Multi-Step Look-Ahead Trajectory Planning in SLAM: Possibility and Necessity

Huang, S; Kwok, N; Dissanayake, G; Ha, QP; Fang, G

Multi-Step Look-Ahead Trajectory Planning in SLAM: Possibility and Necessity

Huang, S

Kwok, N Dissanayake, G

Ha, QP

Fang, G

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Publisher:: The Institute of Electrical and Electronic Engineers Inc
Publication Type:: Conference Proceeding
Citation:: Proceedings of 2005 IEEE International Conference on Robotics and Automation, 2005, pp. 1103 - 1108
Issue Date:: 2005-01

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Field	Value	Language
dc.contributor.author	Huang, S https://orcid.org/0000-0002-6124-4178	en_US
dc.contributor.author	Kwok, N	en_US
dc.contributor.author	Dissanayake, G https://orcid.org/0000-0002-7992-0680	en_US
dc.contributor.author	Ha, QP https://orcid.org/0000-0003-0978-1758	en_US
dc.contributor.author	Fang, G	en_US
dc.contributor.editor	N/A	en_US
dc.date	2005-04-18	en_US
dc.date.issued	2005-01	en_US
dc.identifier.citation	Proceedings of 2005 IEEE International Conference on Robotics and Automation, 2005, pp. 1103 - 1108	en_US
dc.identifier.isbn	0-7803-8914-X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/2774
dc.description.abstract	In this paper, the possibility and necessity of multistep trajectory planning in Extended Kalman Filter (EKF) based SLAM is investigated. The objective of the trajectory planning here is to minimize the estimation error of the robot and landmark locations subject to a given time horizon. We show that the problem can be regarded as an optimization problem for a gradually identified model. A numerical method is proposed for trajectory planning using a variant of the nonlinear Model Predictive Control (MPC). The proposed method is optimal in the sense that the control action is computed using all the information available at the time of decision making. Simulation results are included to compare the results from the one-step look-ahead trajectory planning and the proposed multi-step lookahead technique	en_US
dc.publisher	The Institute of Electrical and Electronic Engineers Inc	en_US
dc.relation.ispartof	Proceedings of 2005 IEEE International Conference on Robotics and Automation	en_US
dc.relation.ispartof	IEEE International Conference on Robots and Automation	en_US
dc.title	Multi-Step Look-Ahead Trajectory Planning in SLAM: Possibility and Necessity	en_US
dc.type	Conference Proceeding
utslib.location	Barcelona, Spain	en_US
utslib.location.activity	Barcelona, Spain	en_US
utslib.for	091007 Manufacturing Robotics and Mechatronics (excl. Automotive Mechatronics)	en_US
utslib.for	091303 Autonomous Vehicles	en_US
dc.location.activity	Barcelona, Spain	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 Electrical and Data Engineering
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
pubs.organisational-group	/University of Technology Sydney/Strength - CBI - Centre for Built Infrastructure
utslib.copyright.status	open_access
pubs.consider-herdc	true	en_US
pubs.place-of-publication	Barcelona, Spain	en_US
pubs.start-date	2005-04-18	en_US

Abstract:

In this paper, the possibility and necessity of multistep trajectory planning in Extended Kalman Filter (EKF) based SLAM is investigated. The objective of the trajectory planning here is to minimize the estimation error of the robot and landmark locations subject to a given time horizon. We show that the problem can be regarded as an optimization problem for a gradually identified model. A numerical method is proposed for trajectory planning using a variant of the nonlinear Model Predictive Control (MPC). The proposed method is optimal in the sense that the control action is computed using all the information available at the time of decision making. Simulation results are included to compare the results from the one-step look-ahead trajectory planning and the proposed multi-step lookahead technique

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http://hdl.handle.net/10453/2774