Optimization of the trajectory of movement of a parallel robot based on a modified AСO algorithm

This paper is devoted to the study of varieties of the Q-Learning algorithm – deep Q-networks and dueling Q-networks. These algorithms belong to the group of reinforcement learning algorithms. Neural network architectures are selected. The process of modeling the robot’s operation in the problem of cargo delivery from a random point A to the green zone is described. The method of obtaining information about the environment by the robot using the Raycast method is described. A block diagram for controlling the robot movement has been developed, which consists of a block of positioning and state sensors, a neural network module, and a block for constructing a trajectory. The last two blocks together form a system for automatically controlling the agent’s movement in the external environment. Modeling was performed in the Unity development environment. To work with ml agents, the special Unity ML-Agents tool is used. This tool is implemented using a modern DRL servo motor, which is based on the model of the optimization algorithm proximal policy optimization (PPO). A constructive simplification of the agent and environment to facilitate the reproduction of training scenes is implemented. An algorithm for training a robot in a random environment is presented. The optimal parameters of the algorithms under consideration are selected. Suggestions are made to improve the performance of algorithms for this problem.

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Development of an algorithm for managing a multi-robot system for cargo transportation based on reinforcement learning in a virtual environment

Rybak

Behera

Averbukh

et al. 2020

IOP Conf. Ser.: Mater. Sci. Eng.

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Visual Servoing and Kalman Filter Applied to Parallel Manipulator 3-RRR

Daraviña,

Valencia,

Holguin

et al. 2024

Electronics

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This study introduces a novel methodology integrating computer vision, visual servo control, and the Kalman Filter to precisely estimate object locations for a 3-RRR planar type parallel manipulator. Through kinematic analysis and the development of a vision system using color indicators, the research enhances the ability of the manipulator to track object trajectories, especially in cases of occlusion. Employing Eye-to-Hand visual servo control, the research further refines the visual orientation of the sensor for optimal end effector and object identification. The incorporation of the Kalman Filter as a robust estimator for occluded objects underscores the predictive accuracy of the system. Results demonstrate the effectiveness of the methodology in trajectory generation and object tracking, with potential implications for improving robotic manipulators in dynamic environments. This comprehensive approach not only advances the fields of kinematic control and visual servoing but also opens new avenues for future research in complex spatial manipulations.

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Analysis and Classification of Rehabilitation Robotic Systems Based on Parallel Manipulators

Posypkin¹,

Halapyan²,

Maslova

et al. 2020

Bulletin of Belgorod State Technological University Named After. V. G. Shukhov

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One of the most actual and complex problems of medicine and neurology is the rehabilitation of patients. Robotic systems currently occupy an important place in the comprehensive rehabilitation of neurological patients with severe motor impairment of various etiologies, as well as the most socially significant and common neurological diseases. The article discusses various architectures of robotic systems for the rehabilitation of the lower limbs. The article also analyzes the work of foreign scientists who deal with these issues. As a result, a classification of rehabilitation systems has been compiled and reviewed. The structure of the rehabilitation system based on a passive orthosis and active parallel 3-PRRR robot is proposed. A numerical algorithm has been developed to determine the workspace of a 3-PRRR robot, taking into account design limitations. The algorithm is based on the concept of non-uniform coverings using interval analysis methods and is implemented in the C++ programming language. The results of computational experiments are presented. The simulation results are visualized by converting a set of three-dimensional parallelepipeds into an STL file. However, many areas remain open to theoretical research. A further area of research is the optimization of the geometric parameters of the system for carrying out rehabilitation procedures.

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Optimization of the trajectory of movement of a parallel robot based on a modified AСO algorithm

Cited by 3 publications

References 4 publications

Development of an algorithm for managing a multi-robot system for cargo transportation based on reinforcement learning in a virtual environment

Development of an algorithm for managing a multi-robot system for cargo transportation based on reinforcement learning in a virtual environment

Visual Servoing and Kalman Filter Applied to Parallel Manipulator 3-RRR

Analysis and Classification of Rehabilitation Robotic Systems Based on Parallel Manipulators

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