Summary report: A preliminary investigation into the use of fuzzy logic for the control of redundant manipulators

The Rice University Department of Mechanical Engineering and Materials Sciences' Robotics Group designed and built an eight degree of freedom redundant manipulator. Fuzzy logic was proposed as a control scheme for tasks not directly controlled by a human operator. In preliminary work, fuzzy logic control was implemented for a camera tracking system and a six degree of freedom manipulator. Both preliminary systems use real time vision data as input to fuzzy controllers. Related projects include integration of tactile sensing and fuzzy control of a redundant snake-like arm that is under construction

Real-Time Inverse Dynamic Deep Neural Network Tracking Control for Delta Robot Based on a COVID-19 Optimization

This paper presents a new technique to design an inverse dynamic model for a delta robot experimental setup to obtain an accurate trajectory. The input/output data were collected using an NI DAQ card where the input is the random angles profile for the three-axis and the output is the corresponding measured torques. The inverse dynamic model was developed based on the deep neural network (NN) and the new COVID-19 optimization to find the optimal initial weights and bias values of the NN model. Due to the system uncertainty and nonlinearity, the inverse dynamic model is not enough to track accurately the preselected profile. So, the PD compensator is used to absorb the error deviation of the end effector. The experimental results show that the proposed inverse dynamic deep NN with PD compensator achieves good performance and high tracking accuracy. The suggested control was examined using two different methods. The spiral path is the first, with a root mean square error of 0.00258 m, while the parabola path is the second, with a root mean square error of 0.00152 m

A hybrid switched reactive-based visual servo control of 5-DOF robot manipulators for pick-and-place tasks

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Intelligent control of mobile robot with redundant manipulator & stereovision: quantum / soft computing toolkit

The task of an intelligent control system design applying soft and quantum computational intelligence technologies discussed. An example of a control object as a mobile robot with redundant robotic manipulator and stereovision introduced. Design of robust knowledge bases is performed using a developed computational intelligence – quantum / soft computing toolkit (QC/SCOptKBTM). The knowledge base self-organization process of fuzzy homogeneous regulators through the application of end-to-end IT of quantum computing described. The coordination control between the mobile robot and redundant manipulator with stereovision based on soft computing described. The general design methodology of a generalizing control unit based on the physical laws of quantum computing (quantum information-thermodynamic trade-off of control quality distribution and knowledge base self-organization goal) is considered. The modernization of the pattern recognition system based on stereo vision technology presented. The effectiveness of the proposed methodology is demonstrated in comparison with the structures of control systems based on soft computing for unforeseen control situations with sensor system

Implementasi Pengolahan Citra Menggunakan Metode YOLO pada Security Robot dibidang Pertanian

Greenhouse merupakan salah satu bentuk solusi pertanian modern untuk membudidayakan tanaman yang tidak sesuai dengan iklim tropis, khususnya di Indonesia. Namun, pembangunan greenhouse itu sendiri memerlukan biaya yang cukup mahal. Sumber daya perangkat elektronik yang diperoleh dari panel surya digunakan untuk menyediakan pasokan listrik kepada perangkat elektronik seperti exhaust fan, panel surya, dan perangkat lainnya. Sayangnya, sering kali terjadi kasus-kasus orang yang tidak bertanggung jawab melakukan pencurian atau merusak properti dan tanaman di area sekitar greenhouse, yang dapat merugikan petani. Penelitian ini bertujuan untuk mendeteksi objek (manusia) yang melintas di sekitar greenhouse, peneliti menggunakan teknik pengolahan citra sebagai mata robot untuk mendeteksi manusia di mana objek selain manusia diabaikan. Metode yang digunakan dalam penelitian ini adalah YOLOv3-tiny, yang merupakan metode pembaharuan dari Convolutional Neural Network (CNN). YOLOv3-tiny akan melakukan prediksi terhadap objek yang akan dideteksi dengan bounding box sebagai output. Selanjutnya, YOLOv3-tiny akan memilih bounding box yang paling sesuai dalam memprediksi objek. Hasil pengujian menunjukkan bahwa robot mampu mendeteksi objek berupa manusia, serta menghitung akurasi kinerja model

Visual Servoing

The goal of this book is to introduce the visional application by excellent researchers in the world currently and offer the knowledge that can also be applied to another field widely. This book collects the main studies about machine vision currently in the world, and has a powerful persuasion in the applications employed in the machine vision. The contents, which demonstrate that the machine vision theory, are realized in different field. For the beginner, it is easy to understand the development in the vision servoing. For engineer, professor and researcher, they can study and learn the chapters, and then employ another application method

Implementasi Image Processing pada Robot Pertanian

Penelitian ini menggunakan penelitian riset. Permasalahan pada penelitian ini ialah bagaimana robot pemantau mendeteksi objek yang berada didepannya. Penelitian ini bertujuan untuk mempelajari sistem deteksi robot pemantau dengan menggunakan metode Image Segmentation. Subjek yang digunakan dalam penelitian ini ialah tanaman dilahan pertanian green house. Sedangkan teknik pengumpulan data yang digunakan adalah observasi dan wawancara secara langsung di lapangan. Hasil dari penelitian ini adalah sebuah gambar yang dihasilkan oleh kamera, lalu ditampilkan pada layar monitor ataupun HP agar para pemantau dan petani dapat melihat hasil yang diperoleh. Lalu, kamera ini bisa digunakan untuk memprediksi atau memberikan kejelasan kepada para pemantau agar dapat dianalisa dan dilihat apakah tanaman tersebut telah sesuai dengan waktu yang ditentukan, contohnya melihat apakah tanaman tersebut siap untuk dipanen maupun melihat tanaman tersebut tumbuh dengan baik atau tidak. Selain itu, penelitian ini bertujuan untuk meringankan pekerjaan para petani pada saat melakukan pemantauan disiang hari jika pada hari tersebut tidak dapat dipantau secara manual

Visual Servoing in Robotics

Visual servoing is a well-known approach to guide robots using visual information. Image processing, robotics, and control theory are combined in order to control the motion of a robot depending on the visual information extracted from the images captured by one or several cameras. With respect to vision issues, a number of issues are currently being addressed by ongoing research, such as the use of different types of image features (or different types of cameras such as RGBD cameras), image processing at high velocity, and convergence properties. As shown in this book, the use of new control schemes allows the system to behave more robustly, efficiently, or compliantly, with fewer delays. Related issues such as optimal and robust approaches, direct control, path tracking, or sensor fusion are also addressed. Additionally, we can currently find visual servoing systems being applied in a number of different domains. This book considers various aspects of visual servoing systems, such as the design of new strategies for their application to parallel robots, mobile manipulators, teleoperation, and the application of this type of control system in new areas

Autonomous navigation of a wheeled mobile robot in farm settings

This research is mainly about autonomously navigation of an agricultural wheeled mobile robot in an unstructured outdoor setting. This project has four distinct phases defined as: (i) Navigation and control of a wheeled mobile robot for a point-to-point motion. (ii) Navigation and control of a wheeled mobile robot in following a given path (path following problem). (iii) Navigation and control of a mobile robot, keeping a constant proximity distance with the given paths or plant rows (proximity-following). (iv) Navigation of the mobile robot in rut following in farm fields. A rut is a long deep track formed by the repeated passage of wheeled vehicles in soft terrains such as mud, sand, and snow. To develop reliable navigation approaches to fulfill each part of this project, three main steps are accomplished: literature review, modeling and computer simulation of wheeled mobile robots, and actual experimental tests in outdoor settings. First, point-to-point motion planning of a mobile robot is studied; a fuzzy-logic based (FLB) approach is proposed for real-time autonomous path planning of the robot in unstructured environment. Simulation and experimental evaluations shows that FLB approach is able to cope with different dynamic and unforeseen situations by tuning a safety margin. Comparison of FLB results with vector field histogram (VFH) and preference-based fuzzy (PBF) approaches, reveals that FLB approach produces shorter and smoother paths toward the goal in almost all of the test cases examined. Then, a novel human-inspired method (HIM) is introduced. HIM is inspired by human behavior in navigation from one point to a specified goal point. A human-like reasoning ability about the situations to reach a predefined goal point while avoiding any static, moving and unforeseen obstacles are given to the robot by HIM. Comparison of HIM results with FLB suggests that HIM is more efficient and effective than FLB. Afterward, navigation strategies are built up for path following, rut following, and proximity-following control of a wheeled mobile robot in outdoor (farm) settings and off-road terrains. The proposed system is composed of different modules which are: sensor data analysis, obstacle detection, obstacle avoidance, goal seeking, and path tracking. The capabilities of the proposed navigation strategies are evaluated in variety of field experiments; the results show that the proposed approach is able to detect and follow rows of bushes robustly. This action is used for spraying plant rows in farm field. Finally, obstacle detection and obstacle avoidance modules are developed in navigation system. These modules enables the robot to detect holes or ground depressions (negative obstacles), that are inherent parts of farm settings, and also over ground level obstacles (positive obstacles) in real-time at a safe distance from the robot. Experimental tests are carried out on two mobile robots (PowerBot and Grizzly) in outdoor and real farm fields. Grizzly utilizes a 3D-laser range-finder to detect objects and perceive the environment, and a RTK-DGPS unit for localization. PowerBot uses sonar sensors and a laser range-finder for obstacle detection. The experiments demonstrate the capability of the proposed technique in successfully detecting and avoiding different types of obstacles both positive and negative in variety of scenarios