Belief-Driven Manipulator Visual Servoing for Less Controlled Environments
Authors: Webb, Stephen1; Furukawa, Tomonari1
Source: Advanced Robotics, Volume 22, Number 5, 2008 , pp. 547-572(26)
Publisher: VSP, an imprint of Brill
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Abstract:
This paper presents the architecture of a feedforward manipulator control strategy based on a belief function that may be appropriate for less controlled environments. In this architecture, the belief about the environmental state, as described by a probability density function, is maintained by a recursive Bayesian estimation process. A likelihood is derived from each observation regardless of whether the targeted features of the environmental state have been detected or not. This provides continuously evolving information to the controller and allows an inaccurate belief to evolve into an accurate belief. Control actions are determined by maximizing objective functions using non-linear optimization. Forward models are used to transform control actions to a predicted state so that objective functions may be expressed in task space. The first set of examples numerically investigates the validity of the proposed strategy by demonstrating control in a two dimensional scenario. Then a more realistic application is presented where a robotic manipulator executes a searching and tracking task using an eye-in-hand vision sensor.Keywords: VISUAL SERVOING; ROBOTIC MANIPULATOR; BAYESIAN ESTIMATION; SEARCH AND TRACKING
Document Type: Research article
DOI: 10.1163/156855308X294888
Affiliations: 1: School of Mechanical and Manufacturing Engineering, University of New South Wales, Sydney, NSW 2052, Australia
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