This paper details the implementation of state-of-the-art whole-body control algorithms on the humanoid robot iCub. We regulate the forces between the robot and its surrounding environment to stabilize a desired posture. We assume that the forces and torques are exerted on rigid contacts. The validity of this assumption is guaranteed by constraining the contact forces and torques, e.g., the contact forces must belong to the associated friction cones. The implementation of this control strategy requires the estimation of both joint torques and external forces acting on the robot. We then detail algorithms to obtain these estimates when using a robot with an iCub-like sensor set, i.e., distributed six-axis force-torque sensors and whole-body tactile sensors. A general theory for identifying the robot inertial parameters is also presented. From an actuation standpoint, we show how to implement a joint-torque control in the case of DC brushless motors. In addition, the coupling mechanism of the iCub torso is investigated. The soundness of the entire control architecture is validated in a real scenario involving the robot iCub balancing and making contact with both arms.
Abstract-This paper presents a new condition, the fully physical consistency for a set of inertial parameters to determine if they can be generated by a physical rigid body. The proposed condition ensure both the positive definiteness and the triangular inequality of 3D inertia matrices as opposed to existing techniques in which the triangular inequality constraint is ignored. This paper presents also a new parametrization that naturally ensures that the inertial parameters are fully physical consistency. The proposed parametrization is exploited to reformulate the inertial identification problem as a manifold optimization problem, that ensures that the identified parameters can always be generated by a physical body. The proposed optimization problem has been validated with a set of experiments on the iCub humanoid robot.
This paper presents a novel approach for incremental semiparametric inverse dynamics learning. In particular, we consider the mixture of two approaches: Parametric modeling based on rigid body dynamics equations and nonparametric modeling based on incremental kernel methods, with no prior information on the mechanical properties of the system. The result is an incremental semiparametric approach, leveraging the advantages of both the parametric and nonparametric models. We validate the proposed technique learning the dynamics of one arm of the iCub humanoid robot.
This paper discusses online algorithms for inverse dynamics modelling in robotics. Several model classes including rigid body dynamics (RBD) models, data-driven models and semiparametric models (which are a combination of the previous two classes) are placed in a common framework. While model classes used in the literature typically exploit joint velocities and accelerations, which need to be approximated resorting to numerical differentiation schemes, in this paper a new "derivative-free" framework is proposed that does not require this preprocessing step. An extensive experimental study with real data from the right arm of the iCub robot is presented, comparing different model classes and estimation procedures, showing that the proposed "derivative-free" methods outperform existing methodologies.
No abstract
The paper presents a novel sensorized skin insole based on tactile capacitive technology. The insole prototype provides information such as pressure distribution, contact force and moments, center of pressure. These variables require an accurate calibration procedure to retrieve the relationship between the measured capacitance and the corresponding applied pressure. A calibration technique is here proposed and validated by exploiting a pair of shoes equipped with force/torque sensors. The validation analysis shows that the quantities estimated by the skin insoles match data measured by the force/torque sensors. Further, an example of real application for using skin insoles is presented for a gait analysis. Keywords sensorized insole, capacitive sensors, tactile sensors array, wearable sensorsSeveral techniques and technologies to detect gait events and plantar pressure monitoring have been developed over the years. The solutions involve mainly the following sensors: force platforms, pedobarographs, force treadmill, sensorized shoes and sensorized insoles. Force platforms [1,10,22], pedobarographs and force treadmill [19] are very reliable and accurate and can stream information at very high frequencies. They can be used for both static and dynamic studies arXiv:1910.06370v1 [physics.app-ph]
The success of robots in real-world environments is largely dependent on their ability to interact with both humans and said environment. The FP7 EU project CoDyCo focused on the latter of these two challenges by exploiting both rigid and compliant contacts dynamics in the robot control problem. Regarding the former, to properly manage interaction dynamics on the robot control side, an estimation of the human behaviours and intentions is necessary. In this paper we present the building blocks of such a human-in-the-loop controller, and validate them in both simulation and on the iCub humanoid robot using a human-robot interaction scenario. In this scenario, a human assists the robot in standing up from being seated on a bench. Index Terms-Physical Human-Robot Interaction, Humanoid Robots I. INTRODUCTION T HE ability to interact with and manipulate the environment gives robots a distinct advantage over purely software based automated agents. In the FP7 European project, CoDyCo, the focus was on how to properly exploit contact dynamics in the control of the robot. When the interaction involves humans, their intrinsic unpredictability makes the collaboration problem far more difficult. Foreseen robotic applications range from the use of robots as service and elderly assistants, to their use in industrial plants in close contact with
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.