Mission Planning for Field Robots using Symbolic Planning and Topology

Author: Jonathan Wheare

  • Thesis download: available for open access on 15 Feb 2020.

Wheare, Jonathan, 2018 Mission Planning for Field Robots using Symbolic Planning and Topology, Flinders University, College of Science and Engineering

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Abstract

Field robotics is an area of research that takes the discipline of robotics from the confines

of the laboratory into the unstructured and complex environment of the real world. Planning

and guidance systems have been developed to allow field robotic platforms to operate in

unstructured environments, but the limited amount of computing resources has constrained

the ability of field platforms to dynamically replan their missions. Domain specific planning

systems for path planning provide the efficiency that is required to handle large and com-

plex environments, but deliberative higher level mission planning systems typically use a

domain independent planner to find a solution to the vehicle’s task. As such, mission plan-

ners lack understanding of their spatial environment. This thesis chronicles the development

of a belief compression method using topological thinning to simplify the spatial environment

sufficiently for it to be solved by a domain independent planner allowing a vehicle’s mission to

be planned using information about its spatial environment. Algorithms are evaluated using

both simulated and real-world data showing that topological thinning can produce compact

domains while maintaining a high level of routing efficiency, enabling the solution of the

high-level mission planning problem. This thesis also examines the properties of topologi-

cal belief compression and the effectiveness of path planning with non-uniform action costs

using domain independent planners. To demonstrate the effectiveness of these algorithms,

a planning and guidance system is tested on an Autonomous Surface Vessel (ASV) built

around a five-metre Wave Adaptive Modular Vehicle platform (WAM-V). When performing

simulated rescue tasks for 20 survivors before returning to a dock, the Symbolic With Re-

finement planner demonstrated plan generation resulting in a mean reduction in path length

of approximately 15% when compared to a Greedy planning system.

Keywords: Robotics, Maritime Robotics, Autonomous Maritime Vehicles, Autonomy

Subject: Engineering thesis

Thesis type: Doctor of Philosophy
Completed: 2018
School: College of Science and Engineering
Supervisor: Professor Karl Sammut