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SUMMARY A new method for the estimation of subject-specific muscle-tendon parameters of the knee actuators based on dynamometry experiments is presented. The algorithm aims at estimating the tendon slack length and the optimal muscle fiber length by minimizing the difference between experimentally reproduced and model-based joint moments. The key innovative features are as follows: (i) the inclusion of a priori physiological knowledge to define a physiologically feasible set, the hot start for the optimization, and constraints for the optimization and (ii) the introduction of a new (affine) transformation of the muscle-tendon parameters, which greatly improves the numerical condition of the optimization. The influence of the initial guess and of measurement noise was studied in a simulation environment, and the performance was compared with that of the method presented earlier by Garner and Pandy for the upper limb. The tendon slack length was estimated for 97.5/63% (extensors/flexors) of all initial guesses within 2% of the ground truth. The optimal fiber length was estimated for 89/90% (extensors/flexors) of all initial guesses within 2% of the ground truth. When 10 Nm measurement noise was added, the mean value of the estimated tendon slack length deviated at most 1.9/1.6% (extensors/flexors) from the ground truth whereas the standard deviations were at most 5.1/3.9%. The mean value of the estimated optimal fiber length deviated at most 4.3/3.0% (extensors/flexors) from the ground truth whereas the standard deviations were at most 10.2/15.5%. In comparison, mean values resulting from the method of Garner and Pandy deviated up to 181% ( ± 123%) and 119% ( ± 30%) from the ground truth for, respectively, optimal fiber length and tendon slack length of rectus femoris. We concluded that the presented method had a low dependency on the initial guess and outperformed the method of Garner and Pandy in terms of accuracy by at least one order of magnitude when parameters were estimated from noisy data. The improvements open new perspectives for subject-specific modelling of muscles and tendons, which is beneficial for the accuracy of human motion simulations. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Computer simulations play an important role in the design and validation of constrained robotic operations. The fidelity of these simulations, however, depends on the specification of contact dynamics parameters, which often need to be determined experimentally. In this paper we investigate the identification of contact parameters from complex stiff multi-point contact scenarios encountered in typical robotic operations using a recently developed least-squares-based method. This method is extended to deal with geometric uncertainties by employing a non-linear separable least-squares formulation. The latter is solved using a variable projection method, and allows simultaneous identification of contact parameters and geometric uncertainties. The conditions for observability of geometric uncertainties are derived and a regularized formulation is proposed in case the identification of geometric uncertainties is ill-conditioned. The applicability of the original method and the benefits of the extended method with identification of geometric uncertainties for the identification of the contact parameters are illustrated by means of experimental data measured with the Special Purpose Dexterous Manipulator (SPDM) Task Verification Facility (STVF) manipulator at the Canadian Space Agency (CSA). [ABSTRACT FROM AUTHOR]

This paper introduces a systematic constraint-based approach to specify complex tasks of general sensor-based robot systems consisting of rigid links and joints. The approach integrates both instantaneous task specification and estimation of geometric uncertainty in a unified framework. Major components are the use offeature coordinates, defined with respect to object and feature frames, which facilitate the task specification, and the introduction of uncertainty coordinates to model geometric uncertainly. While the focus of the paper is on task specification, an existing velocity-based control scheme is reformulated in terms of these feature and uncertainty coordinates. This control scheme compensates for the effect of time varying uncertainly coordinates. Constraint weighting results in an invariant robot behavior in case of conflicting constraints with heterogeneous units. The approach applies to a large variety of robot systems (mobile robots, multiple robot systems, dynamic human-robot interaction, etc.), various sensor systems, and different robot tasks. Ample simulation and experimental results are presented. [ABSTRACT FROM AUTHOR]

This paper describes the construction of a local polyhedral 3-D feature model derived from pose and wrench sensor measurements collected during a force-controlled execution of a sequence of polyhedral contact formations. The procedure consists of two steps: 1) the sensor measurements are filtered, resulting in a (nonminimal) representation of a 3-D feature model; and 2) identification of a reduced set of geometric parameters (vertices and faces) of the rigid polyhedral objects in the environment is per- formed. The following improvements with respect to the state of the art are made: 1) creation of a polyhedral 3-D feature model of a previously unknown polyhedral environment; 2) estimation of a force decomposition useful for feedback in a force controller or for monitoring the contact forces; and 3) reduction of the number of model parameters, leading to a computational reduction, a better precision of the geometric parameters, and a higher level description of the contact topology. [ABSTRACT FROM AUTHOR]

Human joint torques during gait are usually computed using inverse dynamics. This method requires a skeletal model, kinematics and measured ground reaction forces and moments (GRFM). Measuring GRFM is however only possible in a controlled environment. This paper introduces a probabilistic method based on probabilistic principal component analysis to estimate the joint torques for healthy gait without measured GRFM. A gait dataset of 23 subjects was obtained containing kinematics, measured GRFM and joint torques from inverse dynamics in order to obtain a probabilistic model. This model was then used to estimate the joint torques of other subjects without measured GRFM. Only kinematics, a skeletal model and timing of gait events are needed. Estimation only takes 0.28 ms per time instant. Using cross-validation, the resulting root mean square estimation errors for the lower-limb joint torques are found to be approximately 0.1 Nm/kg, which is 6–18% of the range of the ground truth joint torques. Estimated joint torque and GRFM errors are up to two times smaller than model-based state-of-the-art methods. Model-free artificial neural networks can achieve lower errors than our method, but are less repeatable, do not contain uncertainty information on the estimates and are difficult to use in situations which are not in the learning set. In contrast, our method performs well in a new situation where the walking speed is higher than in the learning dataset. The method can for example be used to estimate the kinetics during overground walking without force plates, during treadmill walking without (separate) force plates and during ambulatory measurements. [ABSTRACT FROM AUTHOR]