Aleksei Gonnochenko’s scientific contributions

Simultaneous localization and mapping (SLAM) is one of the fundamental areas of research in robotics and environment reconstruction. State-of-the-art solutions have advanced significantly in terms of mapping quality, localization accuracy and robustness. It becomes possible due to modern stable solvers in the back-end, efficient outlier rejection techniques and diversified front-end: unique features, topologically segmented landmarks, and high-quality sensors. Among the variety of open-source solutions, several promising approaches provide results which are difficult to be reproduced on standard datasets, especially if there is no description for dataset adaptation. The goal of the article is to figure out, which techniques of robots’ localization are the most promising for further use in related disciplines for engineers and researchers. The main contribution is a comparative analysis of state-of-the-art open-source Visual SLAM methods in terms of localization precision for versatile environments. The algorithms are assessed based on accuracy, computational performance, robustness and fault tolerance. Additionally, the survey and comparison of the datasets used for methods evaluation are provided as well as practical recommendations of usage scenarios for further research.

SLAM is one of the most fundamental areas of research in robotics and computer vision. State of the art solutions has advanced significantly in terms of accuracy and stability. Unfortunately, not all the approaches are available as open-source solutions and free to use. The results of some of them are difficult to reproduce, and there is a lack of comparison on common datasets. In our work, we make a comparative analysis of state of the art open-source methods. We assess the algorithms based on accuracy, computational performance, robustness, and fault tolerance. Moreover, we present a comparison of datasets as well as an analysis of algorithms from a practical point of view. The findings of the work raise several crucial questions for SLAM researchers.

Given the laborious difficulty of moving heavy bags of physical currency in the cash center of the bank, there is a large demand for training and deploying safe autonomous systems capable of conducting such tasks in a collaborative workspace. However, the deformable properties of the bag along with the large quantity of rigid-body coins contained within it, significantly increases the challenges of bag detection, grasping and manipulation by a robotic gripper and arm. In this paper, we apply deep reinforcement learning and machine learning techniques to the task of controlling a collaborative robot to automate the unloading of coin bags from a trolley. To accomplish the task-specific process of gripping flexible materials like coin bags where the center of the mass changes during manipulation, a special gripper was implemented in simulation and designed in physical hardware. Leveraging a depth camera and object detection using deep learning, a bag detection and pose estimation has been done for choosing the optimal point of grasping. An intelligent approach based on deep reinforcement learning has been introduced to propose the best configuration of the robot end-effector to maximize successful grasping. A boosted motion planning is utilized to increase the speed of motion planning during robot operation. Real-world trials with the proposed pipeline have demonstrated success rates over 96\% in a real-world setting.

Traditionally, robotics mainly relates to activities which are described as DDD – Dull, Dirty, Dangerous. COVID-19 pandemic has turned to DDD many of common human activities – especially in medical care sphere. Significantly more in demand and more dangerous for the personnel became such mass jobs as disinfection, quarantine control, infected patients' treatment, and number of other crucial services. This transformation highlighted practical abilities of various kinds of service robots. An unexpected application was found for wheel- and caterpillar-mounted platforms, collaborative robots, unmanned autonomous ground and aerial delivery vehicles, etc. in helping healthcare workers and emergency services, while over a half of humankind found itself under contamination threat and quarantined. In the article, the authors inspected and summarized various organizations’ experience and practices undertaken to apply existing robotics solutions to prevent COVID-19 spreading. Considering complexity of such tasks and break-up of global supply chains, the use of mature (or at least tested) solutions which can be rapidly transformed to new applications is deemed the most feasible approach. The authors suppose, as of now, there are no reasonable prerequisites for identifying a leader of response to the pandemic among robotics companies, however, those players who is able to define and enter the advantageous emerging market segment, acquire good chances of business success. Традиционно применение робототехники связывается с видами деятельности, которые характеризуются как DDD – Dull, Dirty, Dangerous («скучные, грязные, опасные»). Пандемия COVID-19 сделала таковыми многие виды деятельности человека, в особенности – в сфере здравоохранения. Значительно более востребованными и опасными для персонала стали такие массовые виды работ, как дезинфекция, контроль режима карантина, забота об инфицированных пациентах и многое другое. Эта трансформация сделала чрезвычайно актуальным применение различных видов сервисной робототехники. Колёсные и гусеничные платформы, коллаборативные роботы, автономные наземные и воздушные доставщики нашли неожиданное применение для помощи медикам и спасательным службам в условиях чрезвычайной ситуации и карантина, в котором оказалось более половины населения Земли. В данной статье изучен и обобщён опыт различных организаций, которые применили уже существующие технологии робототехники для решения актуальной задачи предотвращения распространения вируса. С учётом сложности данной задачи, а также нарушения логистических цепочек по всему миру, наиболее обоснованным представляется подход, в соответствии с которым используются уже готовые, проверенные технические решения, которые быстро перепрофилируются под решение новых задач. Авторы статьи полагают, что пока нет никаких предпосылок для выявления лидеров в области применения робототехнических технологий для борьбы с распространением вируса, однако, те компании, которые сумеют найти чёткую нишу применения и быстро адаптировать свои технологии, имеют хорошие шансы достичь коммерческого успеха.