3,558 research outputs found
Egocentric Spatial Representation in Action and Perception
Neuropsychological findings used to motivate the “two visual systems” hypothesis have been taken to endanger a pair of widely accepted claims about spatial representation in visual experience. The first is the claim that visual experience represents 3-D space around the perceiver using an egocentric frame of reference. The second is the claim that there is a constitutive link between the spatial contents of visual experience and the perceiver’s bodily actions. In this paper, I carefully assess three main sources of evidence for the two visual systems hypothesis and argue that the best interpretation of the evidence is in fact consistent with both claims. I conclude with some brief remarks on the relation between visual consciousness and rational agency
Visually-Guided Manipulation Techniques for Robotic Autonomous Underwater Panel Interventions
The long term of this ongoing research has to do with increasing the autonomy
levels for underwater intervention missions. Bearing in mind that
the speci c mission to face has been the intervention on a panel, in this
paper some results in di erent development stages are presented by using
the real mechatronics and the panel mockup. Furthermore, some details
are highlighted describing two methodologies implemented for the required
visually-guided manipulation algorithms, and also a roadmap explaining the
di erent testbeds used for experimental validation, in increasing complexity
order, are presented. It is worth mentioning that the aforementioned
results would be impossible without previous generated know-how for both,
the complete developed mechatronics for the autonomous underwater vehicle
for intervention, and the required 3D simulation tool. In summary, thanks
to the implemented approach, the intervention system is able to control the
way in which the gripper approximates and manipulates the two panel devices
(i.e. a valve and a connector) in autonomous manner and, results in di erent
scenarios demonstrate the reliability and feasibility of this autonomous
intervention system in water tank and pool conditions.This work was partly supported by Spanish Ministry of Research and
Innovation DPI2011-27977-C03 (TRITON Project) and DPI2014-57746-C3 (MERBOTS Project), by Foundation Caixa Castell o-Bancaixa and Universitat Jaume I grant PID2010-12, by Universitat Jaume I PhD grants PREDOC/2012/47 and PREDOC/2013/46, and by Generalitat Valenciana PhD grant ACIF/2014/298. We would like also to acknowledge the support of our partners inside the Spanish Coordinated Projects TRITON and MERBOTS: Universitat de les Illes Balears, UIB (subprojects VISUAL2 and SUPERION) and Universitat de Girona, UdG (subprojects COMAROB and ARCHROV)
GoferBot: A Visual Guided Human-Robot Collaborative Assembly System
The current transformation towards smart manufacturing has led to a growing
demand for human-robot collaboration (HRC) in the manufacturing process.
Perceiving and understanding the human co-worker's behaviour introduces
challenges for collaborative robots to efficiently and effectively perform
tasks in unstructured and dynamic environments. Integrating recent data-driven
machine vision capabilities into HRC systems is a logical next step in
addressing these challenges. However, in these cases, off-the-shelf components
struggle due to generalisation limitations. Real-world evaluation is required
in order to fully appreciate the maturity and robustness of these approaches.
Furthermore, understanding the pure-vision aspects is a crucial first step
before combining multiple modalities in order to understand the limitations. In
this paper, we propose GoferBot, a novel vision-based semantic HRC system for a
real-world assembly task. It is composed of a visual servoing module that
reaches and grasps assembly parts in an unstructured multi-instance and dynamic
environment, an action recognition module that performs human action prediction
for implicit communication, and a visual handover module that uses the
perceptual understanding of human behaviour to produce an intuitive and
efficient collaborative assembly experience. GoferBot is a novel assembly
system that seamlessly integrates all sub-modules by utilising implicit
semantic information purely from visual perception
Towards Reuse and Recycling of Lithium-ion Batteries: Tele-robotics for Disassembly of Electric Vehicle Batteries
Disassembly of electric vehicle batteries is a critical stage in recovery,
recycling and re-use of high-value battery materials, but is complicated by
limited standardisation, design complexity, compounded by uncertainty and
safety issues from varying end-of-life condition. Telerobotics presents an
avenue for semi-autonomous robotic disassembly that addresses these challenges.
However, it is suggested that quality and realism of the user's haptic
interactions with the environment is important for precise, contact-rich and
safety-critical tasks. To investigate this proposition, we demonstrate the
disassembly of a Nissan Leaf 2011 module stack as a basis for a comparative
study between a traditional asymmetric haptic-'cobot' master-slave framework
and identical master and slave cobots based on task completion time and success
rate metrics. We demonstrate across a range of disassembly tasks a time
reduction of 22%-57% is achieved using identical cobots, yet this improvement
arises chiefly from an expanded workspace and 1:1 positional mapping, and
suffers a 10-30% reduction in first attempt success rate. For unbolting and
grasping, the realism of force feedback was comparatively less important than
directional information encoded in the interaction, however, 1:1 force mapping
strengthened environmental tactile cues for vacuum pick-and-place and contact
cutting tasks.Comment: 21 pages, 12 figures, Submitted to Frontiers in Robotics and AI;
Human-Robot Interactio
A neural network-based exploratory learning and motor planning system for co-robots
Collaborative robots, or co-robots, are semi-autonomous robotic agents designed to work alongside humans in shared workspaces. To be effective, co-robots require the ability to respond and adapt to dynamic scenarios encountered in natural environments. One way to achieve this is through exploratory learning, or "learning by doing," an unsupervised method in which co-robots are able to build an internal model for motor planning and coordination based on real-time sensory inputs. In this paper, we present an adaptive neural network-based system for co-robot control that employs exploratory learning to achieve the coordinated motor planning needed to navigate toward, reach for, and grasp distant objects. To validate this system we used the 11-degrees-of-freedom RoPro Calliope mobile robot. Through motor babbling of its wheels and arm, the Calliope learned how to relate visual and proprioceptive information to achieve hand-eye-body coordination. By continually evaluating sensory inputs and externally provided goal directives, the Calliope was then able to autonomously select the appropriate wheel and joint velocities needed to perform its assigned task, such as following a moving target or retrieving an indicated object
Exploring Robot Teleoperation in Virtual Reality
This thesis presents research on VR-based robot teleoperation with a focus on remote environment visualisation in virtual reality, the effects of remote environment reconstruction scale in virtual reality on the human-operator's ability to control the robot and human-operator's visual attention patterns when teleoperating a robot from virtual reality.
A VR-based robot teleoperation framework was developed, it is compatible with various robotic systems and cameras, allowing for teleoperation and supervised control with any ROS-compatible robot and visualisation of the environment through any ROS-compatible RGB and RGBD cameras. The framework includes mapping, segmentation, tactile exploration, and non-physically demanding VR interface navigation and controls through any Unity-compatible VR headset and controllers or haptic devices.
Point clouds are a common way to visualise remote environments in 3D, but they often have distortions and occlusions, making it difficult to accurately represent objects' textures. This can lead to poor decision-making during teleoperation if objects are inaccurately represented in the VR reconstruction. A study using an end-effector-mounted RGBD camera with OctoMap mapping of the remote environment was conducted to explore the remote environment with fewer point cloud distortions and occlusions while using a relatively small bandwidth. Additionally, a tactile exploration study proposed a novel method for visually presenting information about objects' materials in the VR interface, to improve the operator's decision-making and address the challenges of point cloud visualisation.
Two studies have been conducted to understand the effect of virtual world dynamic scaling on teleoperation flow. The first study investigated the use of rate mode control with constant and variable mapping of the operator's joystick position to the speed (rate) of the robot's end-effector, depending on the virtual world scale. The results showed that variable mapping allowed participants to teleoperate the robot more effectively but at the cost of increased perceived workload.
The second study compared how operators used a virtual world scale in supervised control, comparing the virtual world scale of participants at the beginning and end of a 3-day experiment. The results showed that as operators got better at the task they as a group used a different virtual world scale, and participants' prior video gaming experience also affected the virtual world scale chosen by operators.
Similarly, the human-operator's visual attention study has investigated how their visual attention changes as they become better at teleoperating a robot using the framework.
The results revealed the most important objects in the VR reconstructed remote environment as indicated by operators' visual attention patterns as well as their visual priorities shifts as they got better at teleoperating the robot. The study also demonstrated that operators’ prior video gaming experience affects their ability to teleoperate the robot and their visual attention behaviours
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