678 research outputs found
Autonomy Infused Teleoperation with Application to BCI Manipulation
Robot teleoperation systems face a common set of challenges including
latency, low-dimensional user commands, and asymmetric control inputs. User
control with Brain-Computer Interfaces (BCIs) exacerbates these problems
through especially noisy and erratic low-dimensional motion commands due to the
difficulty in decoding neural activity. We introduce a general framework to
address these challenges through a combination of computer vision, user intent
inference, and arbitration between the human input and autonomous control
schemes. Adjustable levels of assistance allow the system to balance the
operator's capabilities and feelings of comfort and control while compensating
for a task's difficulty. We present experimental results demonstrating
significant performance improvement using the shared-control assistance
framework on adapted rehabilitation benchmarks with two subjects implanted with
intracortical brain-computer interfaces controlling a seven degree-of-freedom
robotic manipulator as a prosthetic. Our results further indicate that shared
assistance mitigates perceived user difficulty and even enables successful
performance on previously infeasible tasks. We showcase the extensibility of
our architecture with applications to quality-of-life tasks such as opening a
door, pouring liquids from containers, and manipulation with novel objects in
densely cluttered environments
Hybrid Shared-Autonomy Architecture for Robot Teleoperation with Wearable Interface
Con la diffusione di sistemi robotici è aumentata la necessitàdi avere un controllo efficace da parte degli utenti, ottenuto tramite la condivisione tra utente e robot. In questo lavoro un sistema del genere di shared autonomy in grado di assistere l'utente in un task di presa è migliorato con l'introduzione di collision avoidance, collegando insieme i due attraverso l'archittetura ibrida proposta, la quale viene poi testata e i risultati riportati confermando l'efficacia
Trust-Based Control of (Semi)Autonomous Mobile Robotic Systems
Despite great achievements made in (semi)autonomous robotic systems, human participa-tion is still an essential part, especially for decision-making about the autonomy allocation of robots in complex and uncertain environments. However, human decisions may not be optimal due to limited cognitive capacities and subjective human factors. In human-robot interaction (HRI), trust is a major factor that determines humans use of autonomy. Over/under trust may lead to dispro-portionate autonomy allocation, resulting in decreased task performance and/or increased human workload. In this work, we develop automated decision-making aids utilizing computational trust models to help human operators achieve a more effective and unbiased allocation. Our proposed decision aids resemble the way that humans make an autonomy allocation decision, however, are unbiased and aim to reduce human workload, improve the overall performance, and result in higher acceptance by a human. We consider two types of autonomy control schemes for (semi)autonomous mobile robotic systems. The first type is a two-level control scheme which includes switches between either manual or autonomous control modes. For this type, we propose automated decision aids via a computational trust and self-confidence model. We provide analytical tools to investigate the steady-state effects of the proposed autonomy allocation scheme on robot performance and human workload. We also develop an autonomous decision pattern correction algorithm using a nonlinear model predictive control to help the human gradually adapt to a better allocation pattern. The second type is a mixed-initiative bilateral teleoperation control scheme which requires mixing of autonomous and manual control. For this type, we utilize computational two-way trust models. Here, mixed-initiative is enabled by scaling the manual and autonomous control inputs with a function of computational human-to-robot trust. The haptic force feedback cue sent by the robot is dynamically scaled with a function of computational robot-to-human trust to reduce humans physical workload. Using the proposed control schemes, our human-in-the-loop tests show that the trust-based automated decision aids generally improve the overall robot performance and reduce the operator workload compared to a manual allocation scheme. The proposed decision aids are also generally preferred and trusted by the participants. Finally, the trust-based control schemes are extended to the single-operator-multi-robot applications. A theoretical control framework is developed for these applications and the stability and convergence issues under the switching scheme between different robots are addressed via passivity based measures
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
Hybrid Shared-Autonomy Architecture for Robot Teleoperation with Wearable Interface
Con la diffusione di sistemi robotici è aumentata la necessitàdi avere un controllo efficace da parte degli utenti, ottenuto tramite la condivisione tra utente e robot. In questo lavoro un sistema del genere di shared autonomy in grado di assistere l'utente in un task di presa è migliorato con l'introduzione di collision avoidance, collegando insieme i due attraverso l'archittetura ibrida proposta, la quale viene poi testata e i risultati riportati confermando l'efficacia
- …