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

Robot swarming applications

This paper discusses the different modes of operation of a swarm of robots: (i) non-communicative swarming, (ii) communicative swarming, (iii) networking, (iv) olfactory-based navigation and (v) assistive swarming. I briefly present the state of the art in swarming and outline the major techniques applied for each mode of operation and discuss the related problems and expected results

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

Reflection-Aware Sound Source Localization

We present a novel, reflection-aware method for 3D sound localization in indoor environments. Unlike prior approaches, which are mainly based on continuous sound signals from a stationary source, our formulation is designed to localize the position instantaneously from signals within a single frame. We consider direct sound and indirect sound signals that reach the microphones after reflecting off surfaces such as ceilings or walls. We then generate and trace direct and reflected acoustic paths using inverse acoustic ray tracing and utilize these paths with Monte Carlo localization to estimate a 3D sound source position. We have implemented our method on a robot with a cube-shaped microphone array and tested it against different settings with continuous and intermittent sound signals with a stationary or a mobile source. Across different settings, our approach can localize the sound with an average distance error of 0.8m tested in a room of 7m by 7m area with 3m height, including a mobile and non-line-of-sight sound source. We also reveal that the modeling of indirect rays increases the localization accuracy by 40% compared to only using direct acoustic rays.Comment: Submitted to ICRA 2018. The working video is available at (https://youtu.be/TkQ36lMEC-M

Dynamic update of a virtual cell for programming and safe monitoring of an industrial robot

A hardware/software architecture for robot motion planning and on-line safe monitoring has been developed with the objective to assure high flexibility in production control, safety for workers and machinery, with user-friendly interface. The architecture, developed using Microsoft Robotics Developers Studio and implemented for a six-dof COMAU NS 12 robot, established a bidirectional communication between the robot controller and a virtual replica of the real robotic cell. The working space of the real robot can then be easily limited for safety reasons by inserting virtual objects (or sensors) in such a virtual environment. This paper investigates the possibility to achieve an automatic, dynamic update of the virtual cell by using a low cost depth sensor (i.e., a commercial Microsoft Kinect) to detect the presence of completely unknown objects, moving inside the real cell. The experimental tests show that the developed architecture is able to recognize variously shaped mobile objects inside the monitored area and let the robot stop before colliding with them, if the objects are not too small

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available