Saeed Yahyanejad

An Autonomous Multi-UAV System for Search and Rescue

This paper proposes and evaluates a modular architecture of an autonomous unmanned aerial vehicle (UAV) system for search and rescue missions. Multiple multicopters are coordinated using a distributed control system. The system is implemented in the Robot Operating System (ROS) and is capable of providing a real-time video stream from a UAV to one or more base stations using a wireless communications infrastructure. The system supports a heterogeneous set of UAVs and camera sensors. If necessary, an operator can interfere and reduce the autonomy. The system has been tested in an outdoor mission serving as a proof of concept. Some insights from these tests are described in the paper.

Drone networks: Communications, coordination, and sensing

Abstract Small drones are being utilized in monitoring, transport, safety and disaster management, and other domains. Envisioning that drones form autonomous networks incorporated into the air traffic, we describe a high-level architecture for the design of a collaborative aerial system consisting of drones with on-board sensors and embedded processing, coordination, and networking capabilities. We implement a multi-drone system consisting of quadcopters and demonstrate its potential in disaster assistance, search and rescue, and aerial monitoring. Furthermore, we illustrate design challenges and present potential solutions based on the lessons learned so far.

Lens distortion correction for thermal cameras to improve aerial imaging with small-scale UAVs

Lens distortion as a result of the shape and construction of a photographic lens is a common problem in image acquisition. Thermal cameras are no exception to this artifact. So far many methods have been developed to formulate the distortion model and almost all of them exploit the patterns in visible range to calibrate the lenses in RGB cameras. A checkerboard is among the most common and well-defined patterns for RGB camera calibration. Unfortunately, most of those patterns will not be easily visible in images taken by a thermal camera. Furthermore, since the thermal cameras measure the infrared radiation (heat), the conductivity of the heat to the bordering objects in the pattern might mitigate sharp edges, which will make detection of relevant features within the pattern harder and less precise. In this paper we propose an algorithm to construct a calibration pattern visible for the thermal infrared cameras. We show how to extract robust features out of the sensed checkerboard pattern which is used afterward for lens distortion correction. Further, we evaluate our method and compare it to results obtained from well established algorithms for visible-light lens calibration. We also demonstrate how distortion correction improves the image registration between thermal and RGB aerial images taken by small-scale unmanned aerial vehicles (UAVs).

Incremental, orthorectified and loop-independent mosaicking of aerial images taken by micro UAVs

In this paper we survey thoroughly the problem of orthorectified and incremental image mosaicking of a sequence of aerial images taken from low-altitude micro aerial vehicles. Most of existing approaches have been exploiting the global optimization (in presence of a loop in the image sequences) to distribute and/or metadata to mitigate the accumulating stitching error. However, the resulting mosaic can be improved if the errors are diminished by studying their sources. Mostly the UAV aerial image mosaicking is affected by the following three important sources of error: i) a weak homography as a result of using unleveled ground control points (GCPs) for image registration, ii) a poor camera calibration and image rectification, and iii) deficiency of a well-defined projection model (cylindrical, planar, etc) and consequently an inappropriate transformation model. We investigate the influences of using a depth map to find the features from the same plane, geometric distortion correction and combining the appropriate choice of projection and transformation model for the mosaicking. We further quantify the improvement of orthorectification in mosaics by mitigating those errors and demonstrate the improvement on real-world mosaics.

YuMi, Come and Play with Me! A Collaborative Robot for Piecing Together a Tangram Puzzle

Human-robot collaboration is a novel hot topic in robotics research opening a broad range of new opportunities. However, the number of sensible and efficient use cases having been presented and analysed in literature is still very limited. In this technical article, we present and evaluate a collaborative use case for a gaming application in which a two-arm robot has to piece a Tangram puzzle together with a human partner. Algorithms and methods employed for this purpose are presented, performance rates are given for different setups, and remaining problems and future developments are outlined.

Communication and Coordination for Drone Networks

Small drones are being utilized in monitoring, delivery of goods, public safety, and disaster management among other civil applications. Due to their sizes, capabilities, payload limitations, and limited flight time, it is not far-fetched to expect multiple networked and coordinated drones incorporated into the air traffic. In this paper, we describe a high-level architecture for the design of a collaborative aerial system that consists of drones with on-board sensors and embedded processing, sensing, coordination, and communication&networking capabilities. We present a multi-drone system consisting of quadrotors and demonstrate its potential in a disaster assistance scenario. Furthermore, we illustrate the challenges in the design of drone networks and present potential solutions based on the lessons we have learned so far.

Towards Real-time Probabilistic Evaluation of Situation Awareness from Human Gaze in Human-Robot Interaction

Human attention processes play a major role for optimization in human-robot interaction (HRI). This work describes a novel methodology to measure situation awareness in real-time from gaze interaction with scene objects of interest using eye tracking glasses and 3D gaze analysis. A probabilistic framework of uncertainty considers coping with measurement errors in eye and position tracking. Comprehensive experiments on HRI were conducted with tasks including handover in a lab based prototypical manufacturing environment. The methodology is proven to predict a standard measure of situation awareness (SAGAT) in real-time and will open new opportunities for human factors based performance optimization in HRI applications.