Fast human motion prediction for human-robot collaboration with wearable interfaces

In this paper, we aim at improving human motion prediction during human-robot collaboration in industrial facilities by exploiting contributions from both physical and physiological signals. Improved human-machine collaboration could prove useful in several areas, while it is crucial for interacting robots to understand human movement as soon as possible to avoid accidents and injuries. In this perspective, we propose a novel human-robot interface capable to anticipate the user intention while performing reaching movements on a working bench in order to plan the action of a collaborative robot. The proposed interface can find many applications in the Industry 4.0 framework, where autonomous and collaborative robots will be an essential part of innovative facilities. A motion intention prediction and a motion direction prediction levels have been developed to improve detection speed and accuracy. A Gaussian Mixture Model (GMM) has been trained with IMU and EMG data following an evidence accumulation approach to predict reaching direction. Novel dynamic stopping criteria have been proposed to flexibly adjust the trade-off between early anticipation and accuracy according to the application. The output of the two predictors has been used as external inputs to a Finite State Machine (FSM) to control the behaviour of a physical robot according to user's action or inaction. Results show that our system outperforms previous methods, achieving a real-time classification accuracy of $94.3\pm2.9\%$ after $160.0msec\pm80.0msec$ from movement onset

a human in the loop cyber physical system for collaborative assembly in smart manufacturing

Abstract Industry 4.0 rose with the introduction of cyber-physical systems (CPS) and Internet of things (IoT) inside manufacturing systems. CPS represent self-controlled physical processes, having tight networking capabilities and efficient interfaces for human interaction. The interactive dimension of CPS reaches its maximum when defined in terms of natural human-machine interfaces (NHMI), i.e., those reducing the technological barriers required for the interaction. This paper presents a NHMI bringing the human decision-making capabilities inside the cybernetic control loop of a smart manufacturing assembly system. The interface allows to control, coordinate and cooperate with an industrial cobot during the task execution

Study and development of sensorimotor interfaces for robotic human augmentation

This thesis presents my research contribution to robotics and haptics in the context of human augmentation. In particular, in this document, we are interested in bodily or sensorimotor augmentation, thus the augmentation of humans by supernumerary robotic limbs (SRL). The field of sensorimotor augmentation is new in robotics and thanks to the combination with neuroscience, great leaps forward have already been made in the past 10 years. All of the research work I produced during my Ph.D. focused on the development and study of fundamental technology for human augmentation by robotics: the sensorimotor interface. This new concept is born to indicate a wearable device which has two main purposes, the first is to extract the input generated by the movement of the user's body, and the second to provide the somatosensory system of the user with an haptic feedback. This thesis starts with an exploratory study of integration between robotic and haptic devices, intending to combine state-of-the-art devices. This allowed us to realize that we still need to understand how to improve the interface that will allow us to feel the agency when using an augmentative robot. At this point, the path of this thesis forks into two alternative ways that have been adopted to improve the interaction between the human and the robot. In this regard, the first path we presented tackles two aspects conerning the haptic feedback of sensorimotor interfaces, which are the choice of the positioning and the effectiveness of the discrete haptic feedback. In the second way we attempted to lighten a supernumerary finger, focusing on the agility of use and the lightness of the device. One of the main findings of this thesis is that haptic feedback is considered to be helpful by stroke patients, but this does not mitigate the fact that the cumbersomeness of the devices is a deterrent to their use. Preliminary results here presented show that both the path we chose to improve sensorimotor augmentation worked: the presence of the haptic feedback improves the performance of sensorimotor interfaces, the co-positioning of haptic feedback and the input taken from the human body can improve the effectiveness of these interfaces, and creating a lightweight version of a SRL is a viable solution for recovering the grasping function

Collaborative robot control with hand gestures

Mestrado de dupla diplomação com a Université Libre de TunisThis thesis focuses on hand gesture recognition by proposing an architecture to control a collaborative robot in real-time vision based on hand detection, tracking, and gesture recognition for interaction with an application via hand gestures. The first stage of our system allows detecting and tracking a bar e hand in a cluttered background using skin detection and contour comparison. The second stage allows recognizing hand gestures using a Machine learning method algorithm. Finally an interface has been developed to control the robot over. Our hand gesture recognition system consists of two parts, in the first part for every frame captured from a camera we extract the keypoints for every training image using a machine learning algorithm, and we appoint the keypoints from every image into a keypoint map. This map is treated as an input for our processing algorithm which uses several methods to recognize the fingers in each hand. In the second part, we use a 3D camera with Infrared capabilities to get a 3D model of the hand to implement it in our system, after that we track the fingers in each hand and recognize them which made it possible to count the extended fingers and to distinguish each finger pattern. An interface to control the robot has been made that utilizes the previous steps that gives a real-time process and a dynamic 3D representation.Esta dissertação trata do reconhecimento de gestos realizados com a mão humana, propondo uma arquitetura para interagir com um robô colaborativo, baseado em visão computacional, rastreamento e reconhecimento de gestos. O primeiro estágio do sistema desenvolvido permite detectar e rastrear a presença de uma mão em um fundo desordenado usando detecção de pele e comparação de contornos. A segunda fase permite reconhecer os gestos das mãos usando um algoritmo do método de aprendizado de máquina. Finalmente, uma interface foi desenvolvida para interagir com robô. O sistema de reconhecimento de gestos manuais está dividido em duas partes. Na primeira parte, para cada quadro capturado de uma câmera, foi extraído os pontos-chave de cada imagem de treinamento usando um algoritmo de aprendizado de máquina e nomeamos os pontos-chave de cada imagem em um mapa de pontos-chave. Este mapa é tratado como uma entrada para o algoritmo de processamento que usa vários métodos para reconhecer os dedos em cada mão. Na segunda parte, foi utilizado uma câmera 3D com recursos de infravermelho para obter um modelo 3D da mão para implementá-lo em no sistema desenvolvido, e então, foi realizado os rastreio dos dedos de cada mão seguido pelo reconhecimento que possibilitou contabilizar os dedos estendidos e para distinguir cada padrão de dedo. Foi elaborado uma interface para interagir com o robô manipulador que utiliza as etapas anteriores que fornece um processo em tempo real e uma representação 3D dinâmica

Physical human-robot collaboration: Robotic systems, learning methods, collaborative strategies, sensors, and actuators

This article presents a state-of-the-art survey on the robotic systems, sensors, actuators, and collaborative strategies for physical human-robot collaboration (pHRC). This article starts with an overview of some robotic systems with cutting-edge technologies (sensors and actuators) suitable for pHRC operations and the intelligent assist devices employed in pHRC. Sensors being among the essential components to establish communication between a human and a robotic system are surveyed. The sensor supplies the signal needed to drive the robotic actuators. The survey reveals that the design of new generation collaborative robots and other intelligent robotic systems has paved the way for sophisticated learning techniques and control algorithms to be deployed in pHRC. Furthermore, it revealed the relevant components needed to be considered for effective pHRC to be accomplished. Finally, a discussion of the major advances is made, some research directions, and future challenges are presented

Enabling Human-Robot Collaboration via Holistic Human Perception and Partner-Aware Control

As robotic technology advances, the barriers to the coexistence of humans and robots are slowly coming down. Application domains like elderly care, collaborative manufacturing, collaborative manipulation, etc., are considered the need of the hour, and progress in robotics holds the potential to address many societal challenges. The future socio-technical systems constitute of blended workforce with a symbiotic relationship between human and robot partners working collaboratively. This thesis attempts to address some of the research challenges in enabling human-robot collaboration. In particular, the challenge of a holistic perception of a human partner to continuously communicate his intentions and needs in real-time to a robot partner is crucial for the successful realization of a collaborative task. Towards that end, we present a holistic human perception framework for real-time monitoring of whole-body human motion and dynamics. On the other hand, the challenge of leveraging assistance from a human partner will lead to improved human-robot collaboration. In this direction, we attempt at methodically defining what constitutes assistance from a human partner and propose partner-aware robot control strategies to endow robots with the capacity to meaningfully engage in a collaborative task