  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.
