Unified-Evaluation-Driven RA-ALA for Three-Dimensional UAV Path Planning in Time-Varying Urban Low-Altitude Environments
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.