Currently, robotic technology offers flexible platforms for addressing many challenging problems that arise in extreme environments. These problems’ nature enhances
the use of heterogeneous multi-vehicle systems which can coordinate and collaborate
to achieve a common set of goals. While such applications have previously been
explored in limited contexts, long-term deployments in such settings often require
an advanced level of autonomy to maintain operability.
The success of planning and acting approaches for multi-robot systems are conditioned by including reasoning regarding temporal, resource and knowledge requirements, and world dynamics. Automated planning provides the tools to enable intelligent behaviours in robotic systems. However, whilst many planning approaches and
plan execution techniques have been proposed, these solutions highlight an inability
to consistently build and execute high-quality plans.
Motivated by these challenges, this thesis presents developments advancing state-of-the-art temporal planning and acting to address multi-robot problems. We propose a set of advanced techniques, methods and tools to build a high-level temporal
planning and execution system that can devise, execute and monitor plans suitable for long-term missions in extreme environments. We introduce a new task
allocation strategy, called HRTA, that optimises the task distribution amongst the
heterogeneous fleet, relaxes the planning problem and boosts the plan search. We
implement the TraCE planner that enforces contingent planning considering propositional temporal and numeric constraints to deal with partial observability about
the initial state. Our developments regarding robust plan execution and mission
adaptability include the HLMA, which efficiently optimises the task allocation and
refines the planning model considering the experience from robots’ previous mission
executions. We introduce the SEA failure solver that, combined with online planning, overcomes unexpected situations during mission execution, deals with joint
goals implementation, and enhances mission operability in long-term deployments.
Finally, we demonstrate the efficiency of our approaches with a series of experiments
using a new set of real-world planning domains.Engineering and Physical Sciences Research Council (EPSRC) grant EP/R026173/
