761 research outputs found
Shared-Control Teleoperation Paradigms on a Soft Growing Robot Manipulator
Semi-autonomous telerobotic systems allow both humans and robots to exploit
their strengths, while enabling personalized execution of a task. However, for
new soft robots with degrees of freedom dissimilar to those of human operators,
it is unknown how the control of a task should be divided between the human and
robot. This work presents a set of interaction paradigms between a human and a
soft growing robot manipulator, and demonstrates them in both real and
simulated scenarios. The robot can grow and retract by eversion and inversion
of its tubular body, a property we exploit to implement interaction paradigms.
We implemented and tested six different paradigms of human-robot interaction,
beginning with full teleoperation and gradually adding automation to various
aspects of the task execution. All paradigms were demonstrated by two expert
and two naive operators. Results show that humans and the soft robot
manipulator can split control along degrees of freedom while acting
simultaneously. In the simple pick-and-place task studied in this work,
performance improves as the control is gradually given to the robot, because
the robot can correct certain human errors. However, human engagement and
enjoyment may be maximized when the task is at least partially shared. Finally,
when the human operator is assisted by haptic feedback based on soft robot
position errors, we observed that the improvement in performance is highly
dependent on the expertise of the human operator.Comment: 15 pages, 14 figure
Development of an open access system for remote operation of robotic manipulators
Mestrado de dupla diplomação com a UTFPR - Universidade Tecnológica Federal do ParanáExploring the realms of research, training, and learning in the field of robotic systems poses obstacles for institutions lacking the necessary infrastructure. The significant investment required to acquire physical robotic systems often limits access and hinders progress in these areas. While robotic simulation platforms provide a virtual environment for experimentation, the potential of remote robotic environments surpasses this by enabling users to interact with real robotic systems during training and research activities. This way, users, including students and researchers, can engage in a virtual experience that transcends geographical boundaries, connecting them to real-world robotic systems though the Internet. By bridging the gap between virtual and physical worlds, remote environments offer a more practical and immersive experience, and open up new horizons for collaborative research and training. Democratizing access to these technologies means
empower educational institutions and research centers to engage in practical and handson learning experiences. However, the implementation of remote robotic environments comes with its own set of technical challenges: communication, security, stability and access. In light of these challenges, a ROS-based system has been developed, providing open access with promising results (low delay and run-time visualization). This system enables remote control of robotic manipulators and has been successfully validated through the
remote operation of a real UR3 manipulator.Explorar as áreas de pesquisa, treinamento e aprendizado no campo de sistemas robóticos apresenta obstáculos para instituições que não possuem a infraestrutura necessária. O investimento significativo exigido para adquirir sistemas robóticos físicos muitas vezes limita o acesso e dificulta o progresso nessas áreas. Embora as plataformas de simulação robótica forneçam um ambiente virtual para experimentação, o potencial dos ambientes robóticos remotos vai além disso, permitindo que os usuários interajam com sistemas robóticos reais durante atividades de treinamento e pesquisa. Dessa forma, os usuários, incluindo estudantes e pesquisadores, podem participar de uma experiência virtual que transcende as fronteiras geográficas, conectando-os a sistemas robóticos do mundo real por meio da Internet. Ao estabelecer uma ponte entre os mundos virtual e físico, os ambientes remotos oferecem uma experiência mais prática e imersiva, abrindo novos horizontes para a pesquisa colaborativa e o treinamento. Democratizar o acesso a essas tecnologias significa capacitar instituições educacionais e centros de pesquisa a se envolverem em experiências práticas e de aprendizado prático. No entanto, a implementação de ambientes robóticos
remotos traz consigo um conjunto próprio de desafios técnicos: comunicação, segurança, estabilidade e acesso. Diante desses desafios, foi desenvolvida uma plataforma baseada em ROS, oferecendo acesso aberto com resultados promissores (baixo delay e visualização em run-time). Essa plataforma possibilita o controle remoto de manipuladores robóticos e foi validada com sucesso por meio da operação remota de um manipulador UR3 real
Safety-Aware Human-Robot Collaborative Transportation and Manipulation with Multiple MAVs
Human-robot interaction will play an essential role in various industries and
daily tasks, enabling robots to effectively collaborate with humans and reduce
their physical workload. Most of the existing approaches for physical
human-robot interaction focus on collaboration between a human and a single
ground robot. In recent years, very little progress has been made in this
research area when considering aerial robots, which offer increased versatility
and mobility compared to their grounded counterparts. This paper proposes a
novel approach for safe human-robot collaborative transportation and
manipulation of a cable-suspended payload with multiple aerial robots. We
leverage the proposed method to enable smooth and intuitive interaction between
the transported objects and a human worker while considering safety constraints
during operations by exploiting the redundancy of the internal transportation
system. The key elements of our system are (a) a distributed payload external
wrench estimator that does not rely on any force sensor; (b) a 6D admittance
controller for human-aerial-robot collaborative transportation and
manipulation; (c) a safety-aware controller that exploits the internal system
redundancy to guarantee the execution of additional tasks devoted to preserving
the human or robot safety without affecting the payload trajectory tracking or
quality of interaction. We validate the approach through extensive simulation
and real-world experiments. These include as well the robot team assisting the
human in transporting and manipulating a load or the human helping the robot
team navigate the environment. To the best of our knowledge, this work is the
first to create an interactive and safety-aware approach for quadrotor teams
that physically collaborate with a human operator during transportation and
manipulation tasks.Comment: Guanrui Li and Xinyang Liu contributed equally to this pape
Machine Learning Meets Advanced Robotic Manipulation
Automated industries lead to high quality production, lower manufacturing
cost and better utilization of human resources. Robotic manipulator arms have
major role in the automation process. However, for complex manipulation tasks,
hard coding efficient and safe trajectories is challenging and time consuming.
Machine learning methods have the potential to learn such controllers based on
expert demonstrations. Despite promising advances, better approaches must be
developed to improve safety, reliability, and efficiency of ML methods in both
training and deployment phases. This survey aims to review cutting edge
technologies and recent trends on ML methods applied to real-world manipulation
tasks. After reviewing the related background on ML, the rest of the paper is
devoted to ML applications in different domains such as industry, healthcare,
agriculture, space, military, and search and rescue. The paper is closed with
important research directions for future works
Collaborative Trolley Transportation System with Autonomous Nonholonomic Robots
Cooperative object transportation using multiple robots has been intensively
studied in the control and robotics literature, but most approaches are either
only applicable to omnidirectional robots or lack a complete navigation and
decision-making framework that operates in real time. This paper presents an
autonomous nonholonomic multi-robot system and an end-to-end hierarchical
autonomy framework for collaborative luggage trolley transportation. This
framework finds kinematic-feasible paths, computes online motion plans, and
provides feedback that enables the multi-robot system to handle long lines of
luggage trolleys and navigate obstacles and pedestrians while dealing with
multiple inherently complex and coupled constraints. We demonstrate the
designed collaborative trolley transportation system through practical
transportation tasks, and the experiment results reveal their effectiveness and
reliability in complex and dynamic environments
Surgical Subtask Automation for Intraluminal Procedures using Deep Reinforcement Learning
Intraluminal procedures have opened up a new sub-field of minimally invasive surgery that use flexible instruments to navigate through complex luminal structures of the body, resulting in reduced invasiveness and improved patient benefits. One of the major challenges in this field is the accurate and precise control of the instrument inside the human body. Robotics has emerged as a promising solution to this problem. However, to achieve successful robotic intraluminal interventions, the control of the instrument needs to be automated to a large extent. The thesis first examines the state-of-the-art in intraluminal surgical robotics and identifies the key challenges in this field, which include the need for safe and effective tool manipulation, and the ability to adapt to unexpected changes in the luminal environment. To address these challenges, the thesis proposes several levels of autonomy that enable the robotic system to perform individual subtasks autonomously, while still allowing the surgeon to retain overall control of the procedure. The approach facilitates the development of specialized algorithms such as Deep Reinforcement Learning (DRL) for subtasks like navigation and tissue manipulation to produce robust surgical gestures. Additionally, the thesis proposes a safety framework that provides formal guarantees to prevent risky actions. The presented approaches are evaluated through a series of experiments using simulation and robotic platforms. The experiments demonstrate that subtask automation can improve the accuracy and efficiency of tool positioning and tissue manipulation, while also reducing the cognitive load on the surgeon. The results of this research have the potential to improve the reliability and safety of intraluminal surgical interventions, ultimately leading to better outcomes for patients and surgeons
The classification and new trends of shared control strategies in telerobotic systems: A survey
Shared control, which permits a human operator and an autonomous controller to share the control of a telerobotic system, can reduce the operator's workload and/or improve performances during the execution of tasks. Due to the great benefits of combining the human intelligence with the higher power/precision abilities of robots, the shared control architecture occupies a wide spectrum among telerobotic systems. Although various shared control strategies have been proposed, a systematic overview to tease out the relation among different strategies is still absent. This survey, therefore, aims to provide a big picture for existing shared control strategies. To achieve this, we propose a categorization method and classify the shared control strategies into 3 categories: Semi-Autonomous control (SAC), State-Guidance Shared Control (SGSC), and State-Fusion Shared Control (SFSC), according to the different sharing ways between human operators and autonomous controllers. The typical scenarios in using each category are listed and the advantages/disadvantages and open issues of each category are discussed. Then, based on the overview of the existing strategies, new trends in shared control strategies, including the “autonomy from learning” and the “autonomy-levels adaptation,” are summarized and discussed
Collaborative and Cooperative Robotics Applications using Visual Perception
The objective of this Thesis is to develop novel integrated strategies for collaborative and cooperative robotic applications. Commonly, industrial robots operate in structured environments and in work-cell separated from human operators. Nowadays, collaborative robots have the capacity of sharing the workspace and collaborate with humans or other robots to perform complex tasks. These robots often operate in an unstructured environment, whereby they need sensors and algorithms to get information about environment changes.
Advanced vision and control techniques have been analyzed to evaluate their performance and their applicability to industrial tasks. Then, some selected techniques have been applied for the first time to an industrial context. A Peg-in-Hole task has been chosen as first case study, since it has been extensively studied but still remains challenging: it requires accuracy both in the determination of the hole poses and in the robot positioning.
Two solutions have been developed and tested. Experimental results have been discussed to highlight the advantages and disadvantages of each technique. Grasping partially known objects in unstructured environments is one of the most challenging issues in robotics. It is a complex task and requires to address multiple subproblems, in order to be accomplished, including object localization and grasp pose detection.
Also for this class of issues some vision techniques have been analyzed. One of these has been adapted to be used in industrial scenarios. Moreover, as a second case study, a robot-to-robot object handover task in a partially structured environment and in the absence of explicit communication between the robots has been developed and validated.
Finally, the two case studies have been integrated in two real industrial setups to demonstrate the applicability of the strategies to solving industrial problems
Safe navigation and human-robot interaction in assistant robotic applications
L'abstract è presente nell'allegato / the abstract is in the attachmen
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