Planning under uncertainty using model predictive control for information gathering

Leung, C; Huang, S; Kwok, N; Dissanayake, G

Planning under uncertainty using model predictive control for information gathering

Leung, C Huang, S

Kwok, N Dissanayake, G

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Publication Type:: Journal Article
Citation:: Robotics and Autonomous Systems, 2006, 54 (11), pp. 898 - 910
Issue Date:: 2006-11-30

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Field	Value	Language
dc.contributor.author	Leung, C	en_US
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.date.issued	2006-11-30	en_US
dc.identifier.citation	Robotics and Autonomous Systems, 2006, 54 (11), pp. 898 - 910	en_US
dc.identifier.issn	0921-8890	en_US
dc.identifier.uri	http://hdl.handle.net/10453/4193
dc.description.abstract	This paper considers trajectory planning problems for autonomous robots in information gathering tasks. The objective of the planning is to maximize the information gathered within a finite time horizon. It is assumed that either the Extended Kalman Filter (EKF) or the Extended Information Filter (EIF) is applied to estimate the features of interest and the information gathered is expressed by the covariance matrix or information matrix. It is shown that the planning process can be formulated as an optimal control problem for a nonlinear control system with a gradually identified model. This naturally leads to the Model Predictive Control (MPC) planning strategy, which uses the updated knowledge about the model to solve a finite horizon optimal control problem at each time step and only executes the first control action. The proposed MPC framework is demonstrated through solutions to two challenging information gathering tasks: (1) Simultaneous planning, localization, and map building (SPLAM) and (2) Multi-robot Geolocation. It is shown that MPC can effectively deal with dynamic constraints, multiple robots/features and a range of objective functions. © 2006 Elsevier Ltd. All rights reserved.	en_US
dc.relation.ispartof	Robotics and Autonomous Systems	en_US
dc.relation.isbasedon	10.1016/j.robot.2006.05.008	en_US
dc.subject.classification	Industrial Engineering & Automation	en_US
dc.title	Planning under uncertainty using model predictive control for information gathering	en_US
dc.type	Journal Article
utslib.citation.volume	11	en_US
utslib.citation.volume	54	en_US
utslib.for	091303 Autonomous Vehicles	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
pubs.issue	11	en_US
pubs.publication-status	Published	en_US
pubs.volume	54	en_US

Abstract:

This paper considers trajectory planning problems for autonomous robots in information gathering tasks. The objective of the planning is to maximize the information gathered within a finite time horizon. It is assumed that either the Extended Kalman Filter (EKF) or the Extended Information Filter (EIF) is applied to estimate the features of interest and the information gathered is expressed by the covariance matrix or information matrix. It is shown that the planning process can be formulated as an optimal control problem for a nonlinear control system with a gradually identified model. This naturally leads to the Model Predictive Control (MPC) planning strategy, which uses the updated knowledge about the model to solve a finite horizon optimal control problem at each time step and only executes the first control action. The proposed MPC framework is demonstrated through solutions to two challenging information gathering tasks: (1) Simultaneous planning, localization, and map building (SPLAM) and (2) Multi-robot Geolocation. It is shown that MPC can effectively deal with dynamic constraints, multiple robots/features and a range of objective functions. © 2006 Elsevier Ltd. All rights reserved.

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http://hdl.handle.net/10453/4193