  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 in dynamic environments, and
receding horizon motion control. In this paper we detail the proposed framework
and outline its integration on a robotic platform. We evaluate the strategy in
extensive simulated trials, traversing between objective waypoints to complete
tasks such as soil sampling, weeding and recharging across a dynamic
environment, demonstrating its capability to robustly adapt long term mission
plans in the presence of moving individuals and obstacles for real world
applications such as large scale farming.
