Top-K removal with higher composite scores and more infeasible outputs. These results support RA-ALA as a simulation-tested route-generation framework under the modeled constraints, without establishing isolated-operator superiority or real-flight readiness. Vehicle dynamics, sensing, tracking, communications, and flight validation remain outside this scope.
Kai-Jun Xu, Yi-Lin Hong, Hong-Da Luo et al.· Drones· 0 citations
Port traffic changes on a time scale that is poorly served by fixed sensors and precomputed routes. We therefore formulate vessel guidance as a repeatedly updated planning problem and use an unmanned aerial vehicle (UAV) to supply local observations when conventional sources are delayed or incomplete. Electronic navigational chart (ENC) constraints, automatic identification system (AIS) reports, and UAV detections are registered in a time-indexed representation of the navigation area. This representation drives an ECNA-based collision-risk term within the route optimizer, while a feedback loop revises only those route segments affected by new observations. In the simulation case, the resulting route is 0.57% longer than the geometric shortest path but is smoother and remains computable at millisecond scale. Adding UAV observations raises detection coverage from 72.0% to 96.4% and lowers the reported collision-risk index from 0.38 to 0.12. These results indicate that mobile aerial sensing can make port guidance more responsive without sacrificing online computational feasibility.
Kai-Nan Ma, Wei Pan· Scientific Journal of Intell...· 0 citations
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