Software Architecture for (Robot-Sense, Think and Act ). It is general architecture for mobile robots for performing tasks.
A Layered architecture, use to build standard software by integrating robot subsystems and user logic.
Intelligent mobile Robotics & Perception SystemsIntelligent mobile Robotics ...Gouasmia Zakaria
Recent advances in human-robot interaction, complex robotic tasks, intelligent reasoning, and decision-making are, at some extent, the results of the notorious evolution and success of ML algorithms. This chapter will cover recent and emerging topics and use-cases related to intelligent perception systems in robotics.
Two guest lectures about motion planning in the course S2016 ECE 486: Robot Dynamics and Control, Spring 2016, Electrical and Computer Engineering Department, University of Waterloo. Useful Resources: - Open source libraries: http://ompl.kavrakilab.org/ http://wiki.ros.org/motion_planners http://moveit.ros.org/ - Book: Steven M. LaValle, Planning Algorithm. Available at: http://planning.cs.uiuc.edu/, last accessed, July 12, 2016
Intelligent mobile Robotics & Perception SystemsIntelligent mobile Robotics ...Gouasmia Zakaria
Recent advances in human-robot interaction, complex robotic tasks, intelligent reasoning, and decision-making are, at some extent, the results of the notorious evolution and success of ML algorithms. This chapter will cover recent and emerging topics and use-cases related to intelligent perception systems in robotics.
Two guest lectures about motion planning in the course S2016 ECE 486: Robot Dynamics and Control, Spring 2016, Electrical and Computer Engineering Department, University of Waterloo. Useful Resources: - Open source libraries: http://ompl.kavrakilab.org/ http://wiki.ros.org/motion_planners http://moveit.ros.org/ - Book: Steven M. LaValle, Planning Algorithm. Available at: http://planning.cs.uiuc.edu/, last accessed, July 12, 2016
Problem Characteristics in Artificial Intelligence,
Unit -2 Problem Solving and Searching Techniques
o choose an appropriate method for a particular problem first we need to categorize the problem based on the following characteristics.
Is the problem decomposable into small sub-problems which are easy to solve?
Can solution steps be ignored or undone?
Is the universe of the problem is predictable?
Is a good solution to the problem is absolute or relative?
Is the solution to the problem a state or a path?
What is the role of knowledge in solving a problem using artificial intelligence?
Does the task of solving a problem require human interaction?
1. Is the problem decomposable into small sub-problems which are easy to solve?
Can the problem be broken down into smaller problems to be solved independently?
See also Water Jug Problem in Artificial Intelligence
The decomposable problem can be solved easily.
Example: In this case, the problem is divided into smaller problems. The smaller problems are solved independently. Finally, the result is merged to get the final result.
Is the problem decomposable
2. Can solution steps be ignored or undone?
In the Theorem Proving problem, a lemma that has been proved can be ignored for the next steps.
Such problems are called Ignorable problems.
In the 8-Puzzle, Moves can be undone and backtracked.
Such problems are called Recoverable problems.
In Playing Chess, moves can be retracted.
Such problems are called Irrecoverable problems.
Ignorable problems can be solved using a simple control structure that never backtracks. Recoverable problems can be solved using backtracking. Irrecoverable problems can be solved by recoverable style methods via planning.
3. Is the universe of the problem is predictable?
In Playing Bridge, We cannot know exactly where all the cards are or what the other players will do on their turns.
Uncertain outcome!
For certain-outcome problems, planning can be used to generate a sequence of operators that is guaranteed to lead to a solution.
For uncertain-outcome problems, a sequence of generated operators can only have a good probability of leading to a solution. Plan revision is made as the plan is carried out and the necessary feedback is provided.
4. Is a good solution to the problem is absolute or relative?
The Travelling Salesman Problem, we have to try all paths to find the shortest one.
See also Generate and Test Heuristic Search - Artificial Intelligence
Any path problem can be solved using heuristics that suggest good paths to explore.
For best-path problems, a much more exhaustive search will be performed.
5. Is the solution to the problem a state or a path
The Water Jug Problem, the path that leads to the goal must be reported.
I.INFORMED SEARCH IN ARTIFICIAL INTELLIGENCE II. HEURISTIC FUNCTION IN AI III...vikas dhakane
Artificial Intelligence: Introduction, Typical Applications. State Space Search: Depth Bounded
DFS, Depth First Iterative Deepening. Heuristic Search: Heuristic Functions, Best First Search,
Hill Climbing, Variable Neighborhood Descent, Beam Search, Tabu Search. Optimal Search: A
*
algorithm, Iterative Deepening A*
, Recursive Best First Search, Pruning the CLOSED and OPEN
Lists
The main objective of our project is to provide an optimum solution to the traffic hazards and the road accidents. According to this project when a vehicle meets with an accident, immediately vibration sensor will detect the signal and sends it to ARM controller. Microcontroller sends the alert message through the GSM MODEM including the location to police control room or a rescue team. So the police can immediately trace the location through the GPS MODEM after receiving the information.
UNIT - I PROBLEM SOLVING AGENTS and EXAMPLES.pptx.pdfJenishaR1
Replicate human intelligence
Solve Knowledge-intensive tasks
An intelligent connection of perception and action
Building a machine which can perform tasks that requires human intelligence such as:
Proving a theorem
Playing chess
Plan some surgical operation
Driving a car in traffic
Creating some system which can exhibit intelligent behavior, learn new things by itself, demonstrate, explain, and can advise to its user.
What Comprises to Artificial Intelligence?
Artificial Intelligence is not just a part of computer science even it's so vast and requires lots of other factors which can contribute to it. To create the AI first we should know that how intelligence is composed, so the Intelligence is an intangible part of our brain which is a combination of Reasoning, learning, problem-solving perception, language understanding, etc.
To achieve the above factors for a machine or software Artificial Intelligence requires the following discipline:
Mathematics
Biology
Psychology
Sociology
Computer Science
Neurons Study
Statistics Advantages of Artificial Intelligence
Following are some main advantages of Artificial Intelligence:
High Accuracy with less errors: AI machines or systems are prone to less errors and high accuracy as it takes decisions as per pre-experience or information.
High-Speed: AI systems can be of very high-speed and fast-decision making, because of that AI systems can beat a chess champion in the Chess game.
High reliability: AI machines are highly reliable and can perform the same action multiple times with high accuracy.
Useful for risky areas: AI machines can be helpful in situations such as defusing a bomb, exploring the ocean floor, where to employ a human can be risky.
Digital Assistant: AI can be very useful to provide digital assistant to the users such as AI technology is currently used by various E-commerce websites to show the products as per customer requirement.
Useful as a public utility: AI can be very useful for public utilities such as a self-driving car which can make our journey safer and hassle-free, facial recognition for security purpose, Natural language processing to communicate with the human in human-language, etc.
This Presentation were Made By BugsBusters team from faculty of Computers and information, Helwan University - Egypt
IMPORTANT NOTE !!!
Do not view this online or it will not be compatible Download it to view videos and see original slides :))
This is implemented to designed a simple system called Smart Dustbin using Arduino, Ultrasonic Sensor and Servo Motor, where the lid of the dustbin will automatically open itself upon detection of human hand.
Robotics and Automation.
This slide describes the concepts of robotics and automation. Line follower is considered as the perfect start of automation robots.
Robo India here in this slide present Construction of Line follower using 8051 Micro controller. the same can be upgraded to obstacle avoiding robot or a wall follower robot.
We are hearing you. Please share your views, we are found at-
Website: http://roboindia.com
email:info@roboindia.com
Towards Rapid Implementation of Adaptive Robotic SystemsMeshDynamics
Current automation design practice produces expensive one-of-a-kind installations where the system cannot be easily modified to
meet changing demands or advancements in technology. It is imperative that we design robot systems to be modular, portable and
easily re-configurable in order to reduce the design lead times and life cycle costs of providing automation alternatives.
The Unified Tele-robotics Architecture Program (UTAP) was developed under the sponsorship of the US Air Force Robotics and
Automation Center of Excellence. A goal of the program was to define and develop prototypes of commonly used software building
blocks for sensor guided real time embedded control of telerobotic devices. Standard building blocks and a non-proprietary
communication protocols would provide the Air Force and specifically the Logistic Centers with a support infrastructure designed to
rapidly and efficiently build and maintain mission critical automation systems.
Discover how to do model execution in Capella, and how to embed digital mockup inside functions to do 'system simulation' with a higher confidence.
A common need in system architecture design is to verify
that the architect is correct and can satisfy its requirements.
Execution of system architect model means to interact with
state machines to test system’s control logic.
It can verify if the logical sequences of functions and interfaces
in different scenarios are desired.
However, only sequence itself is not enough to verify
its consequence or output.
So we need each function to do what it is supposed to do
during model execution to verify its output. That's what we called 'system simulation'.
Problem Characteristics in Artificial Intelligence,
Unit -2 Problem Solving and Searching Techniques
o choose an appropriate method for a particular problem first we need to categorize the problem based on the following characteristics.
Is the problem decomposable into small sub-problems which are easy to solve?
Can solution steps be ignored or undone?
Is the universe of the problem is predictable?
Is a good solution to the problem is absolute or relative?
Is the solution to the problem a state or a path?
What is the role of knowledge in solving a problem using artificial intelligence?
Does the task of solving a problem require human interaction?
1. Is the problem decomposable into small sub-problems which are easy to solve?
Can the problem be broken down into smaller problems to be solved independently?
See also Water Jug Problem in Artificial Intelligence
The decomposable problem can be solved easily.
Example: In this case, the problem is divided into smaller problems. The smaller problems are solved independently. Finally, the result is merged to get the final result.
Is the problem decomposable
2. Can solution steps be ignored or undone?
In the Theorem Proving problem, a lemma that has been proved can be ignored for the next steps.
Such problems are called Ignorable problems.
In the 8-Puzzle, Moves can be undone and backtracked.
Such problems are called Recoverable problems.
In Playing Chess, moves can be retracted.
Such problems are called Irrecoverable problems.
Ignorable problems can be solved using a simple control structure that never backtracks. Recoverable problems can be solved using backtracking. Irrecoverable problems can be solved by recoverable style methods via planning.
3. Is the universe of the problem is predictable?
In Playing Bridge, We cannot know exactly where all the cards are or what the other players will do on their turns.
Uncertain outcome!
For certain-outcome problems, planning can be used to generate a sequence of operators that is guaranteed to lead to a solution.
For uncertain-outcome problems, a sequence of generated operators can only have a good probability of leading to a solution. Plan revision is made as the plan is carried out and the necessary feedback is provided.
4. Is a good solution to the problem is absolute or relative?
The Travelling Salesman Problem, we have to try all paths to find the shortest one.
See also Generate and Test Heuristic Search - Artificial Intelligence
Any path problem can be solved using heuristics that suggest good paths to explore.
For best-path problems, a much more exhaustive search will be performed.
5. Is the solution to the problem a state or a path
The Water Jug Problem, the path that leads to the goal must be reported.
I.INFORMED SEARCH IN ARTIFICIAL INTELLIGENCE II. HEURISTIC FUNCTION IN AI III...vikas dhakane
Artificial Intelligence: Introduction, Typical Applications. State Space Search: Depth Bounded
DFS, Depth First Iterative Deepening. Heuristic Search: Heuristic Functions, Best First Search,
Hill Climbing, Variable Neighborhood Descent, Beam Search, Tabu Search. Optimal Search: A
*
algorithm, Iterative Deepening A*
, Recursive Best First Search, Pruning the CLOSED and OPEN
Lists
The main objective of our project is to provide an optimum solution to the traffic hazards and the road accidents. According to this project when a vehicle meets with an accident, immediately vibration sensor will detect the signal and sends it to ARM controller. Microcontroller sends the alert message through the GSM MODEM including the location to police control room or a rescue team. So the police can immediately trace the location through the GPS MODEM after receiving the information.
UNIT - I PROBLEM SOLVING AGENTS and EXAMPLES.pptx.pdfJenishaR1
Replicate human intelligence
Solve Knowledge-intensive tasks
An intelligent connection of perception and action
Building a machine which can perform tasks that requires human intelligence such as:
Proving a theorem
Playing chess
Plan some surgical operation
Driving a car in traffic
Creating some system which can exhibit intelligent behavior, learn new things by itself, demonstrate, explain, and can advise to its user.
What Comprises to Artificial Intelligence?
Artificial Intelligence is not just a part of computer science even it's so vast and requires lots of other factors which can contribute to it. To create the AI first we should know that how intelligence is composed, so the Intelligence is an intangible part of our brain which is a combination of Reasoning, learning, problem-solving perception, language understanding, etc.
To achieve the above factors for a machine or software Artificial Intelligence requires the following discipline:
Mathematics
Biology
Psychology
Sociology
Computer Science
Neurons Study
Statistics Advantages of Artificial Intelligence
Following are some main advantages of Artificial Intelligence:
High Accuracy with less errors: AI machines or systems are prone to less errors and high accuracy as it takes decisions as per pre-experience or information.
High-Speed: AI systems can be of very high-speed and fast-decision making, because of that AI systems can beat a chess champion in the Chess game.
High reliability: AI machines are highly reliable and can perform the same action multiple times with high accuracy.
Useful for risky areas: AI machines can be helpful in situations such as defusing a bomb, exploring the ocean floor, where to employ a human can be risky.
Digital Assistant: AI can be very useful to provide digital assistant to the users such as AI technology is currently used by various E-commerce websites to show the products as per customer requirement.
Useful as a public utility: AI can be very useful for public utilities such as a self-driving car which can make our journey safer and hassle-free, facial recognition for security purpose, Natural language processing to communicate with the human in human-language, etc.
This Presentation were Made By BugsBusters team from faculty of Computers and information, Helwan University - Egypt
IMPORTANT NOTE !!!
Do not view this online or it will not be compatible Download it to view videos and see original slides :))
This is implemented to designed a simple system called Smart Dustbin using Arduino, Ultrasonic Sensor and Servo Motor, where the lid of the dustbin will automatically open itself upon detection of human hand.
Robotics and Automation.
This slide describes the concepts of robotics and automation. Line follower is considered as the perfect start of automation robots.
Robo India here in this slide present Construction of Line follower using 8051 Micro controller. the same can be upgraded to obstacle avoiding robot or a wall follower robot.
We are hearing you. Please share your views, we are found at-
Website: http://roboindia.com
email:info@roboindia.com
Towards Rapid Implementation of Adaptive Robotic SystemsMeshDynamics
Current automation design practice produces expensive one-of-a-kind installations where the system cannot be easily modified to
meet changing demands or advancements in technology. It is imperative that we design robot systems to be modular, portable and
easily re-configurable in order to reduce the design lead times and life cycle costs of providing automation alternatives.
The Unified Tele-robotics Architecture Program (UTAP) was developed under the sponsorship of the US Air Force Robotics and
Automation Center of Excellence. A goal of the program was to define and develop prototypes of commonly used software building
blocks for sensor guided real time embedded control of telerobotic devices. Standard building blocks and a non-proprietary
communication protocols would provide the Air Force and specifically the Logistic Centers with a support infrastructure designed to
rapidly and efficiently build and maintain mission critical automation systems.
Discover how to do model execution in Capella, and how to embed digital mockup inside functions to do 'system simulation' with a higher confidence.
A common need in system architecture design is to verify
that the architect is correct and can satisfy its requirements.
Execution of system architect model means to interact with
state machines to test system’s control logic.
It can verify if the logical sequences of functions and interfaces
in different scenarios are desired.
However, only sequence itself is not enough to verify
its consequence or output.
So we need each function to do what it is supposed to do
during model execution to verify its output. That's what we called 'system simulation'.
[Capella Day 2019] Model execution and system simulation in CapellaObeo
A common need in system architecture design is to verify that if the architect is correct and can satisfy its requirements. Execution of system architect model means to interact with state machines to test system’s control logic. It can verify if the logical sequences of functions and interfaces in different scenarios are desired.
However, only sequence itself is not enough to verify its consequence or output. So we need each function to do what it is supposed to do during model execution to verify its output, and that is what we called “system simulation”.
This presentation introduces how we do model execution in Capella, and how to embed digital mockup inside functions to do “system simulation” with a higher confidence.
Renfei Xu, Glaway
Renfei Xu is the technical manager of MBSE solution in Glaway. He has participated in many pilot projects of MBSE in areas like Engine Control, Avionics, Mechatronics and so on. In recent years, he is responsible for the deployment of MBSE using Capella and ARCADIA methodology in a Radar research institute.
Wenhua Fang, Glaway
Wenhua Fang is the Director of Systems Engineering in Glaway. He has more than 12 years of working experience in SE.
He is responsible for more than 10 implementation projects of MBSE in areas like Aircraft, Engine Control, Avionics, Automotive and so on. In recent years, he leads the team to deploy MBSE in China(including using Capella and ARCADIA methodology).
This paper is focused on developing a platform that
helps researchers to create verify and implement their
machine learning algorithms to a humanoid robot in real
environment. The presented platform is durable, easy to fix,
upgrade, fast to assemble and cheap. Also, using this platform
we present an approach that solves a humanoid balancing
problem, which uses only fully connected neural network as a
basic idea for real time balancing. The method consists of 3
main conditions: 1) using different types of sensors detect the
current position of the body and generate the input
information for the neural network, 2) using fully connected
neural network produce the correct output, 3) using servomotors make movements that will change the current position
to the new one. During field test the humanoid robot can
balance on the moving platform that tilts up to 10 degrees to
any direction. Finally, we have shown that using our platform
we can do research and compare different neural networks in
similar conditions which can be important for the researchers
to do analyses in machine learning and robotics.
Robots can be autonomous, semi-autonomous or
remotely controlled [6].
The robot arm is widely used in many industries and dangerous areas. Automatic control of the robotic
manipulator involves study of kinematic. The kinematic problem is defined as the transformation from the
Cartesian space to the joint space and vice versa This system include the kinematic control which is used for
picking and placing the object in its workspace. There are many types of robot arm in the world of engineering.
This research describes design of jointed robot arm control system using kinematic modelling. The main focus of
this system is to control the end-effector of robot arm to achieve the desired position in the workspace using
MATLAB programming, microcontroller and inverse kinematic modelling. The MATLAB window(GUI) is used
the inverse kinematic for the requirement data for the specified angle of the arm and displayed on the computer.
The description of this system is to implement the hardware components for the moving process and to control
servo motors with pulse width driving circuit. PIC and Max-232 been used to drive for the servo motors of the
control system and receiving serial data from the computer. The control program is written in Mikro-C
programming language.
Simulation of Signals with Field Signal SimulatorIOSR Journals
Abstract: In the recent trends the field signal processing is an emerging technology for data acquisition systems, controlling application systems and automation system in real time environment. Versa Modular European (VME) and CRIO based hardware to simulate the field signals for the computer based control and instrumentation panel. Simulator is used to transmit multiple number of signals at a time. FSS software is a generic software to simulate the field signals for a computer based control and instrumentation system. Its general purpose nature easily extends its capabilities to build and perform unit under test(UUT’s) ATP specific test routines. Field signal simulator (FSS)is also an Automatic Testing Equipment (ATE). The main the Scope of this project covers real time computer (RTC) systems used for Signal Processing & Control application and the simulation techniques used to achieve automation by testing these RTC systems. It also includes RTC hardware and the software used for process & control applications. Simulation hardware & software used to test VME system is also included in the scope of this study.
A common need in system architecture design is to verify that if the architect is correct and can satisfy its requirements.
Execution of system architect model means to interact with state machines to test system’s control logic. It can verify if the logical sequences of functions and interfaces in different scenarios are desired.
However, only sequence itself is not enough to verify its consequence or output. So we need each function to do what it is supposed to do during model execution to verify its output, and that is what we called “simulation”.
This presentation introduced how to embed Python or MATLAB® codes inside functions to do “simulation” within Capella.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
2. What and Why ?
Layered Architecture (Mobile Robots)
Advantages & Disadvantages
Robot with Layered Architecture
Queries____
3. It refers how a system is divided into subsystems and how those
subsystems interact .(Sense , Think, Act)
It is distinguished from other software architectures because of the
special needs of robot systems. -
A hierarchical set of control loops, representing high-level mission
planning on high-end computing platforms.
For controlling path planning, robot trajectory, obstacle avoidance
4. Driver Layer
Platform Layer
Algorithm Layer
User Interface Layer
Depending on the target hardware, the software layers could potentially be
distributed across multiple targets.
In most cases, all of the layers run on one computing platform.
5. Fig. Autonomous mobile robot with a manipulator
Tasks: Including path planning, Obstacle avoidance, and Mapping.
Used :Agriculture, Logistics, or Search and Rescue.
6. o It handles the low-level driver functions required to operate sense and act of the robot.
o It depends in sensors and actuators used in the system and
also other hardware that the driver software runs on.
o It takes raw sensor data, turn it into meaningful engineering units,
and pass the sensor values to the other architecture levels (ex: analog to digital)
7. Interface to Common Sensors and Actuators
Use : From low-cost infrared sensors to high-definition light detection and ranging
(LIDAR) sensors.
A physics-based environment simulator to switch between hardware and simulation.(Lab VIEW Robotics Module )
8. o It translates data between the driver layer and the higher level algorithm
layer by converting low-level information into a more complete picture
for the higher levels of the software and vice versa .
o It corresponds to the physical hardware configuration of the robot.
Figure .The platform layer translates between the driver layer and algorithm layer.
9. o It represent the high-level control algorithms for the robotic system.
o It take system information such as position, velocity, or processed video
images and make control decisions based on all of the feedback.
o Example of obstacle avoidance using a vector field histogram (VFH).
-> VFH block receives distance data from a distance sensor, which was sent from the platform layer.
-> The output of the VFH block contains path direction, which is sent down to the platform layer.
-> In the platform layer, the path direction is input into the steering algorithm, which generates
low-level code that can be sent directly to the motors at the driver layer.
Figure . The algorithm layer makes control decisions based on feedback.
10. o It provides physical interaction between the robot and a human operator or
displays relevant information on a host PC or Devices.
Example from Fig:
o It read input from a mouse or joystick, or to drive a simple text display.
o It displays live image data from the onboard camera, and the X and Y coordinates of
nearby obstacles on a map. The servo angle control allows the user to rotate
the onboard servo motor that the camera is attached to;
Figure . The user interface layer allows a user to interact with a robot or display information.
11. Reusing components of code in future projects.
Easy to simulate and testing .
Develop custom modules for different environments.
Easy to select right hardware and increase scalability.
Disadvantages
It depends on hardware devices of Robot system.
Software testing flaws cause to damage hardware.
We can not use for multiple tasks.
12. Sample structure (Path Planning)
Device Layer
Sensors , Motors and Relays
-> Send Analog to Digital signal
Platform Layer (Embedded logic )
-> Receive data and send input to micro controller.
-> Receive Image data from cameras .
Algorithm Layer(User Logic with programming)
->Get inputs form platform layer and other Sub
systems.
-> Check path with Image processing algorithm
and compare sensor outputs.(C, Java)
->Send output of logic to platform layer.
->Platform layer sends signals to sensors and
actuators to steer wheel directions and change
image device.
UI Layer(GUI + Remote control devices )
Figure . Three layer software architecture