A Hierarchical Framework for Long-term and Robust Deployment of Field Ground Robots in Large-Scale Farming
Stuart Eiffert, Nathan D. Wallace, He Kong, Navid Pirmarzdashti, and, Salah Sukkarieh

TL;DR
This paper presents a hierarchical framework for deploying ground robots in large-scale farming, enabling long-term, resource-efficient, and robust operation amidst dynamic environments with moving obstacles and terrain challenges.
Contribution
The paper introduces a novel hierarchical framework combining local dynamic path planning, long-term objective planning, and advanced motion control for robust farm robot deployment.
Findings
Successfully demonstrated in simulated trials with tasks like soil sampling and recharging.
Robust adaptation to moving obstacles and terrain undulations.
Enhanced long-term autonomy in dynamic farm environments.
Abstract
Achieving long term autonomy of robots operating in dynamic environments such as farms remains a significant challenge. Arguably, the most demanding factors to achieve this are the on-board resource constraints such as energy, planning in the presence of moving individuals such as livestock and people, and handling unknown and undulating terrain. These considerations require a robot to be adaptive in its immediate actions in order to successfully achieve long-term, resource-efficient and robust autonomy. To achieve this, we propose a hierarchical framework that integrates a local dynamic path planner with a longer term objective based planner and advanced motion control methods, whilst taking into consideration the dynamic responses of moving individuals within the environment. The framework is motivated by and synthesizes our recent work on energy aware mission planning, path planning…
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