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A universal architectural pattern and specification method for robot control system design

Kornuta T., Zieliński C., Winiarski T.

Bulletin of the Polish Academy of Sciences. Technical Sciences

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2020

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Vol. 68, nr 1

3--29

EN

The paper presents a universal architectural pattern and an associated specification method that can be applied in the design of robot control systems. The approach a describes the system in terms of embodied agents and proposes a multi-step decomposition enabling precise definition of their inner structure and operation. An embodied agent is decomposed into effectors, receptors, both real and virtual, and a control subsystem. Those entities communicate through communication buffers. The activities of those entities are governed by FSMs that invoke behaviours formulated in terms of transition functions taking as arguments the contents of input buffers and producing the values inserted into output buffers. The method is exemplified by applying it to the design of a control system of a robot executing one of the most important tasks for a service robot, i.e. picking up, by a position-force controlled robot, an object located using an RGB-D image acquired from a Kinect. Moreover in order to substantiate the universality of the presented approach we present how classical, known from the literature, robotic architectures can be expressed as systems composed of one or more embodied agents.

2

A Low-Cost EMG-Controlled Anthropomorphic Robotic Hand for Power and Precision Grasp

Sánchez-Velasco Leobardo E., Arias-Montiel Manuel, Guzmán-Ramírez Enrique, Lugo-González Esther

Biocybernetics and Biomedical Engineering

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2020

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Vol. 40, no. 1

221--237

EN

In this paper the use of a commercial EMG armband for the motion control of a prototype hand prosthesis is proposed. The mechanical design is based on an open source six degree-of-freedom hand. Some modifications from the original design are proposed, mainly in the actuation and power transmission devices to reduce the prototype's costs and to provide a major mobility to the thumb to adapt the motion to the shape of the grasped object. Unlike some similar prototypes previously reported and considering that the proposed application requires portability, the use of a PC for the acquisition and processing of the EMG data has been replaced by a portable hardware system based on the master/slave architecture. The master device is a Raspberry Pi-based subsystem interfaced with the EMG armlet for gathering and classifying information from the user's muscular activity. The slave device is an ATmega328 microcontroller-based subsystem that defines the movements of the robotic hand from the information collected and processed by the master device. Experimental results are presented to evaluate the performance of the EMG-controlled hand prosthesis carrying out different types of grasping tasks.

3

Pick-and-place task implementation using visual open-loop control

Kołosowski Paweł, Wolniakowski Adam, Bogdan Mariusz

Acta Mechanica et Automatica

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2019

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Vol. 13, no. 3

211--216

EN

In the ever increasing number of robotic system applications in the industry, the robust and fast visual recognition and pose estimation of workpieces are of utmost importance. One of the ubiquitous tasks in industrial settings is the pick-and-place task where the object recognition is often important. In this paper, we present a new implementation of a work-piece sorting system using a template matching method for recognizing and estimating the position of planar workpieces with sparse visual features. The proposed framework is able to distinguish between the types of objects presented by the user and control a serial manipulator equipped with parallel finger gripper to grasp and sort them automatically. The system is furthermore enhanced with a feature that optimizes the visual processing time by automatically adjusting the template scales. We test the proposed system in a real-world setup equipped with a UR5 manipulator and provide experimental results documenting the performance of our approach.

4

Compensating pose uncertainties through appropriate gripper finger cutouts

Wolniakowski A., Gams M., Kramberger A., Miatliuk K., Kiforenko L., Petersen H. G., Hagelskjaer F., Buch A. G., Krüger N., Chrysostomou D., Madsen O., Ude A.

Acta Mechanica et Automatica

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2018

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Vol. 12, no. 1

78--83

EN

The gripper finger design is a recurring problem in many robotic grasping platforms used in industry. The task of switching the gripper configuration to accommodate for a new batch of objects typically requires engineering expertise, and is a lengthy and costly iterative trial-and-error process. One of the open challenges is the need for the gripper to compensate for uncertainties inherent to the workcell, e.g. due to errors in calibration, inaccurate pose estimation from the vision system, or object deformation. In this paper, we present an analysis of gripper uncertainty compensating capabilities in a sample industrial object grasping scenario for a finger that was designed using an automated simulation-based geometry optimization method (Wolniakowski et al., 2013, 2015). We test the developed gripper with a set of grasps subjected to structured perturbation in a simulation environment and in the real-world setting. We provide a comparison of the data obtained by using both of these approaches. We argue that the strong correspondence observed in results validates the use of dynamic simulation for the gripper finger design and optimization.

5

Selecting genetic algorithms for assembler encoding

Praczyk T.

Archives of Control Sciences

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2008

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Vol. 18, no. 4

477-495

EN

Assembler Encoding makes it possible to use genetic algorithms to construct neural networks. Assembler Encoding represents neural network in a form of the so-called Assembler Encoding Program. The task of the program is to create Network Definition Matrix maintaining all the information necessary to construct the network. In Assembler Encoding different components of Assembler Encoding Programs evolve in separate populations. The evolution in each population can be controlled by a different genetic algorithm. In the experiments reported in the paper the following genetic algorithms were used to control the evolution of programs: Canonical Genetic Algorithm, Steady State Genetic Algorithm and Eugenic Algorithm. The programs created by means of the specified algorithms were used to create neural networks. Then, the networks were tested in the so-called predator-prey problem. The results of the experiments are presented at the end of the paper.