How to make Maze Solver robot in 5 mins : https://youtu.be/MXioRPCVOF8
Maze Solver Demonstration : https://youtu.be/sV5-sxzwgt4
Maze solving problem is a very old problem, but still now it is considered as an important field of robotics. This field is based on decision making algorithms. The main aim of this project is to make an Arduino based efficient autonomous maze solver robot. Two simple mazes solving algorithms “Wall following algorithm” and “Flood fill algorithm” are used to make this robot. In this project Hardware development, software development and maze construction had been done. For performance testing, the robot will implement to solve 4×4 maze. Capability of finding the shortest path is also verified.
-- Musfiqur Rahman; email: [email protected]
Obstacle detection Robot using Ultrasonic Sensor and Arduino UNOSanjay Kumar
This document describes how to build an obstacle detection robot using an Arduino UNO, ultrasonic sensor, and motor driver module. It explains the components used, including the Arduino, ultrasonic sensor to detect obstacles from 2-400cm away, and an L298N motor driver module to control DC motors. It provides details on connecting the components, programming the ultrasonic sensor to trigger and receive echo signals to determine distances, and controlling the motor's direction depending on detected obstacles to help the robot navigate. Code and more details are available at the provided GitHub link.
School LAC Plan for school and classroom uses.docxIvyLood1
The document is a School Learning Action (SLAC) Plan submitted by Baring Elementary School for the 2023-2024 school year. It outlines the school's plan to conduct monthly Learning Action Cell (LAC) sessions for teachers to improve teaching competencies. The plan details the objectives, participants, budget, and scheduled activities for the LAC sessions, which will cover topics like instructional strategies, assessment techniques, and developing teaching skills. The goal is to continuously build the capacity of teachers through collaboration and sharing of best practices.
Schizophrenia is a mental disorder characterized by abnormal social behavior and failure to recognize what is real. It is believed to be caused by a combination of genetic and environmental factors. Common symptoms include false beliefs, unclear thinking, hearing voices, reduced social engagement, and lack of motivation. Diagnosis is based on observed behavior and reported experiences, and involves meeting criteria in diagnostic manuals. Treatment primarily involves antipsychotic medication, which can help reduce positive symptoms within weeks but has limited impact on negative symptoms and cognitive dysfunction.
El documento presenta información sobre el sistema PLD + ADM de Mercedes Benz, incluyendo una descripción de sus componentes principales (módulo PLD, módulo ADM), sus funciones (control de inyección, sensores, alimentación) y diagramas de su configuración.
This robot follows a black line on a bright surface or white line on a dark surface using IR sensors to detect the line. It uses a microcontroller, IR sensors, motor driver, and DC motors to sense the line and drive the wheels to stay on the line. When the sensors detect the line on one side, the microcontroller stops that side's motor to turn the robot.
This document describes a 4-bit synchronous binary counter. It contains the truth table for a JK flip-flop, diagrams of the counter circuit using 4 JK flip-flops connected in series with a common clock, and tables showing the output logic states and timing diagram as the counter counts from 0 to 15 over 16 clock pulses.
Thia presentation is presented by Naveed Ahmed, Rizwan Mustafa and Muzaffar Ahmad at Robot Expo in Information Technology University of Punjab, Lahore.
Autonomous robots are robots that can perform tasks intelligently depending on themselves, without any human assistance. Maze Solving Robot is one of the most popular autonomous robots. It is a smallself-reliant robot that can solve a maze from a known starting position to the center area of the maze in the shortest possible time.
This document describes the design and functioning of a light following robot. The robot uses light dependent resistors (LDRs) to sense light and an op-amp circuit to compare the light readings from the LDRs. When more light falls on one LDR, the op-amp output activates the corresponding transistor which drives the motor on that side, causing the robot to turn towards the light source. The robot aims to follow a light source such as a flashlight by moving its motors based on the LDR sensor readings processed by the op-amp circuitry. Applications include uses in street lights, alarms, and devices that adjust screen brightness based on ambient lighting.
This document describes a line following robot project built using an Arduino microcontroller. It lists the components used, which include the Arduino UNO, IR sensors, an L298N motor driver, DC motors, and a chassis. It explains the working principle of how the IR sensors detect a line and the motor driver is used to control the DC motors to follow the line. Diagrams of the circuit, programming code, potential applications, and advantages/disadvantages of the line following robot are also provided.
The document outlines requirements for a line following robot and discusses methods for line detection. It lists key requirements as being able to follow and take turns along a line, while being insensitive to lighting and noise. It also notes the line color does not matter as long as it is darker or lighter than the surroundings. The document further explains that infrared sensors produce analog outputs that need to be converted to digital signals, which can be done using analog to digital converters or comparators. It also provides an overview of features of the 8051 microcontroller, including memory, serial communication ports, timers, I/O pins, interrupts and clock speed.
Introduction to PLL - phase loop lock diagramHai Au
This document provides an introduction and overview of phase-locked loops (PLLs). It discusses the basic components of a PLL including the phase detector, voltage controlled oscillator (VCO), and loop filter. It describes how PLLs are used for applications such as frequency synthesis and data recovery. The document also provides examples of PLL design, including selecting component values for a VCO and calculating PLL parameters like voltage output and frequency sensitivity.
This document describes the design of a line-following robot that uses an ATMega8 microcontroller. The robot uses IR sensors to detect a black or white line and follow it, taking turns automatically. It includes IR sensors, a comparator IC, motor driver IC, DC motors, and a microcontroller board to process sensor input and control the motors accordingly to follow the line. The robot is able to detect the line with the IR sensors, send the sensor signals to the microcontroller via comparators, and have the microcontroller turn the appropriate motor on or off to steer the robot along the line.
The document describes the design and development of a 4-legged walking robot. It discusses the use of an Arduino Uno microcontroller and servo motors to control the robot's legs. The pantograph leg mechanism is employed to simplify the robot's kinematics and reduce computational complexity for controlling the multi-degree of freedom movement required for walking. Software like the Arduino IDE is used to program the microcontroller to coordinate the servo motors to enable the robot's walking abilities.
A proximity sensor is a sensor able to detect the presence of nearby objects without any physical contact. It detects An Object When The Object Approaches Within The Detection Range And Boundary Of The Sensor. Proximity Sensor Includes All The Sensor That Perform Non-Contact Detection In Comparison To Sensors Such As Limit Switch, That Detect The Object By Physically Contacting Them. It is a sensor able to detect the presence of nearby objects without any physical contact
This slides are on the project: Line follower robot using arduino board and PID algorithm. If else coding makes the robot shake way often, so I took the time to learn PID and this is the result.
This document summarizes key concepts about combinational logic circuits. It defines combinational logic as circuits whose outputs depend only on the current inputs, in contrast to sequential logic which also depends on prior inputs. Common combinational circuits are described like half and full adders used for arithmetic, as well as decoders. The design process for combinational circuits is outlined involving specification, formulation, optimization and technology mapping. Implementation of functions using NAND and NOR gates is also discussed.
The document outlines a 5-step process for developing a PLC program for a paint spray application system. The steps are: 1) define the task, 2) define inputs and outputs, 3) develop a logical sequence of operation using a flowchart, 4) develop the PLC program, and 5) test the program. It then provides details for each step as applied to a conveyor system that feeds boxes through a spray nozzle, with inputs like start/stop buttons and outputs like conveyor motor and spray valve control.
The document describes a line follower robot that uses infrared sensors to detect and follow a black line on a white surface. It uses an L293d motor driver IC to control two DC motors and drive the wheels. An LM324 comparator IC compares the output of the IR sensors to a reference voltage to determine if the sensor is over the black line or white surface. The robot also uses an L7805 voltage regulator to maintain a constant voltage supply for the components. The robot is able to navigate tight curves by sensing the line with the IR sensors and maneuvering accordingly using the closed-loop control system.
This project report summarizes the design and working of a line follower robot. It discusses the components used including an LM324 comparator IC, AT89C51 microprocessor, L293D H-bridge motor driver, and IR transmitter and receiver. It explains how the IR sensors detect the line and the microprocessor controls the motors to follow the line by turning when sensors detect line edges. The working principle section describes the robot's line detection and movement logic in detail. Applications mentioned include industrial transport, automated vehicles, and museum tour guides.
This document describes the design and working of an intelligent line following robot. It uses infrared sensors to detect a black line on a white surface and a microcontroller to control motors that navigate the robot along the line. The microcontroller receives sensor input and determines whether the robot should move straight, turn right, or turn left to stay on the line. The line following robot demonstrates principles of sensing, feedback control, and programming intelligence into machines.
The document describes a self-balancing two-wheeled robot project. The goals of the project are to demonstrate techniques for balancing an unstable robotic platform on two wheels and to design a digital control system using a state space model. The robot uses motors, sensors like an accelerometer and gyroscope, and a microprocessor to automatically balance itself in the upright position like an inverted pendulum. It classifies the robot system into three main parts: an inertial sensor unit to read angular velocity and position, an actuator unit with motors driven by analog signals from the controller, and a logical processing unit that processes sensor inputs and controls the actuators to return the robot to vertical position when tilted.
JK flip-flops have two outputs, Q and Q', and four modes of operation: hold, set, reset, toggle. The primary output is Q. There are two stable states that can store state information. JK flip-flops are used for data storage in registers, counting in counters, and frequency division. They can divide the frequency of a periodic waveform in half by toggling on each input clock pulse.
This document summarizes a project using the Sphero robot and the A* pathfinding algorithm. Key points:
- The goal was to program Sphero to traverse any path in a 7x15 grid using A*.
- Commands like roll(), pause(), and connect() were used to control Sphero's movement and connection.
- A* was used to find the optimal path and convert it to a list of coordinates. These were then converted to roll() and pause() commands.
- Challenges included connecting Sphero via Bluetooth, getting it to execute commands reliably, and inconsistent speed responses to roll() commands.
The document describes a proposal for a line maze solver robot project. It includes an introduction to line mazes, the objectives of the project to build an autonomous robot that can solve a line maze, and the key components and methodology. The robot will use 6 light sensors to detect the black line on a white surface and make decisions at intersections. It will use an Arduino microcontroller to process sensor input and control the motors. The first run will record wrong turns to avoid on the second run when it can solve the maze quickly.
This robot follows a black line on a bright surface or white line on a dark surface using IR sensors to detect the line. It uses a microcontroller, IR sensors, motor driver, and DC motors to sense the line and drive the wheels to stay on the line. When the sensors detect the line on one side, the microcontroller stops that side's motor to turn the robot.
This document describes a 4-bit synchronous binary counter. It contains the truth table for a JK flip-flop, diagrams of the counter circuit using 4 JK flip-flops connected in series with a common clock, and tables showing the output logic states and timing diagram as the counter counts from 0 to 15 over 16 clock pulses.
Thia presentation is presented by Naveed Ahmed, Rizwan Mustafa and Muzaffar Ahmad at Robot Expo in Information Technology University of Punjab, Lahore.
Autonomous robots are robots that can perform tasks intelligently depending on themselves, without any human assistance. Maze Solving Robot is one of the most popular autonomous robots. It is a smallself-reliant robot that can solve a maze from a known starting position to the center area of the maze in the shortest possible time.
This document describes the design and functioning of a light following robot. The robot uses light dependent resistors (LDRs) to sense light and an op-amp circuit to compare the light readings from the LDRs. When more light falls on one LDR, the op-amp output activates the corresponding transistor which drives the motor on that side, causing the robot to turn towards the light source. The robot aims to follow a light source such as a flashlight by moving its motors based on the LDR sensor readings processed by the op-amp circuitry. Applications include uses in street lights, alarms, and devices that adjust screen brightness based on ambient lighting.
This document describes a line following robot project built using an Arduino microcontroller. It lists the components used, which include the Arduino UNO, IR sensors, an L298N motor driver, DC motors, and a chassis. It explains the working principle of how the IR sensors detect a line and the motor driver is used to control the DC motors to follow the line. Diagrams of the circuit, programming code, potential applications, and advantages/disadvantages of the line following robot are also provided.
The document outlines requirements for a line following robot and discusses methods for line detection. It lists key requirements as being able to follow and take turns along a line, while being insensitive to lighting and noise. It also notes the line color does not matter as long as it is darker or lighter than the surroundings. The document further explains that infrared sensors produce analog outputs that need to be converted to digital signals, which can be done using analog to digital converters or comparators. It also provides an overview of features of the 8051 microcontroller, including memory, serial communication ports, timers, I/O pins, interrupts and clock speed.
Introduction to PLL - phase loop lock diagramHai Au
This document provides an introduction and overview of phase-locked loops (PLLs). It discusses the basic components of a PLL including the phase detector, voltage controlled oscillator (VCO), and loop filter. It describes how PLLs are used for applications such as frequency synthesis and data recovery. The document also provides examples of PLL design, including selecting component values for a VCO and calculating PLL parameters like voltage output and frequency sensitivity.
This document describes the design of a line-following robot that uses an ATMega8 microcontroller. The robot uses IR sensors to detect a black or white line and follow it, taking turns automatically. It includes IR sensors, a comparator IC, motor driver IC, DC motors, and a microcontroller board to process sensor input and control the motors accordingly to follow the line. The robot is able to detect the line with the IR sensors, send the sensor signals to the microcontroller via comparators, and have the microcontroller turn the appropriate motor on or off to steer the robot along the line.
The document describes the design and development of a 4-legged walking robot. It discusses the use of an Arduino Uno microcontroller and servo motors to control the robot's legs. The pantograph leg mechanism is employed to simplify the robot's kinematics and reduce computational complexity for controlling the multi-degree of freedom movement required for walking. Software like the Arduino IDE is used to program the microcontroller to coordinate the servo motors to enable the robot's walking abilities.
A proximity sensor is a sensor able to detect the presence of nearby objects without any physical contact. It detects An Object When The Object Approaches Within The Detection Range And Boundary Of The Sensor. Proximity Sensor Includes All The Sensor That Perform Non-Contact Detection In Comparison To Sensors Such As Limit Switch, That Detect The Object By Physically Contacting Them. It is a sensor able to detect the presence of nearby objects without any physical contact
This slides are on the project: Line follower robot using arduino board and PID algorithm. If else coding makes the robot shake way often, so I took the time to learn PID and this is the result.
This document summarizes key concepts about combinational logic circuits. It defines combinational logic as circuits whose outputs depend only on the current inputs, in contrast to sequential logic which also depends on prior inputs. Common combinational circuits are described like half and full adders used for arithmetic, as well as decoders. The design process for combinational circuits is outlined involving specification, formulation, optimization and technology mapping. Implementation of functions using NAND and NOR gates is also discussed.
The document outlines a 5-step process for developing a PLC program for a paint spray application system. The steps are: 1) define the task, 2) define inputs and outputs, 3) develop a logical sequence of operation using a flowchart, 4) develop the PLC program, and 5) test the program. It then provides details for each step as applied to a conveyor system that feeds boxes through a spray nozzle, with inputs like start/stop buttons and outputs like conveyor motor and spray valve control.
The document describes a line follower robot that uses infrared sensors to detect and follow a black line on a white surface. It uses an L293d motor driver IC to control two DC motors and drive the wheels. An LM324 comparator IC compares the output of the IR sensors to a reference voltage to determine if the sensor is over the black line or white surface. The robot also uses an L7805 voltage regulator to maintain a constant voltage supply for the components. The robot is able to navigate tight curves by sensing the line with the IR sensors and maneuvering accordingly using the closed-loop control system.
This project report summarizes the design and working of a line follower robot. It discusses the components used including an LM324 comparator IC, AT89C51 microprocessor, L293D H-bridge motor driver, and IR transmitter and receiver. It explains how the IR sensors detect the line and the microprocessor controls the motors to follow the line by turning when sensors detect line edges. The working principle section describes the robot's line detection and movement logic in detail. Applications mentioned include industrial transport, automated vehicles, and museum tour guides.
This document describes the design and working of an intelligent line following robot. It uses infrared sensors to detect a black line on a white surface and a microcontroller to control motors that navigate the robot along the line. The microcontroller receives sensor input and determines whether the robot should move straight, turn right, or turn left to stay on the line. The line following robot demonstrates principles of sensing, feedback control, and programming intelligence into machines.
The document describes a self-balancing two-wheeled robot project. The goals of the project are to demonstrate techniques for balancing an unstable robotic platform on two wheels and to design a digital control system using a state space model. The robot uses motors, sensors like an accelerometer and gyroscope, and a microprocessor to automatically balance itself in the upright position like an inverted pendulum. It classifies the robot system into three main parts: an inertial sensor unit to read angular velocity and position, an actuator unit with motors driven by analog signals from the controller, and a logical processing unit that processes sensor inputs and controls the actuators to return the robot to vertical position when tilted.
JK flip-flops have two outputs, Q and Q', and four modes of operation: hold, set, reset, toggle. The primary output is Q. There are two stable states that can store state information. JK flip-flops are used for data storage in registers, counting in counters, and frequency division. They can divide the frequency of a periodic waveform in half by toggling on each input clock pulse.
This document summarizes a project using the Sphero robot and the A* pathfinding algorithm. Key points:
- The goal was to program Sphero to traverse any path in a 7x15 grid using A*.
- Commands like roll(), pause(), and connect() were used to control Sphero's movement and connection.
- A* was used to find the optimal path and convert it to a list of coordinates. These were then converted to roll() and pause() commands.
- Challenges included connecting Sphero via Bluetooth, getting it to execute commands reliably, and inconsistent speed responses to roll() commands.
The document describes a proposal for a line maze solver robot project. It includes an introduction to line mazes, the objectives of the project to build an autonomous robot that can solve a line maze, and the key components and methodology. The robot will use 6 light sensors to detect the black line on a white surface and make decisions at intersections. It will use an Arduino microcontroller to process sensor input and control the motors. The first run will record wrong turns to avoid on the second run when it can solve the maze quickly.
This document describes a final exam for an embedded systems lab course involving programming an Arduino robot. The objectives are for teams of two students to program a robot that can navigate a tape playing field to either knock down water bottles or hunt down the opposing team's robot using sensors. The document outlines the playing field design, provides flowcharts describing the code, explains the code and programming process, discusses code limitations, and includes photos and an appendix with a development diary.
Obstacle Avoiding robot is a self thinking robot which can take decisions itself using programmed brain without any guidance from human beings. In our Project we use Infrared to sense obstacles and take movements accordingly. Our Project
mainly used in military application, small toys and also used in mines by increasing IR sensors.
Robotics is a branch of science that deals with Mechanical, Electrical and Software fields. Robots are the machines that are used in our day-to-day to life to reduce men power and work accurately without any distortions. Robots can be classified into two different sections basing upon their skills as Automated and Manual. Obstacle detector is a Automated robot which itself recognizes the obstacle in its path and moves in free direction. Robot detects the obstacle by using two IR Sensors placed in front.
The IR sensors are placed on left and right side of the robot through which continuous Infrared radiation is emitted for detection of obstacles in the path. These IR Sensors are connected to a controlling element AT89c51 µc. When a obstacle is placed in the path of robot IR beam is reflected to the sensor from the obstacle. On detecting obstacle in the path sensor sends 0 volts to µc. This 0 voltage is detected by Microcontroller which avoids the obstacle by taking left or right turn. Similarly if the sensor sends +5v to Microcontroller, the Microcontroller assumes it as clear path and makes the robot to move in straight.
Two motors namely right motor and left motor are connected to Motor driver IC (L293D). L293D is interface with Microcontroller. Microcontroller sends logic 0 & logic 1 as per the programming to driver IC which makes motors to rotate in clockwise and anticlockwise direction. Wheels attached to the motors rotate accordingly with the motor shaft causing in the moment of the robot by wheels. In front portion of the robot a free wheel is attached to move the robot easily in any direction as per the requirement.
A 12Volts DC battery is attached to the circuit. As the microcontroller and sensors requires only 5v, set of resistors and capacitors are used to supply 5v DC to them. Power Management System is not maintained in the circuit as the battery can be removed after the usage of robot. So it does not cause any loss in the power of battery.
This type of robots has multiple applications in various fields. They can be used to know the strength of the opposite army in defense system. They can be used as floor and wall cleaners. They are used in automated GPS vehicles to calculate the moment of the vehicle overhead. These robots are easy to construct and cheaper in cost with long durability.
Localization and navigation are important tasks for mobile robots. Localization involves determining a robot's position and orientation, which can be done using global positioning systems outdoors or local sensor networks indoors. Navigation involves planning a path to reach a goal destination. Common navigation algorithms include Dijkstra's algorithm, A* algorithm, potential field method, wandering standpoint algorithm, and DistBug algorithm. Each algorithm has different requirements and approaches to planning paths between a starting point and goal.
The document summarizes the work of a team designing a robot to navigate through an unknown maze efficiently. They tested different code for the robot to follow as well as modifications to its physical design. Their optimal solution involved code using a 3 bump threshold to trigger turns and adjustments that lowered the robot's center of gravity and extended its bumpers. Through testing, their robot averaged 56.7 seconds to complete mazes, demonstrating steady and reliable performance.
The document provides an introduction to robotics, including:
1) It discusses different definitions of robots and classes them based on their mobility and functions. It also explains the typical components of robots including their body, effectors, actuators, sensors, controller and software architecture.
2) It uses the example of the Roomba vacuum cleaning robot to illustrate concepts like its actuators, sensors, differential steering and control.
3) It introduces concepts in robotics like kinematics, forward and inverse kinematics, trajectory error compensation methods, potential field control and reactive control architectures. It also discusses Asimov's three laws of robotics.
The MexiLEGO Project created a graphical interface to simulate space missions for children using Lego Mindstorms robots. The interface allows children to control a robot and explore the world and universe in a fun way. It simulates communication delays between controllers on Earth, the Moon, Mars, and other locations. The interface and application were designed to be easy and enjoyable for children to use.
This Arduino code controls a line-following robot. It defines variables for the light sensors, motor speeds and directions, and starting speed. It includes a calibration routine to offset the sensor values and account for differences in the sensors. The main loop reads the sensor values, compares them to determine if it needs to turn, and sets the motor speeds accordingly to follow a black line.
The document discusses the components, working principle, and programming of a line following robot. It contains the following key points:
1. A line following robot uses IR sensors to sense a black line on a white surface and maneuvers itself to stay on the line by constantly correcting its position.
2. The main components are an Arduino microcontroller, IR sensors to detect the line, and motors controlled by an L298N motor driver.
3. The IR sensors detect the line and send signals to the Arduino, which determines if the robot needs to turn left, right, or go straight to stay centered on the line.
Graph Exploration Algorithm- Term Paper PresentationVarun Narang
The document presents a parallelized depth-first search (PDFS) algorithm for efficient environmental mapping by robot swarms. PDFS models the environment as a graph and divides the depth-first search exploration among multiple robots. At each time step, the algorithm assigns unfinished edges for robots to expand in parallel. While PDFS terminates when full exploration is complete, its worst-case runtime is no better than single-robot DFS due to scenarios where many robots finish early, leaving one robot as a bottleneck. The algorithm aims to efficiently map environments like crop fields to determine optimal pollination paths.
A Comprehensive and Comparative Study Of Maze-Solving Techniques by Implement...IOSR Journals
This document provides a comprehensive comparative study of different maze-solving techniques, specifically those used for autonomous robots in the "Micro mouse" competition. It analyzes algorithms based on graph theory like Depth-First Search (DFS), Breadth-First Search (BFS), Flood Fill, and a Modified Flood Fill. It also examines a non-graph theory Wall Follower approach. Simulation results show that graph theory algorithms like Modified Flood Fill are more efficient by requiring fewer distance updates and cells traversed to find the shortest path through mazes. The document concludes the Modified Flood Fill algorithm may be best suited for the micro mouse competition due to its efficiency in solving mazes.
This document provides a comprehensive comparative study of different maze-solving techniques, specifically those used for autonomous robots in the "Micro mouse" competition. It analyzes algorithms based on graph theory like Depth-First Search (DFS), Breadth-First Search (BFS), Flood Fill, and a Modified Flood Fill. It also examines a non-graph theory Wall Follower approach. Simulation results show that graph theory algorithms like Modified Flood Fill are more efficient by requiring fewer distance updates and cells traversed to find the shortest path through mazes. The document concludes the Modified Flood Fill algorithm may be best suited for the micro mouse competition due to its efficiency in solving mazes.
The advent of Mobile Robotics changed the definition of robotics and brought in some very interesting technologies paving the way for cutting edge sciences like AI, Behaviour Based Systems, etc
The document summarizes key concepts about robot motion. It discusses robot locomotion systems and common configurations like differential drive and tricycle drive. These configurations are non-holonomic and have constraints on instantaneous motion. The document also covers integrating motion in 2D using odometry equations to estimate new positions from motor rotations and control of DC motors using feedback from encoders. Path planning is discussed where a robot follows waypoints by turning to face the next point and driving straight towards it.
Obstacle Detection robot detects the obstacle to avoid collision using ultrasonic sensor. The motors are connected through motor driver IC to microcontroller , to control the speed PWM is used.
Improving Developer Productivity With DORA, SPACE, and DevExJustin Reock
Ready to measure and improve developer productivity in your organization?
Join Justin Reock, Deputy CTO at DX, for an interactive session where you'll learn actionable strategies to measure and increase engineering performance.
Leave this session equipped with a comprehensive understanding of developer productivity and a roadmap to create a high-performing engineering team in your company.
Discover 7 best practices for Salesforce Data Cloud to clean, integrate, secure, and scale data for smarter decisions and improved customer experiences.
Create Your First AI Agent with UiPath Agent BuilderDianaGray10
Join us for an exciting virtual event where you'll learn how to create your first AI Agent using UiPath Agent Builder. This session will cover everything you need to know about what an agent is and how easy it is to create one using the powerful AI-driven UiPath platform. You'll also discover the steps to successfully publish your AI agent. This is a wonderful opportunity for beginners and enthusiasts to gain hands-on insights and kickstart their journey in AI-powered automation.
Jira Administration Training – Day 1 : IntroductionRavi Teja
This presentation covers the basics of Jira for beginners. Learn how Jira works, its key features, project types, issue types, and user roles. Perfect for anyone new to Jira or preparing for Jira Admin roles.
Domino IQ – Was Sie erwartet, erste Schritte und Anwendungsfällepanagenda
Webinar Recording: https://www.panagenda.com/webinars/domino-iq-was-sie-erwartet-erste-schritte-und-anwendungsfalle/
HCL Domino iQ Server – Vom Ideenportal zur implementierten Funktion. Entdecken Sie, was es ist, was es nicht ist, und erkunden Sie die Chancen und Herausforderungen, die es bietet.
Wichtige Erkenntnisse
- Was sind Large Language Models (LLMs) und wie stehen sie im Zusammenhang mit Domino iQ
- Wesentliche Voraussetzungen für die Bereitstellung des Domino iQ Servers
- Schritt-für-Schritt-Anleitung zur Einrichtung Ihres Domino iQ Servers
- Teilen und diskutieren Sie Gedanken und Ideen, um das Potenzial von Domino iQ zu maximieren
Jeremy Millul - A Talented Software DeveloperJeremy Millul
Jeremy Millul is a talented software developer based in NYC, known for leading impactful projects such as a Community Engagement Platform and a Hiking Trail Finder. Using React, MongoDB, and geolocation tools, Jeremy delivers intuitive applications that foster engagement and usability. A graduate of NYU’s Computer Science program, he brings creativity and technical expertise to every project, ensuring seamless user experiences and meaningful results in software development.
Mark Zuckerberg teams up with frenemy Palmer Luckey to shape the future of XR...Scott M. Graffius
Mark Zuckerberg teams up with frenemy Palmer Luckey to shape the future of XR/VR/AR wearables 🥽
Drawing on his background in AI, Agile, hardware, software, gaming, and defense, Scott M. Graffius explores the collaboration in “Meta and Anduril’s EagleEye and the Future of XR: How Gaming, AI, and Agile are Transforming Defense.” It’s a powerful case of cross-industry innovation—where gaming meets battlefield tech.
📖 Read the article: https://www.scottgraffius.com/blog/files/meta-and-anduril-eagleeye-and-the-future-of-xr-how-gaming-ai-and-agile-are-transforming-defense.html
#Agile #AI #AR #ArtificialIntelligence #AugmentedReality #Defense #DefenseTech #EagleEye #EmergingTech #ExtendedReality #ExtremeReality #FutureOfTech #GameDev #GameTech #Gaming #GovTech #Hardware #Innovation #Meta #MilitaryInnovation #MixedReality #NationalSecurity #TacticalTech #Tech #TechConvergence #TechInnovation #VirtualReality #XR
Co-Constructing Explanations for AI Systems using ProvenancePaul Groth
Explanation is not a one off - it's a process where people and systems work together to gain understanding. This idea of co-constructing explanations or explanation by exploration is powerful way to frame the problem of explanation. In this talk, I discuss our first experiments with this approach for explaining complex AI systems by using provenance. Importantly, I discuss the difficulty of evaluation and discuss some of our first approaches to evaluating these systems at scale. Finally, I touch on the importance of explanation to the comprehensive evaluation of AI systems.
Neural representations have shown the potential to accelerate ray casting in a conventional ray-tracing-based rendering pipeline. We introduce a novel approach called Locally-Subdivided Neural Intersection Function (LSNIF) that replaces bottom-level BVHs used as traditional geometric representations with a neural network. Our method introduces a sparse hash grid encoding scheme incorporating geometry voxelization, a scene-agnostic training data collection, and a tailored loss function. It enables the network to output not only visibility but also hit-point information and material indices. LSNIF can be trained offline for a single object, allowing us to use LSNIF as a replacement for its corresponding BVH. With these designs, the network can handle hit-point queries from any arbitrary viewpoint, supporting all types of rays in the rendering pipeline. We demonstrate that LSNIF can render a variety of scenes, including real-world scenes designed for other path tracers, while achieving a memory footprint reduction of up to 106.2x compared to a compressed BVH.
https://arxiv.org/abs/2504.21627
Data Virtualization: Bringing the Power of FME to Any ApplicationSafe Software
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Your startup on AWS - How to architect and maintain a Lean and Mean accountangelo60207
Prevent infrastructure costs from becoming a significant line item on your startup’s budget! Serial entrepreneur and software architect Angelo Mandato will share his experience with AWS Activate (startup credits from AWS) and knowledge on how to architect a lean and mean AWS account ideal for budget minded and bootstrapped startups. In this session you will learn how to manage a production ready AWS account capable of scaling as your startup grows for less than $100/month before credits. We will discuss AWS Budgets, Cost Explorer, architect priorities, and the importance of having flexible, optimized Infrastructure as Code. We will wrap everything up discussing opportunities where to save with AWS services such as S3, EC2, Load Balancers, Lambda Functions, RDS, and many others.
1. INTRODUCTION
A maze is a complicated system of paths from entrance to exit. Maze
solving problem involves determining the path of a mobile robot from its
initial position to its destination while
travelling through environment consisting of obstacles. In addition, the robot
must follow the best possible path among various possible paths present in the
maze.
Maze solving - a seemingly minor challenge for the analytical minds of
humans – has generated enough curiosity and challenges for A.I. experts to
make their machines (robots) solve any given maze.
Applications of such autonomous vehicles range from simple tasks like
robots employed in industries to carry goods through factories, offices,
buildings and other workplaces to dangerous or difficult to reach areas like
bomb sniffing, finding humans in wreckages etc.
Some maze solving methods are designed to be used inside the maze
with no prior knowledge of the maze whereas others are designed to be used
by a computer program that can see whole maze at once. We used the former
one. Our mazes are simply – connected i.e., without any closed loops.
Autonomous maze solving robot that we have built first goes from start to
end searching in the maze using wall follower algorithm (left hand rule) and
then processes out the shortest possible path by eliminating dead ends and
takes that path next time . It does all of that without any human assistance.
OBJECTIVE :
1. Design and build the robot using ultrasonic sensors.
2. Understand and implement wall-follower algorithm.
3. Construct Arduino based program to solve the maze.
COMPONENTS REQUIRED
•Arduino UNO board
•Motor driver
•2 DC motors
•Bread board
•3 ultrasonic sensors
•2 9V battery
•Chassis
•2 tires and ball caster
2. WALL FOLLOWING ALGORITHM
The wall follower, one of the best known rules for traversing mazes, is
also known as either left-hand rule or the right-hand rule. Whenever the robot
reaches a junction, it will sense for the opening walls and select its direction
giving the priority to the selected wall. Here, the selected wall is left! The
priority in left hand wall is in the following order: left, straight and right and if
all the sides are closed, then U-turn.
The robot uses three ultrasonic sensors attached to it, to determine the
distance of the walls in three directions: Front, Left and Right. If the distance
of the wall from the bot is less than the set threshold value in a particular
direction, then the robot assigns it as closed and similarly if it is greater than
the set threshold value, then it assigns it as open. It also uses the left and right
wall distances to align itself parallel to the wall, while moving forward.
START
Reached the end
Left Wall
True
Front Wall
Yes
Right Wall
Yes
Yes
Right turn No
Straight No
U-Turn
FLOW CHART
Left turn No
Now update the array index
and store it in the array!
Call Reduce function to
shorten the path storing
array for any U-Turns, etc.
Now, set the index of the array,
storing the shortened path to the
initial value and continue
traversing the maze from
beginning.
Now, at each node, before
making a turn, verify it with the
final array and adhere to it get to
the end in a shorter way!
END.
False
3. As the bot traverses the maze, its path is stored in an array and is
simultaneously reduced by calling the function ‘reduce’,
if it can be shortened. For example, if we consider the
following part of the maze: at the node where it moved
forward over right, this turn along with its two previous
turns (U-turn and a left turn respectively) can be
shortened to ‘R’! That is, ‘LUF = R’ and now the array
is dynamically modified and the modified path is updated
into it. After reaching the end, the bot again starts from
the beginning and this time, at each node it checks with
the shortened array and follows it to go to the end in a
much shorter path!
In this way, while traversing the maze the bot remembers as well as
shortens and adheres it to reach the end in a much shorter path!
As it is said, the wall follower algorithm is amateur programmers’
favorite! This method works perfectly well for those mazes whose start and
end is wall connected or in other words for those mazes which are simply
connected, but gets stuck in loops. There are many algorithms which have
successfully overcome this problem of getting stuck in loops, but most of them
have one condition, that the maze be known priorly. Examples of some of the
algorithms are: Flood Fill Algorithm which is extensively used in
‘Micromouse Competitions’, the Pledge algorithm, the Tremaux’s algorithm,
etc.
4. CODE
#define fthold 12 // Threshold value in front direction
#define rthold 20 // Threshold value in right direction
#define lthold 20 // Threshold vlaue in left direction
const int t = 1050; // Time alotted for taking 90 degrees for 9V!
int tfr =2750; // Initial Time for which it moves forward when it chooses
forward over right.
int timefr; // Dynamically set time..for which it moves forward,when it
chooses forward over right.
int tlbef=440; // Time for which it moves forward before taking left turn.
int tlaf = 1150; // Time for which it moves forward after taking left turn.
int nf =0; // Number of times it chooses straight over right.
int nlr=0; // Number of times it takes left turn.
bool found=false; // If its true, it indicates that the bot has reached the
end!
int dir[100]; // Array for storing the path of the bot.
int i=-1; // For the indices of the dir array.
int j=0; // Implies the number of intersections bot passed through.
// Front US sensor.
const int trigPinf = 2;
const int echoPinf = 6;
// Right US sensor.
const int trigPinr = 8;
const int echoPinr = 5;
// Left US sensor.
const int trigPinl = 3;
const int echoPinl = 9;
//Booleans for recognising the walls. True if resp sensor distance is less than
the resp threshold vlaue.
bool fsensor; // For the front US sensor.
bool rsensor; // For the right US sensor.
bool lsensor; // For the left US sensor.
// Sorts and returns the median value of a five element array.
float middleval(float arr[])
{
for(int p=0;p<4;p++)
{
for(int q=0;q<4;q++)
{
The below code contains code both for Maze
Solving and Shortest path Simplification.
5. if(arr[q]>arr[q+1])
{
int temp = arr[q];
arr[q] = arr[q+1];
arr[q+1] = temp;
}
}
}
return arr[2]; // Median value.
}
// Moves the bot in the forward direction
void gofront()
{
// Moves forward adjusting its path
float ldist1 = leftdist();
float lconst = ldist1;
while(ldist1<=5) // Should turn a little to its right
{
digitalWrite(10,HIGH);
digitalWrite(11,LOW);
digitalWrite(12,LOW);
digitalWrite(13,LOW);
delay(t/65);
ldist1 = leftdist();
if(abs(lconst - ldist1)>=0.8||(ldist1>=3.6)){break;}
}
float rdist1 = rightdist();
float rconst = rdist1;
while(rdist1<=5.4) // Should turn a little to its left
{
digitalWrite(10,LOW);
digitalWrite(11,LOW);
digitalWrite(12,HIGH);
digitalWrite(13,LOW);
delay(t/65);
rdist1 = rightdist();
if(abs(rconst - rdist1)>=0.9){break;}
}
if(leftdist()>=7.2) // Will move little to its left if its too far from
the left wall
{
digitalWrite(10,LOW);
digitalWrite(11,LOW);
digitalWrite(12,HIGH);
digitalWrite(13,LOW);
6. delay(t/30);
}
digitalWrite(10,HIGH);
digitalWrite(11,LOW);
digitalWrite(12,HIGH);
digitalWrite(13,LOW);
}
// Returns the dist of wall in front of it
float frontdist()
{
float gapf;float ticktockf;
digitalWrite(trigPinf,LOW);
delayMicroseconds(2);
digitalWrite(trigPinf,HIGH);
delayMicroseconds(10);
digitalWrite(trigPinf,LOW);
ticktockf = pulseIn(echoPinf,HIGH); // in one cm there are 29 microseconds.
gapf = ticktockf*0.0344/2;
return gapf;
}
// Returns the distance the wall to its right side
float rightdist()
{
float gapr;float ticktockr;
digitalWrite(trigPinr,LOW);
delayMicroseconds(2);
digitalWrite(trigPinr,HIGH);
delayMicroseconds(10);
digitalWrite(trigPinr,LOW);
ticktockr = pulseIn(echoPinr,HIGH);
gapr = ticktockr*0.0344/2;
return gapr;
}
// Returns the distance of the wall to its left side
float leftdist()
{
float gapl;float ticktockl;
digitalWrite(trigPinl,LOW);
delayMicroseconds(2);
digitalWrite(trigPinl,HIGH);
delayMicroseconds(10);
digitalWrite(trigPinl,LOW);
ticktockl = pulseIn(echoPinl,HIGH);
8. digitalWrite(10,LOW);
digitalWrite(11,LOW);
digitalWrite(12,LOW);
digitalWrite(13,LOW);
}
// When it has to move forward according to the shortest path.(At some
intersection)
void frontturn()
{
for(int n=1;n<=8;n++)
{gofront();delay((timefr)/8);}
digitalWrite(10,LOW);
digitalWrite(11,LOW);
digitalWrite(12,LOW);
digitalWrite(13,LOW);
delay(1000);
}
// When it has to take a right turn according to the shortest path.
void rightturn()
{ stopit();delay(1000);
float prevfdist = frontdist();
//while( abs(frontdist()-prevfdist)<=(prevfdist/2)-1)
for(int n=1;n<=5;n++)
{gofront();delay(260);}
digitalWrite(10,HIGH);
digitalWrite(11,LOW);
digitalWrite(12,LOW);
digitalWrite(13,HIGH);
delay(t);
// gofront();delay(2400);
float prevfrdist = frontdist();
while( abs(frontdist()-prevfrdist)<=18)
/* for(int n=1;n<=10;n++)*/
{gofront();delay(300);}
digitalWrite(10,LOW);
digitalWrite(11,LOW);
digitalWrite(12,LOW);
digitalWrite(13,LOW);
delay(1000);
}
void setup() // put your setup code here, to run once:
{
9. // US pins setup..
pinMode (trigPinf,OUTPUT);
pinMode (echoPinf,INPUT);
pinMode (trigPinr,OUTPUT);
pinMode (echoPinr,INPUT);
pinMode (trigPinl,OUTPUT);
pinMode (echoPinl,INPUT);
pinMode( 4,INPUT); // FOR THE IR SENSOR...
// Motor pins.
pinMode(10,OUTPUT);
pinMode(11,OUTPUT);
pinMode(12,OUTPUT);
pinMode(13,OUTPUT);
Serial.begin(9600); //staartingg serial communication...9600 bits per second.
// dir[0] = 0; // initial direction..?
}
void loop() // put your main code here, to run repeatedly
{
if(nlr==7)
{
found=true; // Reached the end.
for(int i=0;i<=2;i++){Serial.print(dir[i]);}
i=-1;j=0; nlr=0; // Back to start again..
// Stops the bot for 30 seconds after reaching the end.
digitalWrite(10,LOW);
digitalWrite(11,LOW);
digitalWrite(12,LOW);
digitalWrite(13,LOW);
delay(30000);
}
float fdist; float rdist; float ldist; // front, right, and left
distances.
float fduration;float rduration;float lduration; // front, right, and left
travel time in echoPin.
float fdur[5]; float rdur[5]; float ldur[5]; // Arrays which store the
values of five durations... We will take only the median value(afer sorting)
with error bearing capacity of 40%.
float ldista[5];
// For the front US sensor..
for(int i=0;i<=4;i++)
{
digitalWrite(trigPinf,LOW); // Clearing the trigPin.
delayMicroseconds(5);
digitalWrite(trigPinf,HIGH); // Setting the trigPin HIGH for 10
microseconds..sends some 8cycle sonics.
10. delayMicroseconds(10);
digitalWrite(trigPinf,LOW);
fdur[i] = pulseIn(echoPinf,HIGH); // Returns the time for which the wave
travelled.
}
fduration = middleval(fdur);
fdist = fduration*0.0344/2; // Distance of the wall in the forward
direction
/*Serial.print("frontdistance: ");
Serial.println(fdist);*/
// for the right US sensor...
for(int i=0;i<=4;i++)
{
digitalWrite(trigPinr,LOW);
delayMicroseconds(5);
digitalWrite(trigPinr,HIGH);
delayMicroseconds(10);
digitalWrite(trigPinr,LOW);
rdur[i] = pulseIn(echoPinr,HIGH);
}
rduration = middleval(rdur);
rdist = rduration*0.0344/2; // Distance of the wall to its right.
/* Serial.print("rightdistance: ");
Serial.println(rdist);*/
// for the left US sensor....
for(int i=0;i<=4;i++)
{
digitalWrite(trigPinl,LOW);
delayMicroseconds(5);
digitalWrite(trigPinl,HIGH);
delayMicroseconds(10);
digitalWrite(trigPinl,LOW);
ldur[i] = pulseIn(echoPinl,HIGH);
}
lduration = middleval(ldur);
ldist = lduration*0.0344/2; // Distance of the wall to its left side
/* Serial.print("leftdistance: ");
Serial.println(ldist);*/
if((fdist>=125)||(rdist>=150)||(ldist>=400)) {return;} // Cancelling out any
error values...goes back to void loop().
11. fsensor = false;rsensor = false;lsensor = false; // Setting up the
booleans.
if(rdist<=rthold) rsensor = true;
if(ldist<=lthold) lsensor = true;
if(fdist<=fthold) fsensor = true;
// Left
Wall Following Algorithm!
// If left is closed-
if((lsensor==true))
{ // for a U-turn..
if((rsensor==true)&&(fsensor==true))
{ j=j+1;
i=i+1;
dir[i] = 3;
reduce(dir,i);
digitalWrite(10,HIGH);
digitalWrite(11,LOW);
digitalWrite(12,LOW);
digitalWrite(13,HIGH);
delay(2*t);
}
// If Front is open..
else if(fsensor==false)
{
if((rsensor==false)&&(frontdist()>=40)) // If both front and right are
open..
{
i = i+1;
j=j+1;
if((found==true)&(dir[i]!=0)) // After reaching the end ... checks the s
{
rightturn();return;
}
else
{
if(found==false){
dir[i] = 0; // moving forward over right...
reduce(dir,i);
}
/*Serial.print("for the jth turn ..");Serial.print(" =");Serial.print(j);
Serial.print(" the i value is ");Serial.print(i);Serial.print("and the dir
is ..");Serial.println(dir[i]);*/
timefr = tfr + 65*nf;
nf=nf+1;
12. stopit();delay(1000);
for(int g=1;g<=10;g++){gofront();delay(timefr/10);}
stopit();delay(1000);
}
}
else {gofront();delay(300);} // Else moving forward .. only front is open.
}
//for a right turn..
else
{
i = i+1;
j=j+1;
dir[i] = 2;
reduce(dir,i);
float prevfdist = frontdist();
while( abs(frontdist()-prevfdist)<=(prevfdist/2)-2)
{gofront();delay(300);if(frontdist()<=4.5){break;}}
digitalWrite(10,HIGH);
digitalWrite(11,LOW);
digitalWrite(12,LOW);
digitalWrite(13,HIGH);
delay(t);
float prevfrdist = frontdist();
while( abs(frontdist()-
prevfrdist)<=15.2){gofront();delay(300);if(frontdist()<=4.5){break;}}
}
}
else
{
//for a left turn..
i=i+1;
j=j+1;
if((found==true)&&(dir[i]!=1)){
if((dir[i]==2)&&(rightdist>=10)){rightturn();return;}
else if((dir[i]== 0)&&(fsensor==false))
{frontturn();return;}
}
else{
dir[i]=1; // Left turn..
13. nlr=nlr+1;
reduce(dir,i); //calling reduce function to shorten the path
dynamically..if path is not yet completed
{gofront(); delay(tlbef);}
digitalWrite(10,LOW); // takes a left turn..
digitalWrite(11,LOW);
digitalWrite(12,HIGH);
digitalWrite(13,LOW);
delay(2*t);
for(int n=1;n<=8;n++) { gofront();delay(tlaf/8);}
stopit();delay(1000);
}
}
delay(320);
}