Stepan Perminov

Ziang Guo, Stepan Perminov, Mikhail Konenkov et al.

Many established vision perception systems for autonomous driving scenarios ignore the influence of light conditions, one of the key elements for driving safety. To address this problem, we present HawkDrive, a novel perception system with hardware and software solutions. Hardware that utilizes stereo vision perception, which has been demonstrated to be a more reliable way of estimating depth information than monocular vision, is partnered with the edge computing device Nvidia Jetson Xavier AGX. Our software for low light enhancement, depth estimation, and semantic segmentation tasks, is a transformer-based neural network. Our software stack, which enables fast inference and noise reduction, is packaged into system modules in Robot Operating System 2 (ROS2). Our experimental results have shown that the proposed end-to-end system is effective in improving the depth estimation and semantic segmentation performance. Our dataset and codes will be released at https://github.com/ZionGo6/HawkDrive.

Konstantin Gubernatorov, Artem Voronov, Roman Voronov et al.

We address natural language pick-and-place in unseen, unpredictable indoor environments with AnywhereVLA, a modular framework for mobile manipulation. A user text prompt serves as an entry point and is parsed into a structured task graph that conditions classical SLAM with LiDAR and cameras, metric semantic mapping, and a task-aware frontier exploration policy. An approach planner then selects visibility and reachability aware pre grasp base poses. For interaction, a compact SmolVLA manipulation head is fine tuned on platform pick and place trajectories for the SO-101 by TheRobotStudio, grounding local visual context and sub-goals into grasp and place proposals. The full system runs fully onboard on consumer-level hardware, with Jetson Orin NX for perception and VLA and an Intel NUC for SLAM, exploration, and control, sustaining real-time operation. We evaluated AnywhereVLA in a multi-room lab under static scenes and normal human motion. In this setting, the system achieves a $46\%$ overall task success rate while maintaining throughput on embedded compute. By combining a classical stack with a fine-tuned VLA manipulation, the system inherits the reliability of geometry-based navigation with the agility and task generalization of language-conditioned manipulation.

Nikita Mikhailovskiy, Alexander Sedunin, Stepan Perminov et al.

The paper focuses on the development of an autonomous disinfection robot UltraBot to reduce COVID-19 transmission along with other harmful bacteria and viruses. The motivation behind the research is to develop such a robot that is capable of performing disinfection tasks without the use of harmful sprays and chemicals that can leave residues and require airing the room afterward for a long time. UltraBot technology has the potential to offer the most optimal autonomous disinfection performance along with taking care of people, keeping them from getting under the UV-C radiation. The paper highlights UltraBot's mechanical and electrical design as well as disinfection performance. The conducted experiments demonstrate the effectiveness of robot disinfection ability and actual disinfection area per each side with UV-C lamp array. The disinfection effectiveness results show actual performance for the multi-pass technique that provides 1-log reduction with combined direct UV-C exposure and ozone-based air purification after two robot passes at a speed of 0.14 m/s. This technique has the same performance as ten minutes static disinfection. Finally, we have calculated the non-trivial form of the robot disinfection zone by two consecutive experiment to produce optimal path planning and to provide full disinfection in selected areas.

Stepan Perminov, Nikita Mikhailovskiy, Alexander Sedunin et al.

The paper focuses on the development of the autonomous robot UltraBot to reduce COVID-19 transmission and other harmful bacteria and viruses. The motivation behind the research is to develop such a robot that is capable of performing disinfection tasks without the use of harmful sprays and chemicals that can leave residues, require airing the room afterward for a long time, and can cause the corrosion of the metal structures. UltraBot technology has the potential to offer the most optimal autonomous disinfection performance along with taking care of people, keeping them from getting under UV-C radiation. The paper highlights UltraBot's mechanical and electrical structures as well as low-level and high-level control systems. The conducted experiments demonstrate the effectiveness of the robot localization module and optimal trajectories for UV-C disinfection. The results of UV-C disinfection performance revealed a decrease of the total bacterial count (TBC) by 94% on the distance of 2.8 meters from the robot after 10 minutes of UV-C irradiation.

Ekaterina Karmanova, Valerii Serpiva, Stepan Perminov et al.

Reinforcement learning (RL) methods have been actively applied in the field of robotics, allowing the system itself to find a solution for a task otherwise requiring a complex decision-making algorithm. In this paper, we present a novel RL-based Tic-tac-toe scenario, i.e. SwarmPlay, where each playing component is presented by an individual drone that has its own mobility and swarm intelligence to win against a human player. Thus, the combination of challenging swarm strategy and human-drone collaboration aims to make the games with machines tangible and interactive. Although some research on AI for board games already exists, e.g., chess, the SwarmPlay technology has the potential to offer much more engagement and interaction with the user as it proposes a multi-agent swarm instead of a single interactive robot. We explore user's evaluation of RL-based swarm behavior in comparison with the game theory-based behavior. The preliminary user study revealed that participants were highly engaged in the game with drones (70% put a maximum score on the Likert scale) and found it less artificial compared to the regular computer-based systems (80%). The affection of the user's game perception from its outcome was analyzed and put under discussion. User study revealed that SwarmPlay has the potential to be implemented in a wider range of games, significantly improving human-drone interactivity.

Ekaterina Karmanova, Valerii Serpiva, Stepan Perminov et al.

We present a new paradigm of games, i.e. SwarmPlay, where each playing component is presented by an individual drone that has its own mobility and swarm intelligence to win against a human player. The motivation behind the research is to make the games with machines tangible and interactive. Although some research on the robotic players for board games already exists, e.g., chess, the SwarmPlay technology has the potential to offer much more engagement and interaction with a human as it proposes a multi-agent swarm instead of a single interactive robot. The proposed system consists of a robotic swarm, a workstation, a computer vision (CV), and Game Theory-based algorithms. A novel game algorithm was developed to provide a natural game experience to the user. The preliminary user study revealed that participants were highly engaged in the game with drones (69% put a maximum score on the Likert scale) and found it less artificial compared to the regular computer-based systems (77% put maximum score). The affection of the user's game perception from its outcome was analyzed and put under discussion. User study revealed that SwarmPlay has the potential to be implemented in a wider range of games, significantly improving human-drone interactivity.

Valerii Serpiva, Ekaterina Karmanova, Aleksey Fedoseev et al.

We propose a novel human-swarm interaction system, allowing the user to directly control a swarm of drones in a complex environment through trajectory drawing with a hand gesture interface based on the DNN-based gesture recognition. The developed CV-based system allows the user to control the swarm behavior without additional devices through human gestures and motions in real-time, providing convenient tools to change the swarm's shape and formation. The two types of interaction were proposed and implemented to adjust the swarm hierarchy: trajectory drawing and free-form trajectory generation control. The experimental results revealed a high accuracy of the gesture recognition system (99.75%), allowing the user to achieve relatively high precision of the trajectory drawing (mean error of 5.6 cm in comparison to 3.1 cm by mouse drawing) over the three evaluated trajectory patterns. The proposed system can be potentially applied in complex environment exploration, spray painting using drones, and interactive drone shows, allowing users to create their own art objects by drone swarms.

Valerii Serpiva, Ekaterina Karmanova, Aleksey Fedoseev et al.

Teleoperation tasks with multi-agent systems have a high potential in supporting human-swarm collaborative teams in exploration and rescue operations. However, it requires an intuitive and adaptive control approach to ensure swarm stability in a cluttered and dynamically shifting environment. We propose a novel human-swarm interaction system, allowing the user to control swarm position and formation by either direct hand motion or by trajectory drawing with a hand gesture interface based on the DNN gesture recognition. The key technology of the SwarmPaint is the user's ability to perform various tasks with the swarm without additional devices by switching between interaction modes. Two types of interaction were proposed and developed to adjust a swarm behavior: free-form trajectory generation control and shaped formation control. Two preliminary user studies were conducted to explore user's performance and subjective experience from human-swarm interaction through the developed control modes. The experimental results revealed a sufficient accuracy in the trajectory tracing task (mean error of 5.6 cm by gesture draw and 3.1 cm by mouse draw with the pattern of dimension 1 m by 1 m) over three evaluated trajectory patterns and up to 7.3 cm accuracy in targeting task with two target patterns of 1 m achieved by SwarmPaint interface. Moreover, the participants evaluated the trajectory drawing interface as more intuitive (12.9 %) and requiring less effort to utilize (22.7%) than direct shape and position control by gestures, although its physical workload and failure in performance were presumed as more significant (by 9.1% and 16.3%, respectively).

Evgeny Tsykunov, Valery Ilin, Stepan Perminov et al.

We propose to couple two types of Intel RealSense sensors (tracking T265 and depth D435i) in order to obtain localization and 3D occupancy map of the indoor environment. We implemented a python-based observer pattern with multi-threaded approach for camera data synchronization. We compared different point cloud (PC) alignment methods (using transformations obtained from tracking camera and from ICP family methods). Tracking camera and PC alignment allow us to generate a set of transformations between frames. Based on these transformations we obtained different trajectories and provided their analysis. Finally, having poses for all frames, we combined depth data. Firstly we obtained a joint PC representing the whole scene. Then we used Octomap representation to build a map.