Occupancy Map Abstraction for Higher Level Mission Planning of Autonomous Robotic Exploration in Hazardous Nuclear Environments

Batty, David, Manes, Lupo ORCID: 0000-0002-5426-2925, West, Andrew, Patel, Maulik ORCID: 0000-0002-3214-5752, Caliskanelli, Ipek and Paoletti, Paolo ORCID: 0000-0001-6131-0377 (2023) Occupancy Map Abstraction for Higher Level Mission Planning of Autonomous Robotic Exploration in Hazardous Nuclear Environments. .

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Abstract

In the nuclear industry, the need for improved reliability in current and future technology hinders the deployment of autonomous robotic systems. The following research aims to develop a method of reliably mapping a large environment and abstracting the map into a sparse node graph to create a more efficient data form. The proposed data form allows for efficient storage whilst maintaining important map features and coverage. The method utilises an expanding node algorithm to convert standard occupancy maps to a sparse node graph representation. The algorithm’s effectiveness has been tested on simulated maps and real-world maps to test the compression factor for a wide range of scenarios. The algorithm is expanded to function on a semi-unknown map abstracting during exploration.

Item Type:	Conference Item (Unspecified)
Uncontrolled Keywords:	4605 Data Management and Data Science, 46 Information and Computing Sciences
Divisions:	Faculty of Science and Engineering > School of Engineering
Depositing User:	Symplectic Admin
Date Deposited:	17 Oct 2023 07:37
Last Modified:	09 Jun 2025 15:12
DOI:	10.1007/978-3-031-43360-3_7
Related Websites:	Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3173777