On the mechanical contribution of head stabilization to passive dynamics of anthropometric walkers

During the steady gait, humans stabilize their head around the vertical orientation. While there are sensori-cognitive explanations for this phenomenon, its mechanical e fect on the body dynamics remains un-explored. In this study, we take profit from the similarities that human steady gait share with the locomotion of passive dynamics robots. We introduce a simplified anthropometric D model to reproduce a broad walking dynamics. In a previous study, we showed heuristically that the presence of a stabilized head-neck system significantly influences the dynamics of walking. This paper gives new insights that lead to understanding this mechanical e fect. In particular, we introduce an original cart upper-body model that allows to better understand the mechanical interest of head stabilization when walking, and we study how this e fect is sensitive to the choice of control parameters

Rapid Pose Label Generation through Sparse Representation of Unknown Objects

Deep Convolutional Neural Networks (CNNs) have been successfully deployed on robots for 6-DoF object pose estimation through visual perception. However, obtaining labeled data on a scale required for the supervised training of CNNs is a difficult task - exacerbated if the object is novel and a 3D model is unavailable. To this end, this work presents an approach for rapidly generating real-world, pose-annotated RGB-D data for unknown objects. Our method not only circumvents the need for a prior 3D object model (textured or otherwise) but also bypasses complicated setups of fiducial markers, turntables, and sensors. With the help of a human user, we first source minimalistic labelings of an ordered set of arbitrarily chosen keypoints over a set of RGB-D videos. Then, by solving an optimization problem, we combine these labels under a world frame to recover a sparse, keypoint-based representation of the object. The sparse representation leads to the development of a dense model and the pose labels for each image frame in the set of scenes. We show that the sparse model can also be efficiently used for scaling to a large number of new scenes. We demonstrate the practicality of the generated labeled dataset by training a pipeline for 6-DoF object pose estimation and a pixel-wise segmentation network

Tilt estimator for 3D non-rigid pendulum based on a tri-axial accelerometer and gyrometer

International audienceThe paper presents a new observer for tilt estimation of a 3-D non-rigid pendulum. The system can be seen as a multibody robot attached to the environment with a ball joint. There is no sensor for the joint position of the sensor. The estimation of tilt, i.e. roll and pitch angles, is mandatory for balance control for a humanoid robot and all tasks requiring verticality. Our method obtains tilt estimations using encoders on other joints and inertial measurements given by an IMU equipped with tri-axial accelerometer and gyrometer mounted in any body of the robot. The estimator takes profit from the kinematic coupling resulting from the pivot constraint and uses the entire signal of accelerometer including linear accelerations. Almost Global Asymptotic convergence of the estimation errors is proven together with local exponential stability. The performance of the proposed observer is illustrated by simulations

Velocity-Aided IMU-Based Tilt and Attitude Estimation

International audienceThis article addresses the problem of estimating the tilt and more generally the attitude of a rigid body that is subject to high accelerations and equipped with inertial measurement units (IMU) and a sensor providing the body velocity (expressed in the reference frame attached to the body). In the absence of a magnetometer, tilt estimation is proposed through a two-step observer: a global-exponentially stable pre-estimation given to a manifold-constrained complementary filter. In the presence of a magnetometer, the presented observer allows us to reconstruct the full attitude and tune how much the estimation of the tilt is influenced by the magnetometer, depending on the level of confidence given to the measurements of the magnetometer. All state estimators are proposed with proofs of almost global asymptotic stability and local exponential convergence. Finally, these estimators are compared with state-of-the-art solutions in clean and noisy simulations, allowing recommended solutions to be drawn for each case

A Strictly Convex Hull for Computing Proximity Distances With Continuous Gradients

International audienceWe propose a new bounding volume that achieves a tunable strict convexity of a given convex hull. This geometric operator is named sphere-tori-patches bounding volume (STP-BV), which is the acronym for the bounding volume made of patches of spheres and tori. The strict convexity of STP-BV guarantees a unique pair of witness points and at least C 1 continuity of the distance function resulting from a proximity query with another convex shape. Subsequently, the gradient of the distance function is continuous. This is useful for integrating distance as a constraint in robotic motion planners or controllers using smooth optimization techniques. For the sake of completeness, we compare performance in smooth and nonsmooth optimization with examples of growing complexity when involving distance queries between pairs of con-vex shapes

Velocity-Aided IMU-Based Attitude Estimation

This paper addresses the problem of estimating the attitude of a rigid body, which is subject to high accelerations and equipped with inertial measurement unit (IMU) and sensors providing the body velocity (ex-pressed in the reference frame attached to the body). That issue can be treated differently depending on the level of confidence in the measurements of the magnetometer of the IMU, particularly with regard to the observation of the inclination component with respect to the vertical direction, rendering possible to describe the interaction with gravity. Two cases are then studied: either (i) the magnetometer is absent and only the inclination can be estimated, (ii) the magne-tometer is present, giving redundancy and full attitude observability. In the latter case, the presented observer allows to tune how much the inclination estimation is influenced by the magnetometer. All state estimators are proposed with proof of almost global as-ymptotic stability and local exponential convergence. Finally, these estimators are compared with state-of-the-art solutions in clean and noisy simulations, allowing recommended solutions to be drawn for each case

Estimation and Stabilization of Humanoid Flexibility Deformation Using Only Inertial Measurement Units and Contact Information

International audienceMost robots are today controlled as being entirely rigid. But often, as for HRP-2 robot, there are flexible parts, intended for example to absorb impacts. The deformation of this flexibility modifies the orientation of the robot and endangers balance. Nevertheless, robots have usually inertial sensors (IMUs) to reconstruct their orientation based on gravity and inertial effects. Moreover, humanoids have usually to ensure a firm contact with the ground, which provides reliable information on surrounding environment. We show in this study how important it is to take into account these information to improve IMU-based position/orientation reconstruction. We use an extended Kalman filter to rebuild the deformation, making the fusion between IMU and contact information, and without making any assumption on the dynamics of the flexibility. We show how, with this simple setting, we are able to compensate for perturbations and to stabilize the end-effector's position/orientation in the world reference frame. We show also that this estimation is reliable enough to enable a closed-loop stabilization of the flexibility and control of the CoM position with the simplest possible model

Humanoid Flexibility Deformation Can Be Efficiently Estimated Using Only Inertial Measurement Units and Contact Information

International audienceMost robots are today controlled as being entirely rigid. But often, as for HRP-2 robot, there are flexible parts, intended for example to absorb impacts. The deformation of this flexibility changes the configuration of the robot, particularly in orientation. Nevertheless, robots have usually inertial sensors (IMUs) to reconstruct their orientation based on gravity and inertial effects. Moreover, humanoids have usually to ensure a firm contact with the ground, which provides reliable information on the surrounding environment. We show in this study, how important it is to take into account these information to improve IMU-based position/orientation reconstruction. We use an extended Kalman filter to rebuild the deformation, making the fusion between IMU and contact information, and without making any assumption on the dynamics of the flexibility. We show how, with this simple setting, we are able to compensate for perturbations and to stabilize the end-effector's position/orientation in the world reference frame