Bojan Šekoranja

Luka Šiktar, Branimir Ćaran, Bojan Šekoranja et al.

Search and rescue (SAR) operations require rapid responses to save lives or property. Unmanned Aerial Vehicles (UAVs) equipped with vision-based systems support these missions through prior terrain investigation or real-time assistance during the mission itself. Vision-based UAV frameworks aid human search tasks by detecting and recognizing specific individuals, then tracking and following them while maintaining a safe distance. A key safety requirement for UAV following is the accurate estimation of the distance between camera and target object under real-world conditions, achieved by fusing multiple image modalities. UAVs with deep learning-based vision systems offer a new approach to the planning and execution of SAR operations. As part of the system for automatic people detection and face recognition using deep learning, in this paper we present the fusion of depth camera measurements and monocular camera-to-body distance estimation for robust tracking and following. Deep learning-based filtering of depth camera data and estimation of camera-to-body distance from a monocular camera are achieved with YOLO-pose, enabling real-time fusion of depth information using the Extended Kalman Filter (EKF) algorithm. The proposed subsystem, designed for use in drones, estimates and measures the distance between the depth camera and the human body keypoints, to maintain the safe distance between the drone and the human target. Our system provides an accurate estimated distance, which has been validated against motion capture ground truth data. The system has been tested in real time indoors, where it reduces the average errors, root mean square error (RMSE) and standard deviations of distance estimation up to 15,3\% in three tested scenarios.

Bojan Jerbić, Marko Švaco, Filip Šuligoj et al.

Design usually relies on human ingenuity, but the past decade has seen the field's toolbox expanding to Artificial Intelligence (AI) and its adjacent methods, making room for hybrid, algorithmic creations. This article aims to substantiate the concept of interspecies collaboration - that of natural and artificial intelligence - in the active co-creation of a visual identity, describing a case study of the Regional Center of Excellence for Robotic Technology (CRTA) which opened on 750 m2 in June 2021 within the University of Zagreb. The visual identity of the Center comprises three separately devised elements, each representative of the human-AI relationship and embedded in the institution's logo. Firstly, the letter "C" (from the CRTA acronym) was created using a Gaussian Mixture Model (GMM) applied to (x, y) coordinates that the neurosurgical robot RONNA, CRTA's flagship innovation, generated when hand-guided by a human operator. The second shape of the letter "C" was created by using the same (x, y) coordinates as inputs fed to a neural network whose goal was to output letters in a novel, AI-generated typography. A basic feedforward back-propagating neural network with two hidden layers was chosen for the task. The final and third design element was a trajectory the robot RONNA makes when performing a brain biopsy. As CRTA embodies a state-of-the-art venue for robotics research, the 'interspecies' approach was used to accentuate the importance of human-robot collaboration which is at the core of the newly opened Center, illustrating the potential of reciprocal and amicable relationship that humans could have with technology.