51,925 research outputs found
UAV human teleoperation using event-based and frame-based cameras
Teleoperation is a crucial aspect for human-robot
interaction with unmanned aerial vehicles (UAVs) applications.
Fast perception processing is required to ensure robustness,
precision, and safety. Event cameras are neuromorphic sensors
that provide low latency response, high dynamic range and low
power consumption. Although classical image-based methods
have been extensively used for human-robot interaction tasks,
responsiveness is limited by their processing rates. This paper
presents a human-robot teleoperation scheme for UAVs that
exploits the advantages of both traditional and event cameras.
The proposed scheme was tested in teleoperation missions where
the pose of a multirotor robot is controlled in real time using
human gestures detected from events.European Union (UE). H2020 2019-87147Consejo Europeo de Investigación 78824
Human-robot coexistence and interaction in open industrial cells
Recent research results on human\u2013robot interaction and collaborative robotics are leaving behind the traditional paradigm of robots living in a separated space inside safety cages, allowing humans and robot to work together for completing an increasing number of complex industrial tasks. In this context, safety of the human operator is a main concern. In this paper, we present a framework for ensuring human safety in a robotic cell that allows human\u2013robot coexistence and dependable interaction. The framework is based on a layered control architecture that exploits an effective algorithm for online monitoring of relative human\u2013robot distance using depth sensors. This method allows to modify in real time the robot behavior depending on the user position, without limiting the operative robot workspace in a too conservative way. In order to guarantee redundancy and diversity at the safety level, additional certified laser scanners monitor human\u2013robot proximity in the cell and safe communication protocols and logical units are used for the smooth integration with an industrial software for safe low-level robot control. The implemented concept includes a smart human-machine interface to support in-process collaborative activities and for a contactless interaction with gesture recognition of operator commands. Coexistence and interaction are illustrated and tested in an industrial cell, in which a robot moves a tool that measures the quality of a polished metallic part while the operator performs a close evaluation of the same workpiece
Forming Human-Robot Teams Across Time and Space
NASA pushes telerobotics to distances that span the Solar System. At this scale, time of flight for communication is limited by the speed of light, inducing long time delays, narrow bandwidth and the real risk of data disruption. NASA also supports missions where humans are in direct contact with robots during extravehicular activity (EVA), giving a range of zero to hundreds of millions of miles for NASA s definition of "tele". . Another temporal variable is mission phasing. NASA missions are now being considered that combine early robotic phases with later human arrival, then transition back to robot only operations. Robots can preposition, scout, sample or construct in advance of human teammates, transition to assistant roles when the crew are present, and then become care-takers when the crew returns to Earth. This paper will describe advances in robot safety and command interaction approaches developed to form effective human-robot teams, overcoming challenges of time delay and adapting as the team transitions from robot only to robots and crew. The work is predicated on the idea that when robots are alone in space, they are still part of a human-robot team acting as surrogates for people back on Earth or in other distant locations. Software, interaction modes and control methods will be described that can operate robots in all these conditions. A novel control mode for operating robots across time delay was developed using a graphical simulation on the human side of the communication, allowing a remote supervisor to drive and command a robot in simulation with no time delay, then monitor progress of the actual robot as data returns from the round trip to and from the robot. Since the robot must be responsible for safety out to at least the round trip time period, the authors developed a multi layer safety system able to detect and protect the robot and people in its workspace. This safety system is also running when humans are in direct contact with the robot, so it involves both internal fault detection as well as force sensing for unintended external contacts. The designs for the supervisory command mode and the redundant safety system will be described. Specific implementations were developed and test results will be reported. Experiments were conducted using terrestrial analogs for deep space missions, where time delays were artificially added to emulate the longer distances found in space
A Learning-Based Framework for Safe Human-Robot Collaboration with Multiple Backup Control Barrier Functions
Ensuring robot safety in complex environments is a difficult task due to
actuation limits, such as torque bounds. This paper presents a safety-critical
control framework that leverages learning-based switching between multiple
backup controllers to formally guarantee safety under bounded control inputs
while satisfying driver intention. By leveraging backup controllers designed to
uphold safety and input constraints, backup control barrier functions (BCBFs)
construct implicitly defined control invariance sets via a feasible quadratic
program (QP). However, BCBF performance largely depends on the design and
conservativeness of the chosen backup controller, especially in our setting of
human-driven vehicles in complex, e.g, off-road, conditions. While
conservativeness can be reduced by using multiple backup controllers,
determining when to switch is an open problem. Consequently, we develop a
broadcast scheme that estimates driver intention and integrates BCBFs with
multiple backup strategies for human-robot interaction. An LSTM classifier uses
data inputs from the robot, human, and safety algorithms to continually choose
a backup controller in real-time. We demonstrate our method's efficacy on a
dual-track robot in obstacle avoidance scenarios. Our framework guarantees
robot safety while adhering to driver intention
Towards Connecting Control to Perception: High-Performance Whole-Body Collision Avoidance Using Control-Compatible Obstacles
One of the most important aspects of autonomous systems is safety. This
includes ensuring safe human-robot and safe robot-environment interaction when
autonomously performing complex tasks or in collaborative scenarios. Although
several methods have been introduced to tackle this, most are unsuitable for
real-time applications and require carefully hand-crafted obstacle
descriptions. In this work, we propose a method combining high-frequency and
real-time self and environment collision avoidance of a robotic manipulator
with low-frequency, multimodal, and high-resolution environmental perceptions
accumulated in a digital twin system. Our method is based on geometric
primitives, so-called primitive skeletons. These, in turn, are
information-compressed and real-time compatible digital representations of the
robot's body and environment, automatically generated from ultra-realistic
virtual replicas of the real world provided by the digital twin. Our approach
is a key enabler for closing the loop between environment perception and robot
control by providing the millisecond real-time control stage with a current and
accurate world description, empowering it to react to environmental changes. We
evaluate our whole-body collision avoidance on a 9-DOFs robot system through
five experiments, demonstrating the functionality and efficiency of our
framework.Comment: Accepted for publication at 2023 IEEE/RSJ International Conference on
Intelligent Robots and Systems (IROS 2023
Goal-Conditioned Reinforcement Learning within a Human-Robot Disassembly Environment
The introduction of collaborative robots in industrial environments reinforces the need to provide these robots with better cognition to accomplish their tasks while fostering worker safety without entering into safety shutdowns that reduce workflow and production times. This paper presents a novel strategy that combines the execution of contact-rich tasks, namely disassembly, with real-time collision avoidance through machine learning for safe human-robot interaction. Specifically, a goal-conditioned reinforcement learning approach is proposed, in which the removal direction of a peg, of varying friction, tolerance, and orientation, is subject to the location of a human collaborator with respect to a 7-degree-of-freedom manipulator at each time step. For this purpose, the suitability of three state-of-the-art actor-critic algorithms is evaluated, and results from simulation and real-world experiments are presented. In reality, the policy’s deployment is achieved through a new scalable multi-control framework that allows a direct transfer of the control policy to the robot and reduces response times. The results show the effectiveness, generalization, and transferability of the proposed approach with two collaborative robots against static and dynamic obstacles, leveraging the set of available solutions in non-monotonic tasks to avoid a potential collision with the human worker
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