Reactive Planar Manipulation with Convex Hybrid MPC

This paper presents a reactive controller for planar manipulation tasks that leverages machine learning to achieve real-time performance. The approach is based on a Model Predictive Control (MPC) formulation, where the goal is to find an optimal sequence of robot motions to achieve a desired object motion. Due to the multiple contact modes associated with frictional interactions, the resulting optimization program suffers from combinatorial complexity when tasked with determining the optimal sequence of modes. To overcome this difficulty, we formulate the search for the optimal mode sequences offline, separately from the search for optimal control inputs online. Using tools from machine learning, this leads to a convex hybrid MPC program that can be solved in real-time. We validate our algorithm on a planar manipulation experimental setup where results show that the convex hybrid MPC formulation with learned modes achieves good closed-loop performance on a trajectory tracking problem

More than a Million Ways to Be Pushed: A High-Fidelity Experimental Dataset of Planar Pushing

Pushing is a motion primitive useful to handle objects that are too large, too heavy, or too cluttered to be grasped. It is at the core of much of robotic manipulation, in particular when physical interaction is involved. It seems reasonable then to wish for robots to understand how pushed objects move. In reality, however, robots often rely on approximations which yield models that are computable, but also restricted and inaccurate. Just how close are those models? How reasonable are the assumptions they are based on? To help answer these questions, and to get a better experimental understanding of pushing, we present a comprehensive and high-fidelity dataset of planar pushing experiments. The dataset contains timestamped poses of a circular pusher and a pushed object, as well as forces at the interaction.We vary the push interaction in 6 dimensions: surface material, shape of the pushed object, contact position, pushing direction, pushing speed, and pushing acceleration. An industrial robot automates the data capturing along precisely controlled position-velocity-acceleration trajectories of the pusher, which give dense samples of positions and forces of uniform quality. We finish the paper by characterizing the variability of friction, and evaluating the most common assumptions and simplifications made by models of frictional pushing in robotics.Comment: 8 pages, 10 figure

A Whole-Body Pose Taxonomy for Loco-Manipulation Tasks

Exploiting interaction with the environment is a promising and powerful way to enhance stability of humanoid robots and robustness while executing locomotion and manipulation tasks. Recently some works have started to show advances in this direction considering humanoid locomotion with multi-contacts, but to be able to fully develop such abilities in a more autonomous way, we need to first understand and classify the variety of possible poses a humanoid robot can achieve to balance. To this end, we propose the adaptation of a successful idea widely used in the field of robot grasping to the field of humanoid balance with multi-contacts: a whole-body pose taxonomy classifying the set of whole-body robot configurations that use the environment to enhance stability. We have revised criteria of classification used to develop grasping taxonomies, focusing on structuring and simplifying the large number of possible poses the human body can adopt. We propose a taxonomy with 46 poses, containing three main categories, considering number and type of supports as well as possible transitions between poses. The taxonomy induces a classification of motion primitives based on the pose used for support, and a set of rules to store and generate new motions. We present preliminary results that apply known segmentation techniques to motion data from the KIT whole-body motion database. Using motion capture data with multi-contacts, we can identify support poses providing a segmentation that can distinguish between locomotion and manipulation parts of an action.Comment: 8 pages, 7 figures, 1 table with full page figure that appears in landscape page, 2015 IEEE/RSJ International Conference on Intelligent Robots and System

A parametrized three-dimensional model for MEMS thermal shear-stress sensors

This paper presents an accurate and efficient model of MEMS thermal shear-stress sensors featuring a thin-film hotwire on a vacuum-isolated dielectric diaphragm. We consider three-dimensional (3-D) heat transfer in sensors operating in constant-temperature mode, and describe sensor response with a functional relationship between dimensionless forms of hotwire power and shear stress. This relationship is parametrized by the diaphragm aspect ratio and two additional dimensionless parameters that represent heat conduction in the hotwire and diaphragm. Closed-form correlations are obtained to represent this relationship, yielding a MEMS sensor model that is highly efficient while retaining the accuracy of three-dimensional heat transfer analysis. The model is compared with experimental data, and the agreement in the total and net hotwire power, the latter being a small second-order quantity induced by the applied shear stress, is respectively within 0.5% and 11% when uncertainties in sensor geometry and material properties are taken into account. The model is then used to elucidate thermal boundary layer characteristics for MEMS sensors, and in particular, quantitatively show that the relatively thick thermal boundary layer renders classical shear-stress sensor theory invalid for MEMS sensors operating in air. The model is also used to systematically study the effects of geometry and material properties on MEMS sensor behavior, yielding insights useful as practical design guidelines

Dynamic Analysis of Gough Stewart Robot Manipulator by Using Lagrange Formulation in Matlab Software

في هذا البحث، استخدم الروبوت المتوازي كووف ستيوورت له ست درجات من الحرية كنموذج لاشتقاق معادلات الكينماتك المعكوسة والمعادلات الديناميكية العكسية. إن اشتقاق التحليل الكينماتيك العكسي بسيط جداً الذي يستخدم لحساب مصفوفة جاكوبيان وأطوال الارجل لتحديد مسار المنصة المتحركة. المعادلة الديناميكية اشتقت بطريقة لاكرانج من خلال حساب الطاقة الحركية والكامنة للنموذج. المعادلة الديناميكية تم ادخالها في قالب السميولنك كملف ماتلاب تؤدي إلى حساب قوى الارجل في أي وقت من المحاكاة ويمكن استخدامها لبيان ما إذا كان المسار يحتوي على شذوذ حيث تتزايد القوى بسرعة كبيرة في نقطة الشذوذ مقارنة بالنقاط الأخرى أثناء مسار المنصة المتحركة. أخيرا يمكن استخدام هذه القالب في النماذج الأخرى مع مدخلات مختلفة.In this paper, Gough Stewart parallel manipulator with six-degree of freedom used as a model to derived the inverse kinematics equations and inverse dynamic equations. The inverse kinematic problem is very simple to derive then used to computed the jacobian matrix and the lengths of the linkages to determine the path trajectory of the moving platform. The dynamic equation based on the Lagrange method by calculated the kinetic and potential energies for the model. The dynamic equation inserted in Simulink block as Matlab file lead to computed the forces of the linkages at any time of simulation and can be used to explain if the path contain a singularities where the forces increasing very quickly in singular point compare with other points during the path of the moving platform. Finally, these blocks can be used in the other models with different parameters inputs

Computing push plans for disk-shaped robots

Suppose we want to move a passive object along a given path, among obstacles in the plane, by pushing it with an active robot. We present two algorithms to compute a push plan for the case that the obstacles are non-intersecting line segments, and the object and robot are disks. The first algorithm assumes that the robot must maintain contact with the object at all times, and produces a shortest path. There are also situations, however, where the robot has no choice but to let go of the object occasionally. Our second algorithm handles such cases, but no longer guarantees that the produced path is the shortest possible

Computing push plans for disk-shaped robots

Suppose we want to move a passive object along a given path, among obstacles in the plane, by pushing it with an active robot. We present two algorithms to compute a push plan for the case that the obstacles are non-intersecting line segments, and the object and robot are disks. The first algorithm assumes that the robot must maintain contact with the object at all times, and produces a shortest path. There are also situations, however, where the robot has no choice but to let go of the object occasionally. Our second algorithm handles such cases, but no longer guarantees that the produced path is the shortest possible