We present an intuitive human-drone interaction system that utilizes a gesture-based motion controller to enhance the drone operation experience in real and simulated environments. The handheld motion controller enables natural control of the drone through the movements of the operator's hand, thumb, and index finger: the trigger press manages the throttle, the tilt of the hand adjusts pitch and roll, and the thumbstick controls yaw rotation. Communication with drones is facilitated via the ExpressLRS radio protocol, ensuring robust connectivity across various frequencies. The user evaluation of the flight experience with the designed drone controller using the UEQ-S survey showed high scores for both Pragmatic (mean=2.2, SD = 0.8) and Hedonic (mean=2.3, SD = 0.9) Qualities. This versatile control interface supports applications such as research, drone racing, and training programs in real and simulated environments, thereby contributing to advances in the field of human-drone interaction.

Understanding the neural correlates of sensory imagery is crucial for advancing cognitive neuroscience and developing novel Brain-Computer Interface (BCI) paradigms. This study investigated the influence of imagined temperature sensations (ITS) on neural activity within the sensorimotor cortex. The experimental study involved the evaluation of neural activity using electroencephalography (EEG) during both real thermal stimulation (TS: 40 °C Hot, 20 °C Cold) applied to the participants’ hand, and the mental temperature imagination (ITS) of the corresponding hot and cold sensations. The analysis focused on quantifying the event-related desynchronization (ERD) of the sensorimotor μ-rhythm (8-13 Hz). The experimental results revealed a characteristic μ-ERD localized over central scalp regions (e.g., C3) during both TS and ITS conditions. Although the magnitude of μ-ERD during ITS was slightly lower than during TS, this difference was not statistically significant (p > .05). However, ERD during both ITS and TS was statistically significantly different from the resting baseline (p < .001). These findings demonstrate that imagining temperature sensations engages sensorimotor cortical mechanisms in a manner comparable to actual thermal perception. This insight expands our understanding of the neurophysiological basis of sensory imagery and suggests the potential utility of ITS for non-motor BCI control and neurorehabilitation technologies.

This paper introduces HapticVLM, a novel multimodal system that integrates vision-language reasoning and deep convolutional networks to enable real-time haptic feedback. HapticVLM leverages a ConvNeXt-based material recognition module to generate robust visual embeddings for accurate identification of object materials. A state-of-the-art Vision-Language Model (Qwen2-VL-2B-Instruct) infers ambient temperature from environmental cues. The system synthesizes tactile sensations by delivering vibrotactile feedback through speakers and thermal cues with a Peltier module, thereby bridging the gap between visual perception and tactile experience. Experimental evaluations demonstrate an average recognition accuracy of 84.7% across five distinct auditory-tactile patterns and a temperature estimation accuracy of 86.7% using an 8 °C margin error across 15 scenarios. Although promising, the current study is limited by the use of a small set of patterns and participants. Future work will focus on expanding the range of tactile patterns and increasing user studies to further refine and validate the system’s performance. Overall, HapticVLM presents a significant step toward intelligent, context-aware, multimodal haptic interaction for Virtual Reality (VR) and assistive technologies.

The growing use of robots in urban environments has raised concerns about potential safety hazards, especially in public spaces where humans and robots may interact. In this paper, we present a system for safe human-robot interaction that combines an infrared (IR) camera with a wearable marker and airflow potential field. IR cameras enable real-time detection and tracking of humans in challenging environments, while controlled airflow creates a physical barrier that guides humans away from dangerous proximity to robots without the need for wearable devices. A preliminary experiment was conducted to measure the accuracy of the perception of safety barriers rendered by controlled air pressure. In a second experiment, we evaluated our approach in an imitation scenario of an interaction between an inattentive person and an autonomous robotic system. Experimental results show that the proposed system significantly improves a participant's ability to maintain a safe distance from the operating robot compared to trials without the system.

MobileCharger is a novel mobile charging robot with an Inverted Delta actuator for safe and robust energy transfer between two mobile robots. The RGB-D camera-based computer vision system allows to detect the electrodes on the target mobile robot using a convolutional neural network (CNN). The embedded high-fidelity tactile sensors are applied to estimate the misalignment between the electrodes on the charger mechanism and the electrodes on the main robot using CNN based on pressure data on the contact surfaces. Thus, the developed vision-tactile perception system allows precise positioning of the end effector of the actuator and ensures a reliable connection between the electrodes of the two robots. The experimental results showed high average precision (84.2%) for electrode detection using CNN. The percentage of successful trials of the CNN-based electrode search algorithm reached 83% and the average execution time accounted for 60 s. MobileCharger could introduce a new level of charging systems and increase the prevalence of autonomous mobile robots.

WareVR is a novel human-robot interface based on a virtual reality (VR) application to interact with a heterogeneous robotic system for automated inventory management. We have created an interface to supervise an autonomous robot remotely from a secluded workstation in a warehouse that could benefit during the current pandemic COVID-19 since the stocktaking is a necessary and regular process in warehouses, which involves a group of people. The proposed interface allows regular warehouse workers without experience in robotics to control the heterogeneous robotic system consisting of an unmanned ground vehicle (UGV) and unmanned aerial vehicle (UAV). WareVR provides visualization of the robotic system in a digital twin of the warehouse, which is accompanied by a real-time video stream from the real environment through an onboard UAV camera. Using the WareVR interface, the operator can conduct different levels of stocktaking, monitor the inventory process remotely, and teleoperate the drone for a more detailed inspection. Besides, the developed interface includes remote control of the UAV for intuitive and straightforward human interaction with the autonomous robot for stocktaking. The effectiveness of the VR-based interface was evaluated through the user study in a “visual inspection” scenario.