This document discusses robotics research being conducted at the Robotics Laboratory at Wakayama University Graduate School of System Engineering. It lists three references used in their research - a 2014 paper on robot control in the IHI Engineering Review journal, a 2009 paper on robot motion planning in the IEEE Transactions on Robotics journal, and the use of MoveIt! and Blender software for robotics projects. The document provides brief information about the Robotics Laboratory and references used to support their work.
A 2-day workshop on robotics was held in Tezpur University, India in April 2016. It was organized by e-Yantra, an initiative of IIT Bombay to spread robotics education. 12 institutions from Assam, Meghalaya and Mizoram participated, with 4 faculty from each learning robotics using a Firebird V kit. Hands-on sessions covered robot control, sensors and programming. 9 institutions expressed interest in setting up robotics labs and received kits. The workshop aimed to train teachers to implement hands-on robotics experiments.
The document describes the various software tools used in the Robotics Laboratory at Wakayama University Graduate School of System Engineering. It lists Ubuntu 16.04 LTS, PCL 1.8, MORSE Simulator 1.4, ROS Kinetic Kame, Blender 2.76, Python 3.5.2, Protege 5.1.0, Apache 2.4.18, PostgreSQL 9.5.5, Webmin 1.820, and Tomcat 8.5.9 as some of the key software packages. It also provides brief descriptions and instructions for tools like VLC, Kinect, PCL, Jetson TK1, ROS Turtle Sim, MORSE,
This document provides instructions for installing and building the Point Cloud Library (PCL) on an Ubuntu system. It outlines downloading dependencies using apt-get, adding a PCL PPA repository, cloning the PCL GitHub repository, and running cmake and make to build and install PCL. The document also lists some related topics like PostgreSQL, Kinect, deep learning frameworks, robot simulators, and motion planning software that can be used with PCL.
The document discusses the Robotics Laboratory at Wakayama University Graduate School of System Engineering. It outlines the laboratory's work with deep learning, ROS/MORSE simulation, force application, SLIM programming, SVM algorithms, tactile and force sensors, RFID, LRF, IMU, RGBD sensors. It also describes projects involving ROS-TMS, SmartPal V, and MORSE simulation. The laboratory researches topics such as grasping, localization, and navigation to advance robotics.
The document discusses setting up and configuring Webmin and Tomcat on a server. It provides instructions for installing Webmin and configuring its settings to allow access from localhost. It also provides instructions for installing Java, downloading and extracting Tomcat, creating a Tomcat user, and starting Tomcat so it can be accessed from localhost.
The document discusses setting up Apache web server and PostgreSQL database on a server. It provides commands to install and configure Apache to use a server name and set index files. It also provides commands to install and configure PostgreSQL, create a database and user, insert and query data from a table, then remove the table and database. The last page lists other technologies that could also be set up which include UIMA, MySQL and webmini.
The document discusses the Robotics Laboratory at Wakayama University Graduate School of System Engineering. It outlines the laboratory's work with deep learning, ROS/MORSE simulation, force application, SLIM programming, SVM algorithms, tactile and force sensors, RFID, LRF, IMU, RGBD sensors. It also describes projects involving ROS-TMS, SmartPal V, and MORSE simulation. The laboratory researches topics such as grasping, localization, and navigation to advance robotics.
This document discusses software and tools used in a robotics laboratory, including ROS, MORSE simulator, Blender, Python, and Protege ontology software. It also outlines concepts in ontologies like instances, subclasses, parts, and roles. Finally, it mentions Apache UIMA and webmini frameworks.
The document discusses a robotics laboratory at Wakayama University Graduate School of System Engineering. It lists the software used in the laboratory, including Ubuntu 16.04 LTS, MORSE Simulator 1.4, ROS Kinetic Kame, Blender 2.76, Python 3.5.2, Pyenv, Anaconda 4.1.1, and Protege 5.1.0. It also mentions using the MORSE Simulator kitchen environment with a PR2 robot and the Protege Ontology tool.
This document provides information about software tools used in a robotics laboratory, including the MORSE simulator, ROS, Blender, Python, and the Protege ontology editor. It describes how to set up and configure these tools, edit simulation scenarios in MORSE, troubleshoot ROS package paths in Python, and access example ontologies in Protege. Setup instructions are provided for Ubuntu, ROS Kinetic, Blender, Python, and downloading and running Protege.
This document discusses setting up an environment for using MORSE simulator with ROS. It describes installing Ubuntu 16.04 LTS, MORSE simulator 1.4, ROS Kinetic Kame, Blender 2.76, Python 3.5.2. It also discusses checking the environment setup, editing MORSE ROS tutorial scripts in Blender, resolving import errors for rospy by modifying the Python path, and finding the rospy package location.
This document provides an overview of the software and tools used in the Robotics Laboratory at Wakayama University Graduate School of System Engineering. It details the use of 3 Kinect sensors, CNN, TensorFlow, Caffe and other deep learning tools. It also lists the software installed including Ubuntu 16.04, ROS Kinetic, Blender 2.76, Python 2.7 and 3.5, Pyenv, Anaconda and the MORSE simulator for developing and testing robotics applications.
The document describes the Robotics Laboratory at Wakayama University Graduate School of System Engineering. It details the various software and tools used, including ROS, MORSE simulator, Blender, and Python. It provides instructions for setting up and using ROS, MORSE simulator, and controlling robots in a simulated environment using Python scripts.
This document discusses robotics research being conducted at the Robotics Laboratory of Wakayama University Graduate School of System Engineering. The laboratory is using a Leap Motion, Kinect, robot hand, and JETSON TK1 with soracom internet access to conduct research. References are provided relating to robot service initiatives, grasping techniques, and object discovery in 3D scenes via shape analysis.
The document summarizes a research paper titled "Object Discovery in 3D scenes via Shape Analysis" presented at the International Conference on Robotics and Automation (ICRA) in 2013. The paper presents an algorithm that takes 3D meshes of indoor environments as input and ranks segments as potential object candidates based on intrinsic shape measures like compactness, symmetry, and smoothness. It evaluates the approach on a dataset of 58 indoor scenes collected with Kinect Fusion, achieving an average precision of 0.92 in detecting objects.
The document discusses a robotics laboratory at Wakayama University Graduate School of System Engineering. It lists the software used in the laboratory, including Ubuntu 16.04 LTS, MORSE Simulator 1.4, ROS Kinetic Kame, Blender 2.76, Python 3.5.2, Pyenv, Anaconda 4.1.1, and Protege 5.1.0. It also mentions using the MORSE Simulator kitchen environment with a PR2 robot and the Protege Ontology tool.
This document provides information about software tools used in a robotics laboratory, including the MORSE simulator, ROS, Blender, Python, and the Protege ontology editor. It describes how to set up and configure these tools, edit simulation scenarios in MORSE, troubleshoot ROS package paths in Python, and access example ontologies in Protege. Setup instructions are provided for Ubuntu, ROS Kinetic, Blender, Python, and downloading and running Protege.
This document discusses setting up an environment for using MORSE simulator with ROS. It describes installing Ubuntu 16.04 LTS, MORSE simulator 1.4, ROS Kinetic Kame, Blender 2.76, Python 3.5.2. It also discusses checking the environment setup, editing MORSE ROS tutorial scripts in Blender, resolving import errors for rospy by modifying the Python path, and finding the rospy package location.
This document provides an overview of the software and tools used in the Robotics Laboratory at Wakayama University Graduate School of System Engineering. It details the use of 3 Kinect sensors, CNN, TensorFlow, Caffe and other deep learning tools. It also lists the software installed including Ubuntu 16.04, ROS Kinetic, Blender 2.76, Python 2.7 and 3.5, Pyenv, Anaconda and the MORSE simulator for developing and testing robotics applications.
The document describes the Robotics Laboratory at Wakayama University Graduate School of System Engineering. It details the various software and tools used, including ROS, MORSE simulator, Blender, and Python. It provides instructions for setting up and using ROS, MORSE simulator, and controlling robots in a simulated environment using Python scripts.
This document discusses robotics research being conducted at the Robotics Laboratory of Wakayama University Graduate School of System Engineering. The laboratory is using a Leap Motion, Kinect, robot hand, and JETSON TK1 with soracom internet access to conduct research. References are provided relating to robot service initiatives, grasping techniques, and object discovery in 3D scenes via shape analysis.
The document summarizes a research paper titled "Object Discovery in 3D scenes via Shape Analysis" presented at the International Conference on Robotics and Automation (ICRA) in 2013. The paper presents an algorithm that takes 3D meshes of indoor environments as input and ranks segments as potential object candidates based on intrinsic shape measures like compactness, symmetry, and smoothness. It evaluates the approach on a dataset of 58 indoor scenes collected with Kinect Fusion, achieving an average precision of 0.92 in detecting objects.
Welcome to the March 2025 issue of WIPAC Monthly the magazine brought to you by the LinkedIn Group WIPAC Monthly.
In this month's edition, on top of the month's news from the water industry we cover subjects from the intelligent use of wastewater networks, the use of machine learning in water quality as well as how, we as an industry, need to develop the skills base in developing areas such as Machine Learning and Artificial Intelligence.
Enjoy the latest edition
Optimization of Cumulative Energy, Exergy Consumption and Environmental Life ...J. Agricultural Machinery
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Optimal use of resources, including energy, is one of the most important principles in modern and sustainable agricultural systems. Exergy analysis and life cycle assessment were used to study the efficient use of inputs, energy consumption reduction, and various environmental effects in the corn production system in Lorestan province, Iran. The required data were collected from farmers in Lorestan province using random sampling. The Cobb-Douglas equation and data envelopment analysis were utilized for modeling and optimizing cumulative energy and exergy consumption (CEnC and CExC) and devising strategies to mitigate the environmental impacts of corn production. The Cobb-Douglas equation results revealed that electricity, diesel fuel, and N-fertilizer were the major contributors to CExC in the corn production system. According to the Data Envelopment Analysis (DEA) results, the average efficiency of all farms in terms of CExC was 94.7% in the CCR model and 97.8% in the BCC model. Furthermore, the results indicated that there was excessive consumption of inputs, particularly potassium and phosphate fertilizers. By adopting more suitable methods based on DEA of efficient farmers, it was possible to save 6.47, 10.42, 7.40, 13.32, 31.29, 3.25, and 6.78% in the exergy consumption of diesel fuel, electricity, machinery, chemical fertilizers, biocides, seeds, and irrigation, respectively.
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, Vol.54, No.2, pp.32-36 (2014)
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MoveIt! Blender](https://image.slidesharecdn.com/170126-170127061432/85/170126-2-320.jpg)
