Polyhedral contact formation identification for autonomous compliant motion: exact nonlinear bayesian filtering

Abstract

This paper presents new experimental results for the estimation of large position and orientation inaccuracies of contacting objects during force-controlled compliant motion. The estimation is based on position, velocity, and force measurements. The authors have described the contact modeling and presented some Kalman filter identification results for small inaccuracies. However, when dealing with larger inaccuracies, the nonlinear estimation problem remained unsolved. This problem has now been solved satisfactorily by applying a new Bayesian estimator. The Bayesian filter is valid for static systems (parameter estimation) with any kind of nonlinear measurement equation, subject to Gaussian measurement uncertainty and for a limited class of dynamic systems. Experimental results of this new filter are given for the estimation of the positions and orientations of contacting objects during the cube-in-corner assembly described in the first reference.

Revised force control using a compliant sensor with a position controlled robot

“…This is extremely disadvantageous if the contact has a vertex-face topology, since then the rotation will not cause a restoring moment. Therefore, previous work tried to identify the topology, see [9], [10], [3] for more information. In contrast, here a generic approach is presented which allows force control with a distant sensor, without generating unwanted forces or moments at the tcp.…”

Constraint-based Task Specification and Estimation for Sensor-Based Robot Systems in the Presence of Geometric Uncertainty

“…Applying constraint (29) to (27), while also substituting system equation (28) and replacingχ u by its estimate χ u (since its real value may be unknown during the task execution), results in:…”

“…Substituting control input (31) in system equation (28) and then in output equation (27), and solving forẏ, results in:…”

“…This approach results in a better quality of the task execution (higher success rate in case of geometric disturbances as well as smaller tracking errors). The proposed estimation scheme is a generalization of [9] where the scheme was proposed to account for the unknown motion of a contacting surface in 1D force control, and of our work on estimation of geometric uncertainties [10,26,27]. The paper also contains the link between constraint-based task specification and real-time task execution control.…”

A hybrid contact state analysis methodology for robotic-based adjustment of cylindrical pair

“…Determining the orientation of a polygonal planar object using data from a force/torque sensor attached to a fence, and subsequently using this information on sensory-based manipulations to reorient the object to a desired orientation, offers improvements to sensorless orientation techniques [10][11][12]. Filtering techniques such as Kalman filter [13] and Bayesian filter [14,15] were also reported for estimation of contact state and geometric parameters for robotic cube-in-corner assembly. However, the Kalman filter method requires prior knowledge on contact formulation, and the Bayesian filtering method is sensitive to prior probability distribution of the contact state.…”