A Quadratic-Cost Dual Control-Based Approach for Optimal Trajectory Planning under Uncertainty

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This paper presents a dual control-based approach for optimal trajectory planning under uncertainty. The method approximately converts a nonlinear stochastic optimal control problem whose objective function is a combination of quadratic stage and/or terminal costs, with additive Gaussian process and measurement noises, into a deterministic optimal control problem by augmenting the uncertainty state defined by the square-root of the estimation error covariance matrix. The open-loop solution to the resulting deterministic optimal control reformulation is obtained using an existing pseudo-spectral method. The effectiveness of the proposed dual control-based approach is verified with two numerical examples of trajectory planning for two-dimensional robot motion with lack of observability for localization, which highlights the impact of the dual effect on the shape of designed paths.
Publisher
INST CONTROL ROBOTICS & SYSTEMS
Issue Date
2017-10
Language
English
Article Type
Article
Keywords

STOCHASTIC-SYSTEMS; BELIEF SPACE; INFORMATION; COVARIANCE

Citation

INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS, v.15, no.5, pp.2253 - 2261

ISSN
1598-6446
DOI
10.1007/s12555-016-0170-z
URI
http://hdl.handle.net/10203/226840
Appears in Collection
ME-Journal Papers(저널논문)AE-Journal Papers(저널논문)
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