Experimental Evaluation of a Hierarchical Operating Framework for Ground Robots in Agriculture
This work addresses the challenge of deploying ground robots for large-scale farming, though it appears incremental as an empirical evaluation of a proposed framework.
The authors tackled the problem of enabling mobile robots to operate effectively in unstructured agricultural environments by evaluating a hierarchical framework for long-term autonomy on the Swagbot platform. They demonstrated the framework's ability to navigate dynamic settings, validating its use for tasks like autonomous weeding with moving individuals.
For mobile robots to be effectively applied to real world unstructured environments -- such as large scale farming -- they require the ability to generate adaptive plans that account both for limited onboard resources, and the presence of dynamic changes, including nearby moving individuals. This work provides a real world empirical evaluation of our proposed hierarchical framework for long-term autonomy of field robots, conducted on University of Sydney's Swagbot agricultural robot platform. We demonstrate the ability of the framework to navigate an unstructured and dynamic environment in an effective manner, validating its use for long-term deployment in large scale farming, for tasks such as autonomous weeding in the presence of moving individuals.