Motion Planning for ReconfigurableMobile Robots Using Hierarchical FastMarching Trees

Reid, W; Fitch, R; Goktogan, AH; Sukkarieh, S

Motion Planning for ReconfigurableMobile Robots Using Hierarchical FastMarching Trees

Reid, W Fitch, R

Goktogan, AH Sukkarieh, S

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Publisher:: WAFR
Publication Type:: Conference Proceeding
Citation:: Website proceedings of the 12th Workshop on the Algorithmic Foundations of Robotics, 2016, pp. 1 - 16
Issue Date:: 2016-12

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Field	Value	Language
dc.contributor.author	Reid, W	en_US
dc.contributor.author	Fitch, R https://orcid.org/0000-0003-2215-5188	en_US
dc.contributor.author	Goktogan, AH	en_US
dc.contributor.author	Sukkarieh, S	en_US
dc.date	2016-12-18	en_US
dc.date.issued	2016-12	en_US
dc.identifier.citation	Website proceedings of the 12th Workshop on the Algorithmic Foundations of Robotics, 2016, pp. 1 - 16	en_US
dc.identifier.uri	http://hdl.handle.net/10453/99720
dc.description.abstract	Reconfigurable mobile robots are versatile platforms that may safely traverse cluttered environments by morphing their physical geometry. However, planning paths for these robots is challenging due to their many degrees of freedom. We propose a novel hierarchical variant of the Fast Marching Tree (FMT) algorithm. Our algorithm assumes a decomposition of the full state space into multiple sub-spaces, and begins by rapidly finding a set of paths through one such sub-space. This set of solutions is used to generate a biased sampling distribution, which is then explored to find a solution in the full state space. This technique provides a novel way to incorporate prior knowledge of sub-spaces to ef- ficiently bias search within the existing FMT framework. Importantly, probabilistic completeness and asymptotic optimality are preserved. Experimental results are provided for a reconfigurable wheel-on-leg platform that benchmark the algorithm against state-of-the-art samplingbased planners. In minimizing an energy objective that combines the mechanical work required for platform locomotion with that required for reconfiguration, the planner produces intuitive behaviors where the robot dynamically adjusts its footprint, varies its height, and clambers over obstacles using legged locomotion. These results illustrate the generality of the planner in exploiting the platform’s mechanical ability to fluidly transition between various physical geometric configurations, and wheeled/legged locomotion modes.	en_US
dc.publisher	WAFR	en_US
dc.relation.ispartof	Website proceedings of the 12th Workshop on the Algorithmic Foundations of Robotics	en_US
dc.relation.ispartof	Workshop on the Algorithmic Foundations of Robotics (WAFR)	en_US
dc.title	Motion Planning for ReconfigurableMobile Robots Using Hierarchical FastMarching Trees	en_US
dc.type	Conference Proceeding
utslib.location	USA	en_US
utslib.location.activity	San Francisco, USA	en_US
utslib.for	080101 Adaptive Agents and Intelligent Robotics	en_US
utslib.for	091303 Autonomous Vehicles	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/Strength - CAS - Centre for Autonomous Systems
utslib.copyright.status	closed_access
pubs.consider-herdc	true	en_US
pubs.finish-date	2016-12-20	en_US
pubs.place-of-publication	USA	en_US
pubs.publication-status	Published online	en_US
pubs.start-date	2016-12-18	en_US

Abstract:

Reconfigurable mobile robots are versatile platforms that may safely traverse cluttered environments by morphing their physical geometry. However, planning paths for these robots is challenging due to their many degrees of freedom. We propose a novel hierarchical variant of the Fast Marching Tree (FMT*) algorithm. Our algorithm assumes a decomposition of the full state space into multiple sub-spaces, and begins by rapidly finding a set of paths through one such sub-space. This set of solutions is used to generate a biased sampling distribution, which is then explored to find a solution in the full state space. This technique provides a novel way to incorporate prior knowledge of sub-spaces to ef- ficiently bias search within the existing FMT* framework. Importantly, probabilistic completeness and asymptotic optimality are preserved. Experimental results are provided for a reconfigurable wheel-on-leg platform that benchmark the algorithm against state-of-the-art samplingbased planners. In minimizing an energy objective that combines the mechanical work required for platform locomotion with that required for reconfiguration, the planner produces intuitive behaviors where the robot dynamically adjusts its footprint, varies its height, and clambers over obstacles using legged locomotion. These results illustrate the generality of the planner in exploiting the platform’s mechanical ability to fluidly transition between various physical geometric configurations, and wheeled/legged locomotion modes.

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http://hdl.handle.net/10453/99720