Titlebook: Nonlinear Kalman Filtering for Force-Controlled Robot Tasks; Tine Lefebvre,Herman Bruyninckx,Joris Schutter Book 2005 Springer-Verlag Berl

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1 Introduction,, compliant motion tasks are often position-controlled. Hence, they require very structured environments, i.e., the work pieces or parts to assemble are accurately positioned and their dimensions are known. In these cases, the robot receives and executes a nominal task plan.

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3 Literature Survey: Bayesian Probability Theory,ics is discussed (Sect. 3.2). Section 3.3 presents Bayesian . based on data measured at discrete time steps. Section 3.4 describes Bayesian .. Sections 3.5 and 3.6 focus on ., i.e., the optimisation of the experiment in order to provide “optimal” state estimates. Section 3.5 presents ways to measure

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5 The Non-Minimal State Kalman Filter,lman Filter (KF) for linear systems subject to additive Gaussian uncertainties. Other examples are the filters of Bene? [25], which requires the measurement model to be linear, and Daum [61], applicable to a more general class of systems with nonlinear process and measurement models for which the po

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6 Contact Modelling,re needed in the force controller, the estimator and the planner of the system. The models are di.erent for each contact formation (CF), and are a function of the geometrical parameters (i.e., the positions, orientations and dimensions of the contacting objects).

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8 Experiment: A Cube-In-Corner Assembly, and the recognition of CFs during a cube-in-corner assembly, Fig. 1.1. This chapter uses the Iterated Extended Kalman Filter (IEKF) described in Chap. 4, the Non-minimal State Kalman Filter (NMSKF) described in Chap. 5 and the contact models of Chap. 6. The details about the application of these fi

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Countermand

D Kalman Filtering for Non-Minimal Measurement Models, the innovation covariance matrix .. For non-minimal measurement equations, this matrix is singular. This appendix contains the proof that the results of the Kalman Filter (KF) using non-minimal measurement equations are the same as the results of the KF using a minimal set of measurement equations,

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E Partial Observation with the Kalman Filter,riables; and (ii) the full state estimate and covariance matrix (i.e., including the . state variables) can be calculated at any time based on the full initial state estimate and covariance matrix and the new state estimate and covariance matrix of the observed part of the state.

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