Ruslan Agishev

Ruslan Agishev, Karel Zimmermann

We propose end-to-end differentiable model that predicts robot trajectories on rough offroad terrain from camera images and/or lidar point clouds. The model integrates a learnable component that predicts robot-terrain interaction forces with a neural-symbolic layer that enforces the laws of classical mechanics and consequently improves generalization on out-of-distribution data. The neural-symbolic layer includes a differentiable physics engine that computes the robot's trajectory by querying these forces at the points of contact with the terrain. As the proposed architecture comprises substantial geometrical and physics priors, the resulting model can also be seen as a learnable physics engine conditioned on real sensor data that delivers $10^4$ trajectories per second. We argue and empirically demonstrate that this architecture reduces the sim-to-real gap and mitigates out-of-distribution sensitivity. The differentiability, in conjunction with the rapid simulation speed, makes the model well-suited for various applications including model predictive control, trajectory shooting, supervised and reinforcement learning, or SLAM.

Evgeny Tsykunov, Aleksey Fedoseev, Ekaterina Dorzhieva et al.

DroneStick is a novel hands-free method for smooth interaction between a human and a robotic system via one of its agents, without training and any additional handheld or wearable device or infrastructure. A flying joystick (DroneStick), being a part of a multi-robot system, is composed of a flying drone and coiled wire with a vibration motor. By pulling on the coiled wire, the operator commands certain motions of the follower robotic system. The DroneStick system does not require the user to carry any equipment before or after performing the required interaction. DroneStick provides useful feedback to the operator in the form of force transferred through the wire, translation/rotation of the flying joystick, and motor vibrations at the fingertips. Feedback allows users to interact with different forms of robotic systems intuitively. A potential application can enhance an automated `last mile' delivery when a recipient needs to guide a delivery drone/robot gently to a spot where a parcel has to be dropped.

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.