Geometric priors for reinforcement learning-based multi-robot ecological monitoring

Autonomous ecological monitoring requires robotic systems capable of efficiently exploring large natural environments while minimizing redundant traversal under limited communication and partial environmental knowledge. Although reinforcement learning provides adaptive decision making for multi-robot exploration, sparse rewards often result in inefficient exploration and slow policy convergence. This paper investigates whether locality-preserving geometric priors can improve decentralized reinforcement learning for ecological monitoring. We incorporate Hilbert space-filling curve information through state augmentation, exploration biasing, and potential-based reward shaping while preserving the underlying reinforcement learning algorithms. The framework is implemented using Deep Q-Networks (DQN) and Proximal Policy Optimization (PPO) and evaluated in simulated multi-robot ecological survey environments using teams of 4-16 robots. Performance is evaluated using coverage ratio, redundancy, cumulative reward, convergence speed, and ablation studies. Policies are further validated through outdoor experiments using two Boston Dynamics Spot robots operating in an environment containing natural vegetation and terrain obstacles. Results demonstrate consistent improvements in exploration efficiency, survey coverage, redundancy reduction, and learning performance relative to conventional DQN and PPO, demonstrating the effectiveness of geometric priors for reinforcement learning-based autonomous ecological monitoring.