856 research outputs found
Deep Haptic Model Predictive Control for Robot-Assisted Dressing
Robot-assisted dressing offers an opportunity to benefit the lives of many
people with disabilities, such as some older adults. However, robots currently
lack common sense about the physical implications of their actions on people.
The physical implications of dressing are complicated by non-rigid garments,
which can result in a robot indirectly applying high forces to a person's body.
We present a deep recurrent model that, when given a proposed action by the
robot, predicts the forces a garment will apply to a person's body. We also
show that a robot can provide better dressing assistance by using this model
with model predictive control. The predictions made by our model only use
haptic and kinematic observations from the robot's end effector, which are
readily attainable. Collecting training data from real world physical
human-robot interaction can be time consuming, costly, and put people at risk.
Instead, we train our predictive model using data collected in an entirely
self-supervised fashion from a physics-based simulation. We evaluated our
approach with a PR2 robot that attempted to pull a hospital gown onto the arms
of 10 human participants. With a 0.2s prediction horizon, our controller
succeeded at high rates and lowered applied force while navigating the garment
around a persons fist and elbow without getting caught. Shorter prediction
horizons resulted in significantly reduced performance with the sleeve catching
on the participants' fists and elbows, demonstrating the value of our model's
predictions. These behaviors of mitigating catches emerged from our deep
predictive model and the controller objective function, which primarily
penalizes high forces.Comment: 8 pages, 12 figures, 1 table, 2018 IEEE International Conference on
Robotics and Automation (ICRA
Quantification of human operator skill in a driving simulator for applications in human adaptive mechatronics
Nowadays, the Human Machine System (HMS) is considered to be a proven technology, and now plays an important role in various human activities. However,
this system requires that only a human has an in-depth understanding of the machine
operation, and is thus a one-way relationship. Therefore, researchers have recently
developed Human Adaptive Mechatronics (HAM) to overcome this problem and
balance the roles of the human and machine in any HMS. HAM is different compared
to ordinary HMS in terms of its ability to adapt to changes in its surroundings and the
changing skill level of humans. Nonetheless, the main problem with HAM is in
quantifying the human skill level in machine manipulation as part of human
recognition. Therefore, this thesis deals with a proposed formula to quantify and
classify the skill of the human operator in driving a car as an example application
between humans and machines. The formula is evaluated using the logical conditions
and the definition of skill in HAM in terms of time and error. The skill indices are
classified into five levels: Very Highly Skilled, Highly Skilled, Medium Skilled, Low
Skilled and Very Low Skilled.
Driving was selected because it is considered to be a complex mechanical task that
involves skill, a human and a machine. However, as the safety of the human subjects
when performing the required tasks in various situations must be considered, a driving
simulator was used. The simulator was designed using Microsoft Visual Studio,
controlled using a USB steering wheel and pedals, as was able to record the human
ii
path and include the desired effects on the road. Thus, two experiments involving the
driving simulator were performed; 20 human subjects with a varying numbers of
years experience in driving and gaming were used in the experiments. In the first
experiment, the subjects were asked to drive in Expected and Guided Conditions
(EGC). Five guided tracks were used to show the variety of driving skill: straight,
circular, elliptical, square and triangular. The results of this experiment indicate that
the tracking error is inversely proportional to the elapsed time. In second experiment,
the subjects experienced Sudden Transitory Conditions (STC). Two types of
unexpected situations in driving were used: tyre puncture and slippery surface. This
experiment demonstrated that the tracking error is not directly proportional to the
elapsed time. Both experiments also included the correlation between experience and
skill. For the first time, a new skill index formula is proposed based on the logical
conditions and the definition of skill in HAM
A Review of Shared Control for Automated Vehicles: Theory and Applications
The last decade has shown an increasing interest on advanced driver assistance systems (ADAS) based on shared control, where automation is continuously supporting the driver at the control level with an adaptive authority. A first look at the literature offers two main research directions: 1) an ongoing effort to advance the theoretical comprehension of shared control, and 2) a diversity of automotive system applications with an increasing number of works in recent years. Yet, a global synthesis on these efforts is not available. To this end, this article covers the complete field of shared control in automated vehicles with an emphasis on these aspects: 1) concept, 2) categories, 3) algorithms, and 4) status of technology. Articles from the literature are classified in theory- and application-oriented contributions. From these, a clear distinction is found between coupled and uncoupled shared control. Also, model-based and model-free algorithms from these two categories are evaluated separately with a focus on systems using the steering wheel as the control interface. Model-based controllers tested by at least one real driver are tabulated to evaluate the performance of such systems. Results show that the inclusion of a driver model helps to reduce the conflicts at the steering. Also, variables such as driver state, driver effort, and safety indicators have a high impact on the calculation of the authority. Concerning the evaluation, driver-in-the-loop simulators are the most common platforms, with few works performed in real vehicles. Implementation in experimental vehicles is expected in the upcoming years.This work was supported in part by the ECSEL Joint Undertaking, which funded the PRYSTINE project under Grant 783190, and in part by the AUTOLIB project (ELKARTEK 2019 ref. KK-2019/00035; Gobierno Vasco Dpto. Desarrollo económico e infraestructuras)
- …