Learning to Spray in Uncertain and Windy Environments

Deploying robotic systems for precision spraying offers a sustainable alternative to blanket spraying in agriculture. However, existing systems typically assume deterministic kinematics and perfect observations, ignoring the inherent stochasticity of agricultural fields such as unpredictable wind patterns, sensor noise, and variable spray-dispersion dynamics. To bridge this gap, we introduce a novel stochastic multi-robot environment for precision spraying that explicitly models real-world uncertainties: stochastic wind, actuation noise, observation noise, and probabilistic spray decay. We design a spraying mechanism based on wind-dependent Gaussian falloff that models advective spray drift and anisotropic plume elongation caused by variable wind patterns, and calibrate its parameters against published field measurements of UAV spray deposition. We formulate the task as a constrained stochastic optimization problem for coordinating robots to treat infected locations under uncertainty while minimizing path lengths and avoiding collisions. We constructed both centralized (single-agent) and decentralized (multi-agent) POMDPs that share the same global state space, dynamics, and reward function for performing this task. Our environment is compatible with standard single-agent and multi-agent deep Reinforcement Learning (RL) methods and serves as a rigorous benchmark for robust agricultural coordination, which can be modified or extended for other similar applications with real-world uncertainties. We evaluated centralized and decentralized deep RL methods and a model-based controller across multiple environment variations, demonstrating the feasibility of learning effective policies under various uncertainties.