A real-time 3D reconstruction of staircases for rehabilitative exoskeletons

In medical contexts, the use of assistive exoskeletons for the rehabilitation of people with impaired mobility represents a common practice. Recent advances suggest that, soon, such mechatronic systems will also be used to assist people in their everyday life. In order to reach such target, exoskeletons must become able to perceive the environment. To this purpose, a system for the parametric identification of a staircase is proposed in this paper. More precisely, given a staircase of unknown geometry, the system identifies its 3D shape. Furthermore, it also estimates the reciprocal orientation and distance between the exoskeleton and the staircase. Differently from other approaches, this result is achieved by means of low cost devices: an inertial measurement unit, two ranging sensors, and an Arm-Cortex processor. Starting from the ranging sensors acquisitions, the staircase model is identified in real time, during the execution of a step. The proposed procedure is based on an extended recursive total least squares strategy, in order to fully exploit the computational capabilities of the Arm processor, and it is characterized by execution times smaller than 10 −3 s. The estimation algorithm has been tested on an actual exoskeleton and the resulting experimental outcomes are compared with the results obtained through alternative methods

Development of a Quadruped Robot and Parameterized Stair-Climbing Behavior

Stair-climbing is a difficult task for mobile robots to accomplish, particularly for legged robots. While quadruped robots have previously demonstrated the ability to climb stairs, none have so far been capable of climbing stairs of variable height while carrying all required sensors, controllers, and power sources on-board. The goal of this thesis was the development of a self-contained quadruped robot capable of detecting, classifying, and climbing stairs of any height within a specified range. The design process for this robot is described, including the development of the joint, leg, and body configuration, the design and selection of components, and both dynamic and finite element analyses performed to verify the design. A parameterized stair-climbing gait is then developed, which is adaptable to any stair height of known width and height. This behavior is then implemented on the previously discussed quadruped robot, which then demonstrates the capability to climb three different stair variations with no configuration change

Terrain Classification from Body-mounted Cameras during Human Locomotion

Abstract—This paper presents a novel algorithm for terrain type classification based on monocular video captured from the viewpoint of human locomotion. A texture-based algorithm is developed to classify the path ahead into multiple groups that can be used to support terrain classification. Gait is taken into account in two ways. Firstly, for key frame selection, when regions with homogeneous texture characteristics are updated, the fre-quency variations of the textured surface are analysed and used to adaptively define filter coefficients. Secondly, it is incorporated in the parameter estimation process where probabilities of path consistency are employed to improve terrain-type estimation. When tested with multiple classes that directly affect mobility a hard surface, a soft surface and an unwalkable area- our proposed method outperforms existing methods by up to 16%, and also provides improved robustness. Index Terms—texture, classification, recursive filter, terrain classification I

Nonlinear Model Predictive Control for Motion Generation of Humanoids

Das Ziel dieser Arbeit ist die Untersuchung und Entwicklung numerischer Methoden zur Bewegungserzeugung von humanoiden Robotern basierend auf nichtlinearer modell-prädiktiver Regelung. Ausgehend von der Modellierung der Humanoiden als komplexe Mehrkörpermodelle, die sowohl durch unilaterale Kontaktbedingungen beschränkt als auch durch die Formulierung unteraktuiert sind, wird die Bewegungserzeugung als Optimalsteuerungsproblem formuliert. In dieser Arbeit werden numerische Erweiterungen basierend auf den Prinzipien der Automatischen Differentiation für rekursive Algorithmen, die eine effiziente Auswertung der dynamischen Größen der oben genannten Mehrkörperformulierung erlauben, hergeleitet, sodass sowohl die nominellen Größen als auch deren ersten Ableitungen effizient ausgewertet werden können. Basierend auf diesen Ideen werden Erweiterungen für die Auswertung der Kontaktdynamik und der Berechnung des Kontaktimpulses vorgeschlagen. Die Echtzeitfähigkeit der Berechnung von Regelantworten hängt stark von der Komplexität der für die Bewegungerzeugung gewählten Mehrkörperformulierung und der zur Verfügung stehenden Rechenleistung ab. Um einen optimalen Trade-Off zu ermöglichen, untersucht diese Arbeit einerseits die mögliche Reduktion der Mehrkörperdynamik und andererseits werden maßgeschneiderte numerische Methoden entwickelt, um die Echtzeitfähigkeit der Regelung zu realisieren. Im Rahmen dieser Arbeit werden hierfür zwei reduzierte Modelle hergeleitet: eine nichtlineare Erweiterung des linearen inversen Pendelmodells sowie eine reduzierte Modellvariante basierend auf der centroidalen Mehrkörperdynamik. Ferner wird ein Regelaufbau zur GanzkörperBewegungserzeugung vorgestellt, deren Hauptbestandteil jeweils aus einem speziell diskretisierten Problem der nichtlinearen modell-prädiktiven Regelung sowie einer maßgeschneiderter Optimierungsmethode besteht. Die Echtzeitfähigkeit des Ansatzes wird durch Experimente mit den Robotern HRP-2 und HeiCub verifiziert. Diese Arbeit schlägt eine Methode der nichtlinear modell-prädiktiven Regelung vor, die trotz der Komplexität der vollen Mehrkörperformulierung eine Berechnung der Regelungsantwort in Echtzeit ermöglicht. Dies wird durch die geschickte Kombination von linearer und nichtlinearer modell-prädiktiver Regelung auf der aktuellen beziehungsweise der letzten Linearisierung des Problems in einer parallelen Regelstrategie realisiert. Experimente mit dem humanoiden Roboter Leo zeigen, dass, im Vergleich zur nominellen Strategie, erst durch den Einsatz dieser Methode eine Bewegungserzeugung auf dem Roboter möglich ist. Neben Methoden der modell-basierten Optimalsteuerung werden auch modell-freie Methoden des verstärkenden Lernens (Reinforcement Learning) für die Bewegungserzeugung untersucht, mit dem Fokus auf den schwierig zu modellierenden Modellunsicherheiten der Roboter. Im Rahmen dieser Arbeit werden eine allgemeine vergleichende Studie sowie Leistungskennzahlen entwickelt, die es erlauben, modell-basierte und -freie Methoden quantitativ bezüglich ihres Lösungsverhaltens zu vergleichen. Die Anwendung der Studie auf ein akademisches Beispiel zeigt Unterschiede und Kompromisse sowie Break-Even-Punkte zwischen den Problemformulierungen. Diese Arbeit schlägt basierend auf dieser Grundlage zwei mögliche Kombinationen vor, deren Eigenschaften bewiesen und in Simulation untersucht werden. Außerdem wird die besser abschneidende Variante auf dem humanoiden Roboter Leo implementiert und mit einem nominellen modell-basierten Regler verglichen

Robust Reference-Free Sim-to-Real Reinforcement Learning for Bipedal Locomotion

In recent years, model-free Deep Reinforcement Learning (RL) has become an increasingly popular alternative to more traditional model-based or optimization-based control methods in solving robotic legged locomotion. However, deploying RL in the real world can be a significant undertaking. Constructing reward functions which compel controllers to learn the desired behavior is not straightforward. For example, can a reward function which trains a controller to stand be easily modified to train it to walk or run? This thesis seeks to provide insights into training such controllers in ways that make desired behaviors easier to realize by parameterizing behaviors in a low-dimensional periodic space which covers the entire breadth of legged gaits. In addition, it explores the limits of the approach by training controllers to interact (in the real world) with challenging terrain conventionally assumed to be extremely difficult for bipedal robots

Agile Bipedal Locomotion via Hierarchical Control by Incorporating Physical Principles, Learning, and Optimization

Robotic Bipedal locomotion holds the potential for efficient, robust traversal of difficult terrain. The difficulty lies in the dynamics of locomotion which complicate control and motion planning. Bipedal locomotion dynamics are dimensionally large problems, extremely nonlinear, and operate on the limits of actuator capabilities, which limit the performance of generic methods of control. This thesis presents an approach to the problem of agile legged locomotion founded on a first principles understanding of gait dynamics. This approach is built on the perspective that an understanding of locomotion is vital to the successful application of modern control and planning tools. We present 1) a ground-up analysis of legged locomotion as a dynamical phenomenon, 2) approaches that utilize dynamically meaningful reduced order models of locomotion, and 3) applications to the hardware robot Cassie via reinforcement learning

“Local Rank Differences” Image Feature Implemented on GPU

Abstract. A currently popular trend in object detection and pattern recognition is usage of statistical classifiers, namely AdaBoost and its modifications. The speed performance of these classifiers largely depends on the low level image features they are using: both on the amount of information the feature provides and the executional time of its evaluation. Local Rank Differences is an image feature that is alternative to commonly used haar wavelets. It is suitable for implementation in programmable (FPGA) or specialized (ASIC) hardware, but – as this paper shows – it performs very well on graphics hardware (GPU) as well. The paper discusses the LRD features and their properties, describes an experimental implementation of LRD in graphics hardware, presents its empirical performance measures compared to alternative approaches and suggests several notes on practical usage of LRD and proposes directions for future work.

Receding-horizon motion planning of quadrupedal robot locomotion

Quadrupedal robots are designed to offer efficient and robust mobility on uneven terrain. This thesis investigates combining numerical optimization and machine learning methods to achieve interpretable kinodynamic planning of natural and agile locomotion. The proposed algorithm, called Receding-Horizon Experience-Controlled Adaptive Legged Locomotion (RHECALL), uses nonlinear programming (NLP) with learned initialization to produce long-horizon, high-fidelity, terrain-aware, whole-body trajectories. RHECALL has been implemented and validated on the ANYbotics ANYmal B and C quadrupeds on complex terrain. The proposed optimal control problem formulation uses the single-rigid-body dynamics (SRBD) model and adopts a direct collocation transcription method which enables the discovery of aperiodic contact sequences. To generate reliable trajectories, we propose fast-to-compute analytical costs that leverage the discretization and terrain-dependent kinematic constraints. To extend the formulation to receding-horizon planning, we propose a segmentation approach with asynchronous centre of mass (COM) and end-effector timings and a heuristic initialization scheme which reuses the previous solution. We integrate real-time 2.5D perception data for online foothold selection. Additionally, we demonstrate that a learned stability criterion can be incorporated into the planning framework. To accelerate the convergence of the NLP solver to locally optimal solutions, we propose data-driven initialization schemes trained using supervised and unsupervised behaviour cloning. We demonstrate the computational advantage of the schemes and the ability to leverage latent space to reconstruct dynamic segments of plans which are several seconds long. Finally, in order to apply RHECALL to quadrupeds with significant leg inertias, we derive the more accurate lump leg single-rigid-body dynamics (LL-SRBD) and centroidal dynamics (CD) models and their first-order partial derivatives. To facilitate intuitive usage of costs, constraints and initializations, we parameterize these models by Euclidean-space variables. We show the models have the ability to shape rotational inertia of the robot which offers potential to further improve agility