4,451 research outputs found
A Depth Space Approach for Evaluating Distance to Objects -- with Application to Human-Robot Collision Avoidance
We present a novel approach to estimate the distance between a generic point in the Cartesian space and objects detected with a depth sensor. This information is crucial in many robotic applications, e.g., for collision avoidance, contact point identification, and augmented reality. The key idea is to perform all distance evaluations directly in the depth space. This allows distance estimation by considering also the frustum generated by the pixel on the depth image, which takes into account both the pixel size and the occluded points. Different techniques to aggregate distance data coming from multiple object points are proposed. We compare the Depth space approach with the commonly used Cartesian space or Configuration space approaches, showing that the presented method provides better results and faster execution times. An application to human-robot collision avoidance using a KUKA LWR IV robot and a Microsoft Kinect sensor illustrates the effectiveness of the approach
Real-time computation of distance to dynamic obstacles with multiple depth sensors
We present an efficient method to evaluate distances between dynamic obstacles and a number of points of interests (e.g., placed on the links of a robot) when using multiple depth cameras. A depth-space oriented discretization of the Cartesian space is introduced that represents at best the workspace monitored by a depth camera, including occluded points. A depth grid map can be initialized off line from the arrangement of the multiple depth cameras, and its peculiar search characteristics allows fusing on line the information given by the multiple sensors in a very simple and fast way. The real-time performance of the proposed approach is shown by means of collision avoidance experiments where two Kinect sensors monitor a human-robot coexistence task
Human Motion Trajectory Prediction: A Survey
With growing numbers of intelligent autonomous systems in human environments,
the ability of such systems to perceive, understand and anticipate human
behavior becomes increasingly important. Specifically, predicting future
positions of dynamic agents and planning considering such predictions are key
tasks for self-driving vehicles, service robots and advanced surveillance
systems. This paper provides a survey of human motion trajectory prediction. We
review, analyze and structure a large selection of work from different
communities and propose a taxonomy that categorizes existing methods based on
the motion modeling approach and level of contextual information used. We
provide an overview of the existing datasets and performance metrics. We
discuss limitations of the state of the art and outline directions for further
research.Comment: Submitted to the International Journal of Robotics Research (IJRR),
37 page
Human-robot contactless collaboration with mixed reality interface
A control system based on multiple sensors is proposed for the safe collaboration of a robot with a human. New constrained and contactless human-robot coordinated motion tasks are defined to control the robot end-effector so as to maintain a desired relative position to the human head while pointing at it. Simultaneously, the robot avoids any collision with the operator and with nearby static or dynamic obstacles, based on distance compu- tations performed in the depth space of a RGB-D sensor. The various tasks are organized with priorities and executed under hard joint bounds using the Saturation in the Null Space (SNS) algorithm. A direct human-robot communication is integrated within a mixed reality interface using a stereo camera and an augmented reality system. The proposed system is significant for on-line, collaborative quality assessment phases in a manu- facturing process. Various experimental validation scenarios using a 7-dof KUKA LWR4 robot are presented
Safe physical HRI: Toward a unified treatment of speed and separation monitoring together with power and force limiting
So-called collaborative robots are a current trend in industrial robotics.
However, they still face many problems in practical application such as reduced
speed to ascertain their collaborativeness. The standards prescribe two
regimes: (i) speed and separation monitoring and (ii) power and force limiting,
where the former requires reliable estimation of distances between the robot
and human body parts and the latter imposes constraints on the energy absorbed
during collisions prior to robot stopping. Following the standards, we deploy
the two collaborative regimes in a single application and study the performance
in a mock collaborative task under the individual regimes, including
transitions between them. Additionally, we compare the performance under
"safety zone monitoring" with keypoint pair-wise separation distance assessment
relying on an RGB-D sensor and skeleton extraction algorithm to track human
body parts in the workspace. Best performance has been achieved in the
following setting: robot operates at full speed until a distance threshold
between any robot and human body part is crossed; then, reduced robot speed per
power and force limiting is triggered. Robot is halted only when the operator's
head crosses a predefined distance from selected robot parts. We demonstrate
our methodology on a setup combining a KUKA LBR iiwa robot, Intel RealSense
RGB-D sensor and OpenPose for human pose estimation.Comment: 8 pages, 6 figure
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