Work

Multi-objective hierarchical reinforcement learning for efficient and safe robotic manure cleaning in dairy barns

Year: 2026

Type: article

Abstract: Autonomous mobile robots for manure removal in dairy barns must resolve conflicting objectives of cleaning efficiency, environmental impact, and animal welfare under highly cluttered and dynamic barn conditions. We introduce a Multi-Objective Hierarchical Reinforcement Learning (MO-HRL) framework that integrates a Global Evolutionary Scheduler (GES) for long-horizon cleaning task planning with an Adaptive RL-based Executor (ARE) for real-time, risk-aware robot motion control. The GES solves a multi-objective optimization problem over the robot’s travel distance, ammonia emissions, and cleaning yield via an evolutionary algorithm. The ARE employs a constrained Markov decision process enriched with historical high-loss weighting, adaptive action masking, and a final execution shield to guarantee smooth, non-injurious contacts with cows. In Webots simulations across three barn layouts (with ten, twenty, and thirty free-roaming cows, respectively), MO-HRL maintains high task completion ( ≥ 93 . 03 % ) with low comfort-distance violations ( ≤ 6 . 67 % ), achieves a peak throughput of 1 . 98 kg/min , and shortens completion time by up to 6× versus PPO (up to 8× in Small). Ammonia emission reduction reaches up to 0.144 kg m − 3 . These results indicate that hierarchical, risk-aware RL can translate long-horizon scheduling and real-time safety constraints into tangible gains for rapid, safe, and sustainable barn sanitation.

Source: Computers and Electronics in Agriculture

DOI: 10.1016/j.compag.2026.111749

Authors: Wei Wei, Congcong Sun, Lin Wu, Jianglin Lan

Institutions: University of Glasgow, Wageningen University & Research

Language: English

Topic: Smart Agriculture and AI

Subfield: Plant Science

Field: Agricultural and Biological Sciences

Domain: Life Sciences

Open Access status: hybrid

Estimated APC paid: $3,870

Funder: Leverhulme Trust