Vyacheslav Petrenko

About

Vyacheslav Petrenko is a leading researcher in multi-robotic systems, swarm intelligence, and anthropomorphic robotics, with a focus on decentralized control and energy-efficient automation. His major contributions include developing a consensus achievement method for robotic swarms to identify environmental features (12 citations), which enhances collective decision-making without central coordination. Petrenko also pioneered path planning algorithms for anthropomorphic manipulators, addressing high energy consumption through quasi-optimal trajectory methods, and introduced an iterative labor division approach for multi-robot systems to optimize task allocation. His work extends to modular robot reconfiguration using greedy algorithms and deep reinforcement learning for agricultural automation, such as automatic harvesting in greenhouses. With over 40 citations across his top papers, Petrenko’s research demonstrates significant impact in advancing swarm control strategies, kinematic reconfiguration, and obstacle-aware motion planning. Notably, his 2020 study on robotic swarm consensus and his 2019 analysis of path planning effectiveness for manipulators are foundational, offering practical solutions for real-time, decentralized operations in dynamic environments. Petrenko’s achievements underscore his role in bridging theoretical multi-agent systems with applied robotics, making his work essential for students and researchers exploring autonomous, collaborative robotic systems.

Research Focus

Key Achievements

Top Papers

1. 1
   Consensus achievement method for a robotic swarm about the most frequently feature of an environment

   12 citations · 2020
2. 2
   Analysis of the Effectiveness Path Planning Methods and Algorithm for the Anthropomorphic Robot Manipulator

   8 citations · 2019
3. 3
   Path Planning Method in the Formation of the Configuration of a Multifunctional Modular Robot Using a Swarm Control Strategy

   6 citations · 2019
4. 4
   Iterative Method of Labor Division for Multi-Robotic Systems

   4 citations · 2022
5. 5
   A Robotic Complex Control Method Based on Deep Reinforcement Learning of Recurrent Neural Networks for Automatic Harvesting of Greenhouse Crops

   4 citations · 2020
6. 6
   The Method of the Kinematic Structure Reconfiguration of a Multifunctional Modular Robot Based on the Greedy Algorithm

   2 citations · 2019
7. 7
   Kinematic analysis anthropomorphic gripper with group drive

   2 citations · 2020
8. 8
   Method of Controlling the Movement of an Anthropomorphic Manipulator in the Working Area With Dynamic Obstacle

   2 citations · 2020
9. 9
   METHOD FOR PLANNING THE OPTIMAL TRAJECTORY OF A THREE-LINK MANIPULATOR IN TRIDIMENSIONAL SPACE WITH AN OBSTACLE

   2 citations · 2018
10. 10
    Energy-Efficient Path Planning: Designed Software Implementation

    2 citations · 2019

Key Collaborators

Contact & Links