KC-BFPRL: Knowledge-Guided Multi-UAV Collaboration for Grassland Restoration via Bilevel Formerpointer-Based Reinforcement Learning
2026-08-17 • Multiagent Systems
Multiagent Systems
AI summaryⓘ
The authors developed a new method to help a group of drones work together better when restoring large natural areas like grasslands. They broke the complex problem into smaller steps, using smart algorithms that consider both the environment and drone abilities. Their system uses advanced AI techniques to plan drone routes and tasks more efficiently, overcoming common training problems. Tests showed their method is faster and more accurate than existing ones, making it useful for real-time ecological work.
Unmanned Aerial Vehicles (UAV)Ecological RestorationCombinatorial OptimizationReinforcement LearningTransformer EncoderPointer NetworkActor-Critic FrameworkTask AllocationTrajectory PlanningHeuristic Logic
Authors
Dongbin Jiao, Xianyi Wang, Yuchen Yuan, Weibo Yang, Peng Yang, Peng Zhao, Zhanhuan Shang, Shi Yan
Abstract
Multi-unmanned aerial vehicle (UAV) systems provide scalable service platforms for large-scale environmental tasks, such as grassland ecosystem restoration. However, coordinating fleet operations requires solving the restoration area maximization problem (RAMP). This non-linear combinatorial optimization challenge is complicated by payload-dependent energy dynamics and heterogeneous ecological degradation. We propose a novel knowledge-guided collaborative bilevel formerpointer reinforcement learning framework (KC-BFPRL) to address this complexity. Using a hierarchical paradigm, KC-BFPRL decomposes RAMP into global task allocation and local restoration planning, with the latter further divided into upper-level trajectory planning and lower-level restoration area allocation. Our specialized architecture pairs featuring a Transformer-based encoder that fuses static environmental features with dynamic UAV states, and a Pointer Network decoder trained via a robust actor-critic framework. By embedding ecological priority rules and heuristic logic, KC-BFPRL achieves a structured warm-start, solving the RL cold-start problem while ensuring strict constraint satisfaction. Extensive experiments demonstrate that KC-BFPRL consistently outperforms state-of-the-art baselines, achieving superior objective values and efficiency. It maintains a $0.00\%$ optimality gap in the most complex scenarios U8-R160 and operates nearly three times faster than MAPDP, validating its robustness, scalability, and real-time applicability for large-scale automated ecological restoration.