Яковлев Константин Сергеевич
Факультет компьютерных наук
Профессиональные интересы
Должности
- Заведующий кафедрой — Факультет компьютерных наук, Базовая кафедра Федерального исследовательского центра «Информатика и управление» Российской академии наук
- Доцент — Факультет компьютерных наук, Базовая кафедра Федерального исследовательского центра «Информатика и управление» Российской академии наук
Био
- · Начал работать в НИУ ВШЭ в 2014 году.
- · Научно-педагогический стаж: 31 год.
Образование
- 2010 · Кандидат физико-математических наук: Институт программных систем РАН , специальность 05.13.17 «Теоретические основы информатики», тема диссертации: "Исследование методов и разработка алгоритмов автоматического планирования траектории на плоскости"
- 2006 · Магистратура: Российский университет дружбы народов, факультет: Физико-математический, специальность «Прикладная математика и информатика», квалификация «Магистр математики. Преподаватель высшей школы.»
- 2004 · Бакалавриат: Российский университет дружбы народов, факультет: Физико-математический, специальность «Прикладная математика и информатика», квалификация «Бакалавр прикладной математики и информатики»
Опыт работы
- · Федеральный исследовательский центр “Информатика и управление” Российской академии наук (ФИЦ ИУ РАН)
- · Ведущий научный сотрудник, Отдел 71 "Интеллектуальные динамические системы и когнитивные исследования"
- · Национальный исследовательский университет “Высшая школа Экономики” (ВШЭ)
- · Доцент, Факультет компьютерных наук, Базовая кафедра "Интеллектуальные технологии системного анализа и управления" ФИЦ ИУ РАН (по совместительству)
- · Московский физико-технический институт (МФТИ)
- · Доцент, Физтех-школа прикладной математики и информатики, Научно-образовательный центр "Когнитивное моделирование" (по совместительству)
- · Москва
- · Институт искусственного интеллекта AIRI (AIRI)
- · Ведущий научный сотрудник, лаборатория Cognitive AI-agents
Награды и поощрения
- · Благодарность проректора НИУ ВШЭ (февраль 2023)
- · Благодарность Высшей школы экономики (декабрь 2022)
- · Благодарность Факультета компьютерных наук НИУ ВШЭ (сентябрь 2019)
- · Надбавка за публикацию в журнале из Списка А (и приравненном к нему научном издании) (2025–2026, 2024–2025, 2023–2024)
- · Надбавка за публикацию в международном рецензируемом научном издании (2022–2023, 2021–2022, 2020–2022, 2018–2020)
Гранты и проекты
- — · на соискание учёной степени кандидата наук
Конференции (1)
Показать все
- · 2024: XIV Всероссийское совещание по проблемам управления (ВСПУ 2024) (Москва). Доклад: Применение управления с прогнозирующими моделями и стохастической оптимизацией в задаче децентрализованного много-агентного избегания столкновений
Идентификаторы исследователя
- ORCID:
0000-0002-4377-321X - ResearcherID:
J-5636-2015 - SPIN РИНЦ:
8073-5930 - Google Scholar: http://scholar.google.ru/citations?user=Tw0A27kAAAAJ
- Scopus AuthorID:
57125950700
Публикации (72)
GAN Path Finder: Preliminary results
2019 · CHAPTER · en
2D path planning in static environment is a well-known problem and one of the common ways to solve it is to (1) represent the environment as a grid and (2) perform a heuristic search for a path on it. At the same time 2D grid resembles much a digital image, thus an appealing idea comes to being – to treat the problem as an image generation task and to solve it utilizing the recent advances in deep learning. In this work we make an attempt to apply a generative neural network as a path finder and report preliminary results, convincing enough to claim that this direction of research is worth further exploration.
Towards Total Coverage in Autonomous Exploration for UGV in 2.5D Dense Clutter Environment
2019 · CHAPTER · en
Recent developments in 3D reconstruction systems enable to capture an environment in great detail. Several studies have provided algorithms that deal with a path-planning problem of total coverage of observable space in time-efficient manner. However, not much work was done in the area of globally optimal solutions in dense clutter environments. This paper presents a novel solution for autonomous exploration of a cluttered 2.5D environment using an unmanned ground mobile vehicle, where robot locomotion is limited to a 2D plane, while obstacles have a 3D shape. Our exploration algorithm increases coverage of 3D environment mapping comparatively to other currently available algorithms. The algorithm was implemented and tested in randomly generated dense clutter environments in MATLAB.
Grid Path Planning with Deep Reinforcement Learning: Preliminary Results
2018 · ARTICLE · en
Single-shot grid-based path finding is an important problem with the applications in robotics, video games etc. Typically in AI community heuristic search methods (based on A And its variations) are used to solve it. In this work we present the results of preliminary studies on how neural networks can be utilized to path planning on square grids, e.g. how well they can cope with path finding tasks by themselves within the well-known reinforcement problem statement. Conducted experiments show that the agent using neural Q-learning algorithm robustly learns to achieve the goal on small maps and demonstrate promising results on the maps have ben never seen by him before.
Original Loop-closure Detection Algorithm for Monocular vSLAM
2018 · CHAPTER · en
Vision-based simultaneous localization and mapping (vSLAM) is a well-established problem in mobile robotics and monocular vSLAM is one of the most challenging variations of that problem nowadays. In this work we study one of the core post-processing optimization mechanisms in vSLAM, e.g. loop-closure detection. We analyze the existing methods and propose original algorithm for loop-closure detection, which is suitable for dense, semi-dense and feature-based vSLAM methods. We evaluate the algorithm experimentally and show that it contribute to more accurate mapping while speeding up the monocular vSLAM pipeline to the extent the latter can be used in real-time for controlling small multi-rotor vehicle (drone).
Path Finding for the Coalition of Co-operative Agents Acting in the Environment with Destructible Obstacles
2018 · CHAPTER · en
The problem of planning a set of paths for the coalition of robots (agents) with different capabilities is considered in the paper. Some agents can modify the environment by destructing the obstacles thus allowing the other ones to shorten their paths to the goal. As a result the mutual solution of lower cost, e.g. time to completion, may be acquired. We suggest an original procedure to identify the obstacles for further removal that can be embedded into almost any heuristic search planner (we use Theta*) and evaluate it empirically. Results of the evaluation show that time-to-complete the mission can be decreased up to 9–12 % by utilizing the proposed technique.
Sparse 3D Point-cloud Map Upsampling and Noise Removal as a vSLAM Post-processing Step: Experimental Evaluation
2018 · CHAPTER · en
The monocular vision-based simultaneous localization and mapping (vSLAM) is one of the most challenging problem in mobile robotics and computer vision. In this work we study the post-processing techniques applied to sparse 3D point-cloud maps, obtained by feature-based vSLAM algorithms. Map post-processing is split into 2 major steps: (1) noise and outlier removal and (2) upsampling. We evaluate different combinations of known algorithms for outlier removing and upsampling on datasets of real indoor and outdoor environments and identify the most promising combination. We further use it to convert a point-cloud map, obtained by the real UAV performing indoor flight to 3D voxel grid (octo-map) potentially suitable for path planning.
eLIAN: Enhanced Algorithm for Angle-Constrained Path Finding
2018 · CHAPTER · en
Problem of finding 2D paths of special shape, e.g. paths comprised of line segments having the property that the angle between any two consecutive segments does not exceed the predefined threshold, is considered in the paper. This problem is harder to solve than the one when shortest paths of any shape are sought, since the planer’s search space is substantially bigger as multiple search nodes corresponding to the same location need to be considered. One way to reduce the search effort is to fix the length of the path’s segment and to prune the nodes that violate the imposed constraint. This leads to incompleteness and to the sensitivity of the’s performance to chosen parameter value. In this work we introduce a novel technique that reduces this sensitivity by automatically adjusting the length of the path’s segment on-the-fly, e.g. during the search. Embedding this technique into the known grid-based angle-constrained path finding algorithm LIAN, leads to notable increase of the planner’s effectiveness, e.g. success rate, while keeping efficiency, e.g. runtime, overhead at reasonable level. Experimental evaluation shows that LIAN with the suggested enhancements, dubbed eLIAN, solves up to 20% of tasks more compared to the predecessor. Meanwhile, the solution quality of eLIAN is nearly the same as the one of LIAN.
Two Techniques That Enhance the Performance of Multi-robot Prioritized Path Planning
2018 · CHAPTER · en
We introduce and empirically evaluate two techniques aimed at enhancing the performance of multi-robot prioritized path planning.The first technique is the deterministic procedure for re-scheduling(as opposed to well-known approach based on random restarts), the second one is the heuristic procedure that modifies the search-spaceof the individual planner involved in the prioritized path findin
A real-time algorithm for mobile robot mapping based on rotation-invariant descriptors and iterative close point algorithm
2017 · CHAPTER · en
Nowadays many algorithms for mobile robot mapping in indoor environments have been created. In this work we use a Kinect 2.0 camera, a visible range cameras Beward B2720 and an infrared camera Flir Tau 2 for building 3D dense maps of indoor environments. We present the RGB-D Mapping and a new fusion algorithm combining visual features and depth information for matching images, aligning of 3D point clouds, a “loop-closure” detection, pose graph optimization to build global consistent 3D maps. Such 3D maps of environments have various applications in robot navigation, real-time tracking, non-cooperative remote surveillance, face recognition, semantic mapping. The performance and computational complexity of the proposed RGB-D Mapping algorithm in real indoor environments is presented and discussed.
Any-Angle Pathfinding for Multiple Agents Based on SIPP Algorithm
2017 · CHAPTER · en
The problem of finding conflict-free trajectories for multiple agents of identical circular shape, operating in shared 2D workspace, is addressed in the paper and decoupled, e.g., prioritized, approach is used to solve this problem. Agents’ workspace is tessellated into the square grid on which any-angle moves are allowed, e.g. each agent can move into an arbitrary direction as long as this move follows the straight line segment whose endpoints are tied to the distinct grid elements. A novel any-angle planner based on Safe Interval Path Planning (SIPP) algorithm is proposed to find trajectories for an agent moving amidst dynamic obstacles (other agents) on a grid. This algorithm is then used as part of a prioritized multi-agent planner AA-SIPP(m). On the theoretical side, we show that AA-SIPP(m) is complete under well-defined conditions. On the experimental side, in simulation tests with up to 200 agents involved, we show that our planner finds much better solutions in terms of cost (up to 20%) compared to the planners relying on cardinal moves only
Курсы (2)
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Research Seminar "Number Theoretic and Algebraic Methods in Data Analysis" · 3 раза
2025/2026, 2024/2025, 2023/2024 · Магистратура · Анг
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Research Seminar ''Intelligent Systems and Structural Analysis'' · 2 раза
2022/2023, 2021/2022 · Магистратура · Анг