Roman İbrahimov

Roman Ibrahimov, Nikolay Zherdev, Dzmitry Tsetserukou

We propose a novel human-drone interaction paradigm where a user directly interacts with a drone to light-paint predefined patterns or letters through hand gestures. The user wears a glove which is equipped with an IMU sensor to draw letters or patterns in the midair. The developed ML algorithm detects the drawn pattern and the drone light-paints each pattern in midair in the real time. The proposed classification model correctly predicts all of the input gestures. The DroneLight system can be applied in drone shows, advertisements, distant communication through text or pattern, rescue, and etc. To our knowledge, it would be the world's first human-centric robotic system that people can use to send messages based on light-painting over distant locations (drone-based instant messaging). Another unique application of the system would be the development of vision-driven rescue system that reads light-painting by person who is in distress and triggers rescue alarm.

Evgeny Tsykunov, Ruslan Agishev, Roman Ibrahimov et al.

For the human operator, it is often easier and faster to catch a small size quadrotor right in the midair instead of landing it on a surface. However, interaction strategies for such cases have not yet been considered properly, especially when more than one drone has to be landed at the same time. In this paper, we propose a novel interaction strategy to land multiple robots on the human hands using vibrotactile feedback. We developed a wearable tactile display that is activated by the intensity of light emitted from an LED ring on the bottom of the quadcopter. We conducted experiments, where participants were asked to adjust the position of the palm to land one or two vertically-descending drones with different landing speeds, by having only visual feedback, only tactile feedback or visual-tactile feedback. We conducted statistical analysis of the drone landing positions, landing pad and human head trajectories. Two-way ANOVA showed a statistically significant difference between the feedback conditions. Experimental analysis proved that with an increasing number of drones, tactile feedback plays a more important role in accurate hand positioning and operator's convenience. The most precise landing of one and two drones was achieved with the combination of tactile and visual feedback.

Grigoriy A. Yashin, Daria Trinitatova, Ruslan T. Agishev et al.

Drone application for aerial manipulation is tested in such areas as industrial maintenance, supporting the rescuers in emergencies, and e-commerce. Most of such applications require teleoperation. The operator receives visual feedback from the camera installed on a robot arm or drone. As aerial manipulation requires delicate and precise motion of robot arm, the camera data delay, narrow field of view, and blurred images caused by drone dynamics can lead the UAV to crash. The paper focuses on the development of a novel teleoperation system for aerial manipulation using Virtual Reality (VR). The controlled system consists of UAV with a 4-DoF robotic arm and embedded sensors. VR application presents the digital twin of drone and remote environment to the user through a head-mounted display (HMD). The operator controls the position of the robotic arm and gripper with VR trackers worn on the arm and tracking glove with vibrotactile feedback. Control data is translated directly from VR to the real robot in real-time. The experimental results showed a stable and robust teleoperation mediated by the VR scene. The proposed system can considerably improve the quality of aerial manipulations.

Roman Ibrahimov, Evgeny Tsykunov, Vladimir Shirokun et al.

We report on the teleoperation system DronePick which provides remote object picking and delivery by a human-controlled quadcopter. The main novelty of the proposed system is that the human user continuously gets the visual and haptic feedback for accurate teleoperation. DronePick consists of a quadcopter equipped with a magnetic grabber, a tactile glove with finger motion tracking sensor, hand tracking system, and the Virtual Reality (VR) application. The human operator teleoperates the quadcopter by changing the position of the hand. The proposed vibrotactile patterns representing the location of the remote object relative to the quadcopter are delivered to the glove. It helps the operator to determine when the quadcopter is right above the object. When the "pick" command is sent by clasping the hand in the glove, the quadcopter decreases its altitude and the magnetic grabber attaches the target object. The whole scenario is in parallel simulated in VR. The air flow from the quadcopter and the relative positions of VR objects help the operator to determine the exact position of the delivered object to be picked. The experiments showed that the vibrotactile patterns were recognized by the users at the high recognition rates: the average 99% recognition rate and the average 2.36s recognition time. The real-life implementation of DronePick featuring object picking and delivering to the human was developed and tested.