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Konstantin Yakovlev - IEEE Xplore Author Profile
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Konstantin Yakovlev

Also published under: K. Yakovlev

Publications
14
Citations
113
Publications by Year
20162025
Co-Authors:
Show All Co-Authors (24)
Image of author Konstantin Yakovlev

Konstantin Yakovlev

Also published under: K. Yakovlev

Affiliation

FRC CSC RAS, Moscow, Russia
AIRI, Moscow, Russia

Publication Topics

Biography

Konstantin Yakovlev received the Ph.D. degree in computer science from the Institute for Systems Analysis of Russian Academy of Sciences, Moscow, Russia, in 2010.,He is currently the Leading Researcher with the Federal Research Center for Computer Science and Control of Russian Academy of Sciences and also affiliated with AIRI, Moscow, where he holds the same position, as well as with HSE University and Moscow Institute of Physics and Technology, Moscow. His research interests include (but are not limited to) heuristic search, single- and multi-agent pathfinding, motion planning, multi-agent systems, and robotics.(Based on document published on 31 August 2023).
Publications
14
Citations
113
Publications by Year
20162025
Co-Authors:
Show All Co-Authors (24)
Author's Published Works

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PRISM-TopoMap: Online Topological Mapping With Place Recognition and Scan Matching

Kirill Muravyev;Alexander Melekhin;Dmitry Yudin;Konstantin Yakovlev

IEEE Robotics and Automation Letters
Year: 2025 | Volume: 10, Issue: 4 | Journal Article |
Mapping is one of the crucial tasks enabling autonomous navigation of a mobile robot. Conventional mapping methods output a dense geometric map representation, e.g. an occupancy grid, which is not trivial to keep consistent for prolonged runs covering large environments. Meanwhile, capturing the topological structure of the workspace enables fast path planning, is typically less prone to odometry ...Show More

PRISM-TopoMap: Online Topological Mapping With Place Recognition and Scan Matching

Kirill Muravyev;Alexander Melekhin;Dmitry Yudin;Konstantin Yakovlev

Year: 2025 | Volume: 10, Issue: 4 | Journal Article |

Optimal and Bounded Suboptimal Any-Angle Multi-agent Pathfinding

Konstantin Yakovlev;Anton Andreychuk;Roni Stern

2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Year: 2024 | Conference Paper |
Multi-agent pathfinding (MAPF) is the problem of finding a set of conflict-free paths for a set of agents. Typically, the agents' moves are limited to a pre-defined graph of possible locations and allowed transitions between them, e.g. a 4-neighborhood grid. We explore how to solve MAPF problems when each agent can move between any pair of possible locations as long as traversing the line segment ...Show More

Optimal and Bounded Suboptimal Any-Angle Multi-agent Pathfinding

Konstantin Yakovlev;Anton Andreychuk;Roni Stern

Year: 2024 | Conference Paper |

When to Switch: Planning and Learning for Partially Observable Multi-Agent Pathfinding

Alexey Skrynnik;Anton Andreychuk;Konstantin Yakovlev;Aleksandr I. Panov

IEEE Transactions on Neural Networks and Learning Systems
Year: 2024 | Volume: 35, Issue: 12 | Journal Article |
Cited by: Papers (6)
Multi-agent pathfinding (MAPF) is a problem that involves finding a set of non-conflicting paths for a set of agents confined to a graph. In this work, we study a MAPF setting, where the environment is only partially observable for each agent, i.e., an agent observes the obstacles and other agents only within a limited field-of-view. Moreover, we assume that the agents do not communicate and do no...Show More

When to Switch: Planning and Learning for Partially Observable Multi-Agent Pathfinding

Alexey Skrynnik;Anton Andreychuk;Konstantin Yakovlev;Aleksandr I. Panov

Year: 2024 | Volume: 35, Issue: 12 | Journal Article |

Evaluation of Topological Mapping Methods in Indoor Environments

Kirill Muravyev;Konstantin Yakovlev

IEEE Access
Year: 2023 | Volume: 11 | Journal Article |
Cited by: Papers (5)
Mapping is one of the key components of mobile robot navigation. Representing a map as a topological structure is suitable for fast path planning and does not require high positioning precision or high computational resources, which is particularly useful in large environments. In recent years, numerous methods of topological graph building have emerged. Most of these methods build a topological g...Show More

Evaluation of Topological Mapping Methods in Indoor Environments

Kirill Muravyev;Konstantin Yakovlev

Year: 2023 | Volume: 11 | Journal Article |

Policy Optimization to Learn Adaptive Motion Primitives in Path Planning With Dynamic Obstacles

Brian Angulo;Aleksandr Panov;Konstantin Yakovlev

IEEE Robotics and Automation Letters
Year: 2023 | Volume: 8, Issue: 2 | Journal Article |
Cited by: Papers (8)
This letter addresses the kinodynamic motion planning for non-holonomic robots in dynamic environments with both static and dynamic obstacles – a challenging problem that lacks a universal solution yet. One of the promising approaches to solve it is decomposing the problem into the smaller sub-problems and combining the local solutions into the global one. The crux of any planning method for non-h...Show More

Policy Optimization to Learn Adaptive Motion Primitives in Path Planning With Dynamic Obstacles

Brian Angulo;Aleksandr Panov;Konstantin Yakovlev

Year: 2023 | Volume: 8, Issue: 2 | Journal Article |

Augmenting GRIPS with Heuristic Sampling for Planning Feasible Trajectories of a Car-Like Robot

Brian Angulo;Ivan Radionov;Konstantin Yakovlev

2021 European Conference on Mobile Robots (ECMR)
Year: 2021 | Conference Paper |
Kinodynamic motion planning for non-holomonic mobile robots is a challenging problem that is lacking a universal solution. One of the computationally efficient ways to solve it is to build a geometric path first and then transform this path into a kinematically feasible one. Gradient-informed Path Smoothing (GRIPS) [1] is a recently introduced method for such transformation. GRIPS iteratively defo...Show More

Augmenting GRIPS with Heuristic Sampling for Planning Feasible Trajectories of a Car-Like Robot

Brian Angulo;Ivan Radionov;Konstantin Yakovlev

Year: 2021 | Conference Paper |

Distributed Multi-Agent Navigation Based on Reciprocal Collision Avoidance and Locally Confined Multi-Agent Path Finding

Stepan Dergachev;Konstantin Yakovlev

2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)
Year: 2021 | Conference Paper |
Cited by: Papers (11)
Avoiding collisions is the core problem in multiagent navigation. In decentralized settings, when agents have limited communication and sensory capabilities, collisions are typically avoided in a reactive fashion, relying on local ob-servations/communications. Prominent collision avoidance techniques, e.g. ORCA, are computationally efficient and scale well to a large number of agents. However, in ...Show More

Distributed Multi-Agent Navigation Based on Reciprocal Collision Avoidance and Locally Confined Multi-Agent Path Finding

Stepan Dergachev;Konstantin Yakovlev

Year: 2021 | Conference Paper |

Hybrid Policy Learning for Multi-Agent Pathfinding

Alexey Skrynnik;Alexandra Yakovleva;Vasilii Davydov;Konstantin Yakovlev;Aleksandr I. Panov

IEEE Access
Year: 2021 | Volume: 9 | Journal Article |
Cited by: Papers (10)
In this work we study the behavior of groups of autonomous vehicles, which are the part of the Internet of Vehicles systems. One of the challenging modes of operation of such systems is the case when the observability of each vehicle is limited and the global/local communication is unstable, e.g. in the crowded parking lots. In such scenarios the vehicles have to rely on the local observations and...Show More

Hybrid Policy Learning for Multi-Agent Pathfinding

Alexey Skrynnik;Alexandra Yakovleva;Vasilii Davydov;Konstantin Yakovlev;Aleksandr I. Panov

Year: 2021 | Volume: 9 | Journal Article |

Empirical Evaluation of Theta*-RRT and GRIPS Algorithms

Zain A. Ali;Brian Angulo;Vladislav Golovin;Konstantin Yakovlev

2021 International Siberian Conference on Control and Communications (SIBCON)
Year: 2021 | Conference Paper |
Motion planning is a fundamental task for wheeled mobile robots. This task becomes challenging when the kinematic constraints of the robot (differential-drive, car-like, etc.) are to be taken into account. In this work we analyze and empirically compare two promising approaches to construct kinematically-feasible trajectories for differential drive robots – Theta*-RRT and GRIPS. Both of these appr...Show More

Empirical Evaluation of Theta*-RRT and GRIPS Algorithms

Zain A. Ali;Brian Angulo;Vladislav Golovin;Konstantin Yakovlev

Year: 2021 | Conference Paper |

Automatic tool for Gazebo world construction: from a grayscale image to a 3D solid model

Bulat Abbyasov;Roman Lavrenov;Aufar Zakiev;Konstantin Yakovlev;Mikhail Svinin;Evgeni Magid

2020 IEEE International Conference on Robotics and Automation (ICRA)
Year: 2020 | Conference Paper |
Cited by: Papers (36)
Robot simulators provide an easy way for evaluation of new concepts and algorithms in a simulated physical environment reducing development time and cost. Therefore it is convenient to have a tool that quickly creates a 3D landscape from an arbitrary 2D image or 2D laser range finder data. This paper presents a new tool that automatically constructs such landscapes for Gazebo simulator. The tool c...Show More

Automatic tool for Gazebo world construction: from a grayscale image to a 3D solid model

Bulat Abbyasov;Roman Lavrenov;Aufar Zakiev;Konstantin Yakovlev;Mikhail Svinin;Evgeni Magid

Year: 2020 | Conference Paper |

LPLian: Angle-Constrained Path Finding in Dynamic Grids

Natalia Soboleva;Konstantin Yakovlev

2019 12th International Conference on Developments in eSystems Engineering (DeSE)
Year: 2019 | Conference Paper |
We consider the problem of planning angleconstrained paths in dynamic 2D environment represented as a grid. We propose an extension of the LIAN algorithm, which is tailored to solve the problem on static grids, by combining it with the prominent Lifelong planning approach. We describe the resultant algorithm, LPLian, and evaluate it empirically in the series of simulated experiments. The results o...Show More

LPLian: Angle-Constrained Path Finding in Dynamic Grids

Natalia Soboleva;Konstantin Yakovlev

Year: 2019 | Conference Paper |

Real-time Vision-based Depth Reconstruction with NVidia Jetson

Andrey Bokovoy;Kirill Muravyev;Konstantin Yakovlev

2019 European Conference on Mobile Robots (ECMR)
Year: 2019 | Conference Paper |
Cited by: Papers (19)
Vision-based depth reconstruction is a challenging problem extensively studied in computer vision but still lacking universal solution. Reconstructing depth from single image is particularly valuable to mobile robotics as it can be embedded to the modern vision-based simultaneous localization and mapping (vSLAM) methods providing them with the metric information needed to construct accurate maps i...Show More

Real-time Vision-based Depth Reconstruction with NVidia Jetson

Andrey Bokovoy;Kirill Muravyev;Konstantin Yakovlev

Year: 2019 | Conference Paper |

Prioritized Multi-agent Path Finding for Differential Drive Robots

Konstantin Yakovlev;Anton Andreychuk;Vitaly Vorobyev

2019 European Conference on Mobile Robots (ECMR)
Year: 2019 | Conference Paper |
Cited by: Papers (13)
Methods for centralized planning of the collision-free trajectories for a fleet of mobile robots typically solve the discretized version of the problem and rely on numerous simplifying assumptions, e.g. moves of uniform duration, cardinal only translations, equal speed and size of the robots etc., thus the resultant plans can not always be directly executed by the real robotic systems. To mitigate...Show More

Prioritized Multi-agent Path Finding for Differential Drive Robots

Konstantin Yakovlev;Anton Andreychuk;Vitaly Vorobyev

Year: 2019 | Conference Paper |

Simultaneous localization and mapping in unknown environment using dynamic matching of images and registration of point clouds

A. Vokhmintsev;M. Timchenko;K. Yakovlev

2016 2nd International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM)
Year: 2016 | Conference Paper |
Cited by: Papers (5)
This paper investigates the usage of the RGB-D cameras for generation of dense 3D maps of environments. The proposed approach to generation of dense three-dimensional maps of indoor environments with a complex infrastructure is based on multisensor dynamic information, spatial alignment of successive frames of data, view-based loop closure detection (the robot returns to the primary point), the al...Show More

Simultaneous localization and mapping in unknown environment using dynamic matching of images and registration of point clouds

A. Vokhmintsev;M. Timchenko;K. Yakovlev

Year: 2016 | Conference Paper |

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