Human-Robot Interaction Strategies for Walker-Assisted Locomotion

Neurological and age-related diseases affect human mobility at different levels causing partial or total loss of such faculty. There is a significant need to improve safe and efficient ambulation of patients with gait impairments. In this context, walkers present important benefits for human mobility, improving balance and reducing the load on their lower limbs. Most importantly, walkers induce the use of patients residual mobility capacities in different environments. In the field of robotic technologies for gait assistance, a new category of walkers has emerged, integrating robotic technology, electronics and mechanics. Such devices are known as robotic walkers, intelligent walkers or smart walkers One of the specific and important common aspects to the field of assistive technologies and rehabilitation robotics is the intrinsic interaction between the human and the robot. In this thesis, the concept of Human-Robot Interaction (HRI) for human locomotion assistance is explored. This interaction is composed of two interdependent components. On the one hand, the key role of a robot in a Physical HRI (pHRI) is the generation of supplementary forces to empower the human locomotion. This involves a net flux of power between both actors. On the other hand, one of the crucial roles of a Cognitive HRI (cHRI) is to make the human aware of the possibilities of the robot while allowing him to maintain control of the robot at all times. This doctoral thesis presents a new multimodal human-robot interface for testing and validating control strategies applied to a robotic walkers for assisting human mobility and gait rehabilitation. This interface extracts navigation intentions from a novel sensor fusion method that combines: (i) a Laser Range Finder (LRF) sensor to estimate the users legs kinematics, (ii) wearable Inertial Measurement Unit (IMU) sensors to capture the human and robot orientations and (iii) force sensors measure the physical interaction between the humans upper limbs and the robotic walker. Two close control loops were developed to naturally adapt the walker position and to perform body weight support strategies. First, a force interaction controller generates velocity outputs to the walker based on the upper-limbs physical interaction. Second, a inverse kinematic controller keeps the walker within a desired position to the human improving such interaction. The proposed control strategies are suitable for natural human-robot interaction as shown during the experimental validation. Moreover, methods for sensor fusion to estimate the control inputs were presented and validated. In the experimental studies, the parameters estimation was precise and unbiased. It also showed repeatability when speed changes and continuous turns were performed

MRSL: AUTONOMOUS NEURAL NETWORK-BASED SELF-STABILIZING SYSTEM

Stabilizing and localizing the positioning systems autonomously in the areas without GPS accessibility is a difficult task. In this thesis we describe a methodology called Most Reliable Straight Line (MRSL) for stabilizing and positioning camera-based objects in 3-D space. The camera-captured images are used to identify easy-to-track points “interesting points� and track them on two consecutive images. The distance between each of interesting points on the two consecutive images are compared and one with the maximum length is assigned to MRSL, which is used to indicate the deviation from the original position. To correct this our trained algorithm is deployed to reduce the deviation by issuing relevant commands, this action is repeated until MRSL converges to zero. To test the accuracy and robustness, the algorithm was deployed to control positioning of a Quadcopter. It was demonstrated that the Quadcopter (a) was highly robust to any external forces, (b) can fly even if the Quadcopter experiences loss of engine, (c) can fly smoothly and positions itself on a desired location

Calibration of full-waveform airborne laser scanning data for 3D object segmentation

Phd ThesisAirborne Laser Scanning (ALS) is a fully commercial technology, which has seen rapid uptake from the photogrammetry and remote sensing community to classify surface features and enhance automatic object recognition and extraction processes. 3D object segmentation is considered as one of the major research topics in the field of laser scanning for feature recognition and object extraction applications. The demand for automatic segmentation has significantly increased with the emergence of full-waveform (FWF) ALS, which potentially offers an unlimited number of return echoes. FWF has shown potential to improve available segmentation and classification techniques through exploiting the additional physical observables which are provided alongside the standard geometric information. However, use of the FWF additional information is not recommended without prior radiometric calibration, taking into consideration all the parameters affecting the backscattered energy. The main focus of this research is to calibrate the additional information from FWF to develop the potential of point clouds for segmentation algorithms. Echo amplitude normalisation as a function of local incidence angle was identified as a particularly critical aspect, and a novel echo amplitude normalisation approach, termed the Robust Surface Normal (RSN) method, has been developed. Following the radar equation, a comprehensive radiometric calibration routine is introduced to account for all variables affecting the backscattered laser signal. Thereafter, a segmentation algorithm is developed, which utilises the raw 3D point clouds to estimate the normal for individual echoes based on the RSN method. The segmentation criterion is selected as the normal vector augmented by the calibrated backscatter signals. The developed segmentation routine aims to fully integrate FWF data to improve feature recognition and 3D object segmentation applications. The routine was tested over various feature types from two datasets with different properties to assess its potential. The results are compared to those delivered through utilizing only geometric information, without the additional FWF radiometric information, to assess performance over existing methods. The results approved the potential of the FWF additional observables to improve segmentation algorithms. The new approach was validated against manual segmentation results, revealing a successful automatic implementation and achieving an accuracy of 82%

Estratégia de Apoio á Marcha Humana Asistida por Andador Robótico Baseada em Forças de Interação

Este trabalho apresenta o desenvolvimento de um andador robótico para auxílio a pessoas com deficiência na marcha, para compensação funcional ou para reabilitação, com ênfase em uma estratégia de apoio à marcha humana baseada em forças de interação entre o usuário e o andador. Para isso, inicialmente é abordado o estudo de alguns dispositivos de ajuda à marcha, dentre os quais são destacados os andadores robóticos mais atuais e citados na literatura. A revisão bibliográfica dá ênfase aos andadores que utilizam sensores de força como canal de interação física. Neste contexto, é apresentado o Projeto de Pesquisa UFES Smart Walker, desenvolvido no Programa de Pós-Graduação em Engenharia Elétrica da Universidade Federal do Espírito Santo, o qual dá continuidade a um Projeto de Pesquisa espanhol SIMBIOSIS desenvolvido no Centro de Automática y Robótica do Consejo Superior de Investigaciones Cientificas CSIC. Para interagir com o usuário, o UFES Smart Walker está dotado de sensores de força triaxiais sob os apoios dos antebraços, sensor de varredura laser para detectar as pernas do usuário, e locomoção assistida por motores, além de eletrônica embarcada para o processamento dos sinais em tempo real. A fim de desenvolver uma interface mais natural entre o usuário e o andador, um rigoroso processamento de sinais baseado em filtros adaptativos é realizado. Este andador robótico proporciona uma ferramenta de reabilitação mais natural, segura e adaptada às necessidades do usuário, sendo que, a validação do dispositivo envolveu vários experimentos, nos quais são analisados todos os sinais do processo, com o objetivo de determinar o comportamento da estratégia de interação homem-máquina. O processo de validação foi dividido em três etapas: forças de interação entre o usuário e o andador, parâmetros relacionado com a marcha, e resposta dos controladores de alto e baixo nível