arXiv subjects

Dzmitry Tsetserukou

Publications and source records attributed to Dzmitry Tsetserukou.

At least 145 records · Page 8Linked to original sources

arXiv2023

TeslaMirror: Multistimulus Encounter-Type Haptic Display for Shape and Texture Rendering in VR

This paper proposes a novel concept of a hybrid tactile display with multistimulus feedback, allowing the real-time experience of the position, shape, and texture of the virtual object. The key technology of the TeslaMirror is that we can deliver the sensation of object parameters (pressure, vibration, and electrotactile feedback) without any wearable haptic devices. We developed the full digital twin of the 6 DOF UR robot in the virtual reality (VR) environment, allowing the adaptive surface simulation and control of the hybrid display in real-time. The preliminary user study was conducted to evaluate the ability of TeslaMirror to reproduce shape sensations with the under-actuated end-effector. The results revealed that potentially this approach can be used in the virtual systems for rendering versatile VR shapes with high fidelity haptic experience.

cs.HC↗

arXiv2023

Filasofia: A Framework for Streamlined Development of Real-Time Surgical Simulations

Virtual reality simulation has become a popular approach for training and assessing medical students. It offers diverse scenarios, realistic visuals, and quantitative performance metrics for objective evaluation. However, creating these simulations can be time-consuming and complex, even for experienced users. The SOFA framework is an open-source solution that efficiently simulates finite element (FE) models in real-time. Yet, some users find it challenging to navigate the software due to the numerous components required for a basic simulation and their variability. Additionally, SOFA has limited visual rendering capabilities, leading developers to integrate other software for high-quality visuals. To address these issues, we developed Filasofia, a dedicated framework that simplifies development, provides modern visualization, and allows fine-tuning using SOFA objects. Our experiments demonstrate that Filasofia outperforms conventional SOFA simulations, even with real-time subdivision. Our design approach aims to streamline development while offering flexibility for fine-tuning. Future work will focus on further simplification of the development process for users.

cs.SE↗

arXiv2023

HaptiCharger: Robotic Charging of Electric Vehicles Based on Human Haptic Patterns

The growing demand for electric vehicles requires the development of automated car charging methods. At the moment, the process of charging an electric car is completely manual, and that requires physical effort to accomplish the task, which is not suitable for people with disabilities. Typically, the effort in the automation of the charging task research is focused on detecting the position and orientation of the socket, which resulted in a relatively high accuracy, 5 mm, and 10 degrees. However, this accuracy is not enough to complete the charging process. In this work, we focus on designing a novel methodology for robust robotic plug-in and plug-out based on human haptics to overcome the error in the orientation of the socket. Participants were invited to perform the charging task, and their cognitive capabilities were recognized by measuring the applied forces along with the movements of the charger. Eventually, an algorithm was developed based on the human's best strategies to be applied to a robotic arm.

cs.RO↗

arXiv2023

PolyMerge: A Novel Technique aimed at Dynamic HD Map Updates Leveraging Polylines

Currently, High-Definition (HD) maps are a prerequisite for the stable operation of autonomous vehicles. Such maps contain information about all static road objects for the vehicle to consider during navigation, such as road edges, road lanes, crosswalks, and etc. To generate such an HD map, current approaches need to process pre-recorded environment data obtained from onboard sensors. However, recording such a dataset often requires a lot of time and effort. In addition, every time actual road environments are changed, a new dataset should be recorded to generate a relevant HD map. This paper addresses a novel approach that allows to continuously generate or update the HD map using onboard sensor data. When there is no need to pre-record the dataset, updating the HD map can be run in parallel with the main autonomous vehicle navigation pipeline. The proposed approach utilizes the VectorMapNet framework to generate vector road object instances from a sensor data scan. The PolyMerge technique is aimed to merge new instances into previous ones, mitigating detection errors and, therefore, generating or updating the HD map. The performance of the algorithm was confirmed by comparison with ground truth on the NuScenes dataset. Experimental results showed that the mean error for different levels of environment complexity was comparable to the VectorMapNet single instance error.

cs.RO↗

arXiv2023

TeslaCharge: Smart Robotic Charger Driven by Impedance Control and Human Haptic Patterns

The growing demand for electric vehicles requires the development of automated car charging methods. At the moment, the process of charging an electric car is completely manual, and that requires physical effort to accomplish the task, which is not suitable for people with disabilities. Typically, the effort in the research is focused on detecting the position and orientation of the socket, which resulted in a relatively high accuracy, $\pm 5 \: mm $ and $\pm 10^o$. However, this accuracy is not enough to complete the charging process. In this work, we focus on designing a novel methodology for robust robotic plug-in and plug-out based on human haptics, to overcome the error in the position and orientation of the socket. Participants were invited to perform the charging task, and their cognitive capabilities were recognized by measuring the applied forces along with the movement of the charger. Three controllers were designed based on impedance control to mimic the human patterns of charging an electric car. The recorded data from humans were used to calibrate the parameters of the impedance controllers: inertia $M_d$, damping $D_d$, and stiffness $K_d$. A robotic validation was performed, where the designed controllers were applied to the robot UR10. Using the proposed controllers and the human kinesthetic data, it was possible to successfully automate the operation of charging an electric car.

cs.RO↗

arXiv2023

LocoNeRF: A NeRF-based Approach for Local Structure from Motion for Precise Localization

Visual localization is a critical task in mobile robotics, and researchers are continuously developing new approaches to enhance its efficiency. In this article, we propose a novel approach to improve the accuracy of visual localization using Structure from Motion (SfM) techniques. We highlight the limitations of global SfM, which suffers from high latency, and the challenges of local SfM, which requires large image databases for accurate reconstruction. To address these issues, we propose utilizing Neural Radiance Fields (NeRF), as opposed to image databases, to cut down on the space required for storage. We suggest that sampling reference images around the prior query position can lead to further improvements. We evaluate the accuracy of our proposed method against ground truth obtained using LIDAR and Advanced Lidar Odometry and Mapping in Real-time (A-LOAM), and compare its storage usage against local SfM with COLMAP in the conducted experiments. Our proposed method achieves an accuracy of 0.068 meters compared to the ground truth, which is slightly lower than the most advanced method COLMAP, which has an accuracy of 0.022 meters. However, the size of the database required for COLMAP is 400 megabytes, whereas the size of our NeRF model is only 160 megabytes. Finally, we perform an ablation study to assess the impact of using reference images from the NeRF reconstruction.

cs.CV↗

arXiv2023

DNFOMP: Dynamic Neural Field Optimal Motion Planner for Navigation of Autonomous Robots in Cluttered Environment

Motion planning in dynamically changing environments is one of the most complex challenges in autonomous driving. Safety is a crucial requirement, along with driving comfort and speed limits. While classical sampling-based, lattice-based, and optimization-based planning methods can generate smooth and short paths, they often do not consider the dynamics of the environment. Some techniques do consider it, but they rely on updating the environment on-the-go rather than explicitly accounting for the dynamics, which is not suitable for self-driving. To address this, we propose a novel method based on the Neural Field Optimal Motion Planner (NFOMP), which outperforms state-of-the-art approaches in terms of normalized curvature and the number of cusps. Our approach embeds previously known moving obstacles into the neural field collision model to account for the dynamics of the environment. We also introduce time profiling of the trajectory and non-linear velocity constraints by adding Lagrange multipliers to the trajectory loss function. We applied our method to solve the optimal motion planning problem in an urban environment using the BeamNG.tech driving simulator. An autonomous car drove the generated trajectories in three city scenarios while sharing the road with the obstacle vehicle. Our evaluation shows that the maximum acceleration the passenger can experience instantly is -7.5 m/s^2 and that 89.6% of the driving time is devoted to normal driving with accelerations below 3.5 m/s^2. The driving style is characterized by 46.0% and 31.4% of the driving time being devoted to the light rail transit style and the moderate driving style, respectively.

cs.RO↗

arXiv2023

NeuroSwarm: Multi-Agent Neural 3D Scene Reconstruction and Segmentation with UAV for Optimal Navigation of Quadruped Robot

Quadruped robots have the distinct ability to adapt their body and step height to navigate through cluttered environments. Nonetheless, for these robots to utilize their full potential in real-world scenarios, they require awareness of their environment and obstacle geometry. We propose a novel multi-agent robotic system that incorporates cutting-edge technologies. The proposed solution features a 3D neural reconstruction algorithm that enables navigation of a quadruped robot in both static and semi-static environments. The prior areas of the environment are also segmented according to the quadruped robots' abilities to pass them. Moreover, we have developed an adaptive neural field optimal motion planner (ANFOMP) that considers both collision probability and obstacle height in 2D space.Our new navigation and mapping approach enables quadruped robots to adjust their height and behavior to navigate under arches and push through obstacles with smaller dimensions. The multi-agent mapping operation has proven to be highly accurate, with an obstacle reconstruction precision of 82%. Moreover, the quadruped robot can navigate with 3D obstacle information and the ANFOMP system, resulting in a 33.3% reduction in path length and a 70% reduction in navigation time.

cs.RO↗

arXiv2023

AirTouch: Towards Safe Human-Robot Interaction Using Air Pressure Feedback and IR Mocap System

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.

cs.RO↗

arXiv2023

MorphoLander: Reinforcement Learning Based Landing of a Group of Drones on the Adaptive Morphogenetic UAV

This paper focuses on a novel robotic system MorphoLander representing heterogeneous swarm of drones for exploring rough terrain environments. The morphogenetic leader drone is capable of landing on uneven terrain, traversing it, and maintaining horizontal position to deploy smaller drones for extensive area exploration. After completing their tasks, these drones return and land back on the landing pads of MorphoGear. The reinforcement learning algorithm was developed for a precise landing of drones on the leader robot that either remains static during their mission or relocates to the new position. Several experiments were conducted to evaluate the performance of the developed landing algorithm under both even and uneven terrain conditions. The experiments revealed that the proposed system results in high landing accuracy of 0.5 cm when landing on the leader drone under even terrain conditions and 2.35 cm under uneven terrain conditions. MorphoLander has the potential to significantly enhance the efficiency of the industrial inspections, seismic surveys, and rescue missions in highly cluttered and unstructured environments.

cs.RO↗

arXiv2023

GHACPP: Genetic-based Human-Aware Coverage Path Planning Algorithm for Autonomous Disinfection Robot

Numerous mobile robots with mounted Ultraviolet-C (UV-C) lamps were developed recently, yet they cannot work in the same space as humans without irradiating them by UV-C. This paper proposes a novel modular and scalable Human-Aware Genetic-based Coverage Path Planning algorithm (GHACPP), that aims to solve the problem of disinfecting of unknown environments by UV-C irradiation and preventing human eyes and skin from being harmed. The proposed genetic-based algorithm alternates between the stages of exploring a new area, generating parts of the resulting disinfection trajectory, called mini-trajectories, and updating the current state around the robot. The system performance in effectiveness and human safety is validated and compared with one of the latest state-of-the-art online coverage path planning algorithms called SimExCoverage-STC. The experimental results confirmed both the high level of safety for humans and the efficiency of the developed algorithm in terms of decrease of path length (by 37.1%), number (39.5%) and size (35.2%) of turns, and time (7.6%) to complete the disinfection task, with a small loss in the percentage of area covered (0.6%), in comparison with the state-of-the-art approach.

cs.RO↗

arXiv2023

ArUcoGlide: a Novel Wearable Robot for Position Tracking and Haptic Feedback to Increase Safety During Human-Robot Interaction

The current capabilities of robotic systems make human collaboration necessary to accomplish complex tasks effectively. In this work, we are introducing a framework to ensure safety in a human-robot collaborative environment. The system is composed of a wearable 2-DOF robot, a low-cost and easy-to-install tracking system, and a collision avoidance algorithm based on the Artificial Potential Field (APF). The wearable robot is designed to hold a fiducial marker and maintain its visibility to the tracking system, which, in turn, localizes the user's hand with good accuracy and low latency and provides haptic feedback to the user. The system is designed to enhance the performance of collaborative tasks while ensuring user safety. Three experiments were carried out to evaluate the performance of the proposed system. The first one evaluated the accuracy of the tracking system. The second experiment analyzed human-robot behavior during an imminent collision. The third experiment evaluated the system in a collaborative activity in a shared working environment. The results show that the implementation of the introduced system reduces the operation time by 16% and increases the average distance between the user's hand and the robot by 5 cm.

cs.RO↗

arXiv2023

POA: Passable Obstacles Aware Path-planning Algorithm for Navigation of a Two-wheeled Robot in Highly Cluttered Environments

This paper focuses on Passable Obstacles Aware (POA) planner - a novel navigation method for two-wheeled robots in a highly cluttered environment. The navigation algorithm detects and classifies objects to distinguish two types of obstacles - passable and unpassable. Our algorithm allows two-wheeled robots to find a path through passable obstacles. Such a solution helps the robot working in areas inaccessible to standard path planners and find optimal trajectories in scenarios with a high number of objects in the robot's vicinity. The POA planner can be embedded into other planning algorithms and enables them to build a path through obstacles. Our method decreases path length and the total travel time to the final destination up to 43% and 39%, respectively, comparing to standard path planners such as GVD, A*, and RRT*

cs.RO↗

arXiv2023

MorphoArms: Morphogenetic Teleoperation of Multimanual Robot

Nowadays, there are few unmanned aerial vehicles (UAVs) capable of flying, walking and grasping. A drone with all these functionalities can significantly improve its performance in complex tasks such as monitoring and exploring different types of terrain, and rescue operations. This paper presents MorphoArms, a novel system that consists of a morphogenetic chassis and a hand gesture recognition teleoperation system. The mechanics, electronics, control architecture, and walking behavior of the morphogenetic chassis are described. This robot is capable of walking and grasping objects using four robotic limbs. Robotic limbs with four degrees-of-freedom are used as pedipulators when walking and as manipulators when performing actions in the environment. The robot control system is implemented using teleoperation, where commands are given by hand gestures. A motion capture system is used to track the user's hands and to recognize their gestures. The method of controlling the robot was experimentally tested in a study involving 10 users. The evaluation included three questionnaires (NASA TLX, SUS, and UEQ). The results showed that the proposed system was more user-friendly than 56% of the systems, and it was rated above average in terms of attractiveness, stimulation, and novelty.

cs.RO↗

arXiv2023

LLM-BRAIn: AI-driven Fast Generation of Robot Behaviour Tree based on Large Language Model

This paper presents a novel approach in autonomous robot control, named LLM-BRAIn, that makes possible robot behavior generation, based on operator's commands. LLM-BRAIn is a transformer-based Large Language Model (LLM) fine-tuned from Stanford Alpaca 7B model to generate robot behavior tree (BT) from the text description. We train the LLM-BRAIn on 8,5k instruction-following demonstrations, generated in the style of self-instruct using text-davinchi-003. The developed model accurately builds complex robot behavior while remaining small enough to be run on the robot's onboard microcomputer. The model gives structural and logical correct BTs and can successfully manage instructions that were not presented in training set. The experiment did not reveal any significant subjective differences between BTs generated by LLM-BRAIn and those created by humans (on average, participants were able to correctly distinguish between LLM-BRAIn generated BTs and human-created BTs in only 4.53 out of 10 cases, indicating that their performance was close to random chance). The proposed approach potentially can be applied to mobile robotics, drone operation, robot manipulator systems and Industry 4.0.

cs.RO↗

arXiv2023

Hierarchical Whole-body Control of the cable-Suspended Aerial Manipulator endowed with Winch-based Actuation

During operation, aerial manipulation systems are affected by various disturbances. Among them is a gravitational torque caused by the weight of the robotic arm. Common propeller-based actuation is ineffective against such disturbances because of possible overheating and high power consumption. To overcome this issue, in this paper we propose a winchbased actuation for the crane-stationed cable-suspended aerial manipulator. Three winch-controlled suspension rigging cables produce a desired cable tension distribution to generate a wrench that reduces the effect of gravitational torque. In order to coordinate the robotic arm and the winch-based actuation, a model-based hierarchical whole-body controller is adapted. It resolves two tasks: keeping the robotic arm end-effector at the desired pose and shifting the system center of mass in the location with zero gravitational torque. The performance of the introduced actuation system as well as control strategy is validated through experimental studies.

cs.RO↗

arXiv2023

SwipeBot: DNN-based Autonomous Robot Navigation among Movable Obstacles in Cluttered Environments

In this paper, we propose a novel approach to wheeled robot navigation through an environment with movable obstacles. A robot exploits knowledge about different obstacle classes and selects the minimally invasive action to perform to clear the path. We trained a convolutional neural network (CNN), so the robot can classify an RGB-D image and decide whether to push a blocking object and which force to apply. After known objects are segmented, they are being projected to a cost-map, and a robot calculates an optimal path to the goal. If the blocking objects are allowed to be moved, a robot drives through them while pushing them away. We implemented our algorithm in ROS, and an extensive set of simulations showed that the robot successfully overcomes the blocked regions. Our approach allows a robot to successfully build a path through regions, where it would have stuck with traditional path-planning techniques.

cs.RO↗

arXiv2023

Hierarchical Visual Localization Based on Sparse Feature Pyramid for Adaptive Reduction of Keypoint Map Size

Visual localization is a fundamental task for a wide range of applications in the field of robotics. Yet, it is still a complex problem with no universal solution, and the existing approaches are difficult to scale: most state-of-the-art solutions are unable to provide accurate localization without a significant amount of storage space. We propose a hierarchical, low-memory approach to localization based on keypoints with different descriptor lengths. It becomes possible with the use of the developed unsupervised neural network, which predicts a feature pyramid with different descriptor lengths for images. This structure allows applying coarse-to-fine paradigms for localization based on keypoint map, and varying the accuracy of localization by changing the type of the descriptors used in the pipeline. Our approach achieves comparable results in localization accuracy and a significant reduction in memory consumption (up to 16 times) among state-of-the-art methods.

cs.RO↗