PPCPP: A Predator-Prey-Based Approach to Adaptive Coverage Path Planning

Hassan, M; Liu, D

PPCPP: A Predator-Prey-Based Approach to Adaptive Coverage Path Planning

Hassan, M

Liu, D

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Robotics, 2020, 36 (1), pp. 284 - 301
Issue Date:: 2020-02-01

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Field	Value	Language
dc.contributor.author	Hassan, M https://orcid.org/0000-0002-0824-3272	en_US
dc.contributor.author	Liu, D https://orcid.org/0000-0002-1581-5582	en_US
dc.date.issued	2020-02-01	en_US
dc.identifier.citation	IEEE Transactions on Robotics, 2020, 36 (1), pp. 284 - 301	en_US
dc.identifier.issn	1552-3098	en_US
dc.identifier.uri	http://hdl.handle.net/10453/138103
dc.description.abstract	© 2004-2012 IEEE. Most of the existing coverage path planning (CPP) algorithms do not have the capability of enabling a robot to handle unexpected changes in the coverage area of interest. Examples of unexpected changes include the sudden introduction of stationary or dynamic obstacles in the environment and change in the reachable area for coverage (e.g., due to imperfect base localization by an industrial robot). Thus, a novel adaptive CPP approach is developed that is efficient to respond to changes in real-time while aiming to achieve complete coverage with minimal cost. As part of the approach, a total reward function that incorporates three rewards is designed where the first reward is inspired by the predator-prey relation, the second reward is related to continuing motion in a straight direction, and the third reward is related to covering the boundary. The total reward function acts as a heuristic to guide the robot at each step. For a given map of an environment, model parameters are first tuned offline to minimize the path length while assuming no obstacles. It is shown that applying these learned parameters during real-time adaptive planning in the presence of obstacles will still result in a coverage path with a length close to the optimized path length. Many case studies with various scenarios are presented to validate the approach and to perform numerous comparisons.	en_US
dc.relation.ispartof	IEEE Transactions on Robotics	en_US
dc.relation.isbasedon	10.1109/TRO.2019.2946891	en_US
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject.classification	Industrial Engineering & Automation	en_US
dc.title	PPCPP: A Predator-Prey-Based Approach to Adaptive Coverage Path Planning	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	36	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	0913 Mechanical Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CAS - Centre for Autonomous Systems
utslib.copyright.status	open_access	*
utslib.copyright.embargo	2021-11-27T00:00:00+1100
pubs.issue	1	en_US
pubs.publication-status	Published	en_US
pubs.volume	36	en_US

Abstract:

© 2004-2012 IEEE. Most of the existing coverage path planning (CPP) algorithms do not have the capability of enabling a robot to handle unexpected changes in the coverage area of interest. Examples of unexpected changes include the sudden introduction of stationary or dynamic obstacles in the environment and change in the reachable area for coverage (e.g., due to imperfect base localization by an industrial robot). Thus, a novel adaptive CPP approach is developed that is efficient to respond to changes in real-time while aiming to achieve complete coverage with minimal cost. As part of the approach, a total reward function that incorporates three rewards is designed where the first reward is inspired by the predator-prey relation, the second reward is related to continuing motion in a straight direction, and the third reward is related to covering the boundary. The total reward function acts as a heuristic to guide the robot at each step. For a given map of an environment, model parameters are first tuned offline to minimize the path length while assuming no obstacles. It is shown that applying these learned parameters during real-time adaptive planning in the presence of obstacles will still result in a coverage path with a length close to the optimized path length. Many case studies with various scenarios are presented to validate the approach and to perform numerous comparisons.

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http://hdl.handle.net/10453/138103