Collision-aware cooperative multi-UAV path planning with hierarchical PPO-LSTM
Neural Computing and Applications, vol.38, no.15, 2026 (Scopus)
- Publication Type: Article / Article
- Volume: 38 Issue: 15
- Publication Date: 2026
- Doi Number: 10.1007/s00521-026-12366-8
- Journal Name: Neural Computing and Applications
- Journal Indexes: Scopus, Aerospace Database, Applied Science & Technology Source, Compendex, Index Islamicus, INSPEC, zbMATH, Academic Search Ultimate (EBSCO), Engineering Source (EBSCO), Technology Collection (ProQuest)
- Keywords: Collision avoidance, Hierarchical reinforcement learning, Multi‑UAV coordination, Path planning
- Open Archive Collection: AVESIS Open Access Collection
- Istanbul University Affiliated: Yes
Abstract
Coordinating multiple unmanned aerial vehicles (UAVs) for inspection, delivery, and search-and-rescue requires routing strategies that are both efficient and safety-aware under partial observability. Conventional optimisation methods and single-level reinforcement-learning policies often struggle when fleet size and obstacle density increase, since local collision avoidance and long-horizon route coordination must be learned within the same primitive-action policy. We reformulate multi-UAV path planning as a hierarchical reinforcement-learning problem and propose a two-tier controller in which a recurrent PPO+LSTM Manager selects coarse waypoint-level decisions, while a shared recurrent PPO+LSTM Worker executes local collision-aware movements. The Worker is pre-trained and then frozen, allowing the recurrent Manager to learn macro-level coordination over a reduced decision horizon. Under idealised deterministic grid assumptions, we show that enlarging the waypoint dictionary weakly expands the feasible macro-action set and can preserve safe alternatives when such alternatives exist. Experiments on controlled grid scenarios with 2–6 UAVs and 20–40 % obstacle coverage show that hierarchical PPO+LSTM achieves 90–93 % mission success and collision rates of at most 10 %, outperforming flat PPO and PPO+LSTM baselines. The evaluation further includes reward-coefficient sensitivity analysis, high-resolution path-planning visualisations, a hard bottleneck feasibility illustration, MAPPO-style diagnostic convergence curves, and rollout-loop runtime diagnostics. Statistical validation using Welch’s t-tests supports the significance of the main performance improvements. The results indicate that separating waypoint-level strategy from recurrent local execution improves mission reliability and collision avoidance in the tested grid environments, while larger random-map benchmarks, fully controlled MAPPO/QMIX comparisons, and continuous 3-D simulation remain important future work.