Lecture 04: Sensors
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This document discusses robotics perception and sensors. It provides an overview of forward and inverse kinematics, feedback control concepts, and different types of sensors used in robotics like laser rangefinders, infrared sensors, and their performance characteristics. It also outlines an exercise for designing a robot capable of various tasks like vacuuming, lawn mowing, or ball collection and discusses the necessary sensors, algorithms, and mechanisms to address. Homework assigned is to read a section on perception and complete a questionnaire.
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Lecture 04
This document discusses robot sensors and perception. It begins with a review of robot kinematics and control concepts like forward and inverse kinematics, feedback control, and proportional control. It then covers specific sensors like laser rangefinders, infrared sensors, and cameras. Examples of sensors on robots like Roomba, PrairieDog, Nao, and PR2 are provided. The performance aspects of sensors like range, resolution, linearity, and accuracy are defined. Case studies of laser rangefinders and infrared distance sensors are described in detail relating their physics and working principles to their performance capabilities and limitations. An exercise is proposed to design robots for vacuuming, lawn mowing, and ball collecting giving consideration to necessary sensors, algorithms, and mechanisms
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2) It uses the example of the Roomba vacuum cleaning robot to illustrate concepts like its actuators, sensors, differential steering and control.
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2) Forward kinematics determines end effector position from joint angles. Inverse kinematics determines joint angles for a desired end effector position.
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In this thesis, a method for forward and inverse kinematics analysis of a 5-DOF and a 7- DOF Redundant manipulator
is proposed. Obtaining the trajectory and computing the required joint angles for a higher DOF robot manipulator is one
of the important concerns in robot kinematics and control. The difficulties in solving the inverse kinematics equations
of these redundant robot manipulator arises due to the presence of uncertain, time varying and non-linear nature of
equations having transcendental functions. In this thesis, the ability of ANFIS is used to the generated data for solving
inverse kinematics problem. A single- output Sugeno-type FIS using grid partitioning has been modeled in this work.
The forward kinematics and inverse kinematics for a 5-DOF and 7-DOF manipulator are analyzed systemically. The Efficiency
of ANFIS can be concluded by observing the surface plot, residual plot and normal probability plot. This current
study in using different nonlinear models for the prediction of the IKs of a 5-DOF and 7-DOF Redundant manipulator
will give a valuable source of information for other modellers.
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notation.
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The document discusses robot drive systems and actuators. It describes the main types of drive systems used in robotics which are electric, hydraulic, and pneumatic. It provides details on electric drive systems including servomotors, stepper motors, and direct-drive electric motors. Hydraulic and pneumatic actuators are also discussed. The advantages and disadvantages of different mechanical drive systems and electric motors are summarized. Finally, the document discusses robot sensors and vision systems which allow robots to sense their environment.
3-Robot drive system, functions of drive systems, pneumatic systems, electric...
The document discusses robot drive systems and actuators. It describes the three main types of drive systems - electric, hydraulic, and pneumatic. Electric systems use motors like servomotors and stepper motors. Hydraulic systems are used for large robots and can deliver high power. Pneumatic systems are used for smaller robots and offer fine accuracy and speed. The document also discusses actuators, sensors, and end effectors that are important components of robot systems.
Semi Autonomous Hand Launched Rotary Wing Unmanned Air Vehicles
The document summarizes research being conducted on semi-autonomous hand-launched rotary-wing unmanned air vehicles (RUAVs) at Penn State. The research aims to design avionics systems using low-cost processors and sensors to enable reliable semi-autonomous control of small electric-powered quad-rotor RUAVs. Several processing and sensor options are being evaluated. Preliminary flight testing is being conducted using a PC-104 system on a Draganflyer III airframe. A custom-designed autopilot board is also being developed and tested. The researchers are taking an incremental approach to control design, starting with stability augmentation and moving towards semi-autonomous and autonomous capabilities.
Lecture 06: Features and Uncertainty
1. The document discusses various topics related to robotics including vision, midterm exams, locomotion, control, mapping, and homework.
2. Key concepts that are reviewed include line detection, calculating uncertainty from sensor measurements, propagating uncertainty in features, and different algorithms for mapping like watershed segmentation.
3. Exercises are assigned related to writing controllers for different robots, dealing with obstacles, and improving control parameters. Students are divided into groups to work on a final project to implement a controller.
Design of a Low-cost Autonomous Mobile Robot
Detection of obstacles during navigation of a mobile robot is considered difficult due to varying nature in terms of size, shape and location of the obstacle as well as ambient light that interferes with infra-red (IR) signals of the robot. In this paper, we propose a novel low-cost method that successfully guides a robot along a path using image processing and IR sensor circuits. A high continuous IR signal is converted into a low continuous IR signal by means of a demodulation circuit that enables a peripheral interface controller to receive this low continuous IR signal and take relevant decisions based on the signal. The images taken from a web camera are preprocessed to remove noise and detect edges. Subsequently, an image processing routine effectively calculates the angle to be rotated of the front wheels using a scan line algorithm. A minimum mean distance error of 2.45 was observed in tracking the path at a signal-to-nose ratio of 26.50. The accuracy of speech recognition was 92% for two voice training sessions.
Two wheeled self balancing robot for autonomous navigation
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High speed measurement
The document discusses various techniques for high precision speed measurement. It describes laser Doppler anemometry which uses lasers to measure the Doppler shift induced by moving particles to calculate speed. Optical fibers can also be used, with light signals affected by passing trains allowing their speed detection. GPS methods calculate speed from position data recorded at intervals. Interferometric techniques precisely measure small displacements over time for speed. Accuracy, non-contact operation and suitability for different applications are advantages of optical speed measurement methods.
Sensing
This document provides an overview of sensors for robotics applications. It discusses why robots need sensors to collect information about their environment through sensing, planning and acting. It describes different types of sensors categorized by their working mechanism, including active and passive sensors, exteroceptive and proprioceptive sensors, and sensors based on different mediums like electromagnetic radiation, sound, touch, and chemicals. Common sensors like encoders, tilt sensors, light sensors, temperature sensors, potentiometers, and cameras are presented with examples. The document concludes with questions about applying various sensors in robotics.
Sensors-and-Actuators-working principle and types of sensors
The document provides an overview of a presentation on sensors and actuators for a robotics club. It discusses:
1. The comparison between transducers, sensors, and actuators.
2. Descriptions and classifications of different sensor types including active vs passive sensors.
3. How actuators work and examples like motors.
4. The computer process control system and concepts like analog to digital conversion, sampling, quantization, and encoding.
EECS221 - Final Report
This document describes a method for indoor localization using a smartphone and Bluetooth Low Energy (BLE) sensor tag. The sensor tag contains an accelerometer, gyroscope, and other sensors to measure a user's motion and transmit the data via BLE to a smartphone app. The app uses dead reckoning algorithms integrating accelerometer and gyroscope data to calculate the user's distance and direction of movement over time without GPS or network connectivity. Challenges included increasing the sensor sampling rate and integrating data from multiple sensors. The described method provides indoor navigation when outdoor positioning systems like GPS are unavailable.
Eecs221 final report
This document describes a method for indoor localization using a smartphone and Bluetooth Low Energy (BLE) sensor tag. The sensor tag contains an accelerometer, gyroscope, and other sensors to measure a user's motion and transmit the sensor data via BLE to a smartphone app. The app uses dead reckoning algorithms integrating accelerometer and gyroscope data to calculate the user's distance and direction of movement over time without GPS or network connectivity. Challenges addressed include increasing the sensor sampling rate and integrating accelerometer and gyroscope data to limit position error accumulation. The described system provides indoor navigation when outdoor positioning systems like GPS are unavailable.
Indoor localisation and dead reckoning using Sensor Tag™ BLE.
The mobile application uses readings of the Accelerometer and Gyroscope from the Sensor Tag to describe details of motion in a planar mode. The project has been implemented as a part of the EECS 221 coursework at University of California, Irvine.
UAV Presentation
The document discusses the design and development of quadcopter unmanned aerial vehicles (UAVs). It describes the prototypes created, including improvements made to reduce weight and increase lift. Sensors and controllers are discussed, including sensors for position, proximity, and navigation. The final prototype achieved stable hovering with a weight of 43 grams and incorporated an inertial measurement unit, ultrasonic sensors, GPS, and radio frequency transmission for control and data transmission.
IRJET- Automatic Luggage Follower
This document describes the development of an automatic luggage follower robot. The robot is intended to follow its owner through busy airports while hauling their luggage. It uses an ultrasonic sensor to detect the position of the owner and determine its own path to follow them. Additional ultrasonic sensors detect obstacles to prevent collisions. A PIC microcontroller controls the sensors, GPS, GSM module, and DC motors. The GPS tracks the luggage's location, and GSM allows the owner to locate lost luggage by messaging the robot. This design aims to reduce the effort of transporting luggage for travelers.
Track 4 session 3 - st dev con 2016 - pedestrian dead reckoning
The document provides an overview of ST Microelectronics' Pedestrian Dead Reckoning (PDR) solution for indoor positioning. The PDR system uses MEMS sensors and algorithms to continuously track a user's position without needing connectivity. It consists of components for sensor calibration, step detection, stride length modeling, attitude filtering, carry position determination, and more. The PDR solution provides APIs to output location, activity mode, and other data to applications and can integrate with Android's location services.
All About Robotics (pdf)
Hello Everyone!
This is the best ppt on Robotics. It includes all the topics from history to today's latest technology. Those who are keen to know about artificial intelligence then this ppt is the best platform to start. I am sure that you will get to know how the robots are made, their operation in industries and how they can become a part of our future technology.
Do watch and learn the whole ppt and start learning more about it for your future career.
Remember the future is here!
Thank You!
First fare 2010 lab-view sensors for frc robots
A variety of sensors can enhance a FIRST robotics competition robot's operation, including ultrasonic sensors, gyroscopes, accelerometers, encoders, photoelectric sensors, and joysticks. These sensors provide input to drive the robot and make autonomous decisions. Common sensors include ultrasonic detectors for measuring distance, gyroscopes and accelerometers for sensing motion, encoders for monitoring wheel rotations and gear health, and photoelectric sensors for detecting objects. Joysticks also act as sensors, providing directional control and buttons that can be programmed to control additional robot functions. Diagrams and sample code are provided to illustrate how these sensors integrate with the robot control system.
208114036 l aser guided robo
The document describes a laser-guided vehicle that uses Java and MATLAB for control. It discusses using a laser scanner and other sensors on a MICA wheelchair to navigate through doorways and corridors. It outlines goals like driving through corridors and avoiding obstacles. It provides details on the sensors, control methods, and algorithms used, including Hough transforms of laser scans to identify walls and doors, and kinematic models to control the vehicle's movement and steering.
Robotics ppt
Hello Everyone!
This is the best ppt on Robotics. It includes all the topics from history to today's latest technology. Those who are keen to know about artificial intelligence then this ppt is the best platform to start. I am sure that you will get to know how the robots are made, their operation in industries and how they can become a part of our future technology.
Do watch and learn the whole ppt and start learning more about it for your future career.
Remember the future is here!
Thank You!
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Three-Dimensional Construction with Mobile Robots and Modular Blocks presents a system for constructing 3D structures using mobile robots and modular blocks. Passive blocks contain self-aligning connectors and communicate locally to maintain a "shape map" for construction. Active robots manipulate blocks and do not communicate. The system uses simple rules for block placement to reliably build any structure meeting criteria of no loops and limited overhangs. Various robot algorithms are evaluated for building structures with the lowest costs.Template classes and ROS messages
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Lecture 10: Summary
This document provides a summary of the key topics covered in an introduction to robotics course over 15 weeks, including:
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- Sensors like laser range scanners, their properties and how to model uncertainty. Visual sensors, odometry, and localization using Bayes' rule and Kalman filters.
- Midterm and final exam preparation advice focusing on understanding core concepts rather than derivations, and practicing examples like kinematics and localization problems.
- The final exam is scheduled for Wednesday, December 15 from 7:30-10:00 pm in the CAETE classroom.
Lecture 09: SLAM
1) Probabilistic state estimation techniques like the Kalman filter, extended Kalman filter, unscented Kalman filter, and particle filters allow estimating the state of a system using probabilistic models and sensor measurements with uncertainty.
2) The Kalman filter provides a recursive solution to the linear filtering problem by estimating the current state as a weighted average of the prior state and new measurements, with the weights based on their uncertainties.
3) Extensions to the Kalman filter like the extended and unscented Kalman filters allow handling nonlinear systems by linearizing around the current estimate, while particle filters represent the state distribution with random samples and are more flexible for nonlinear and non-Gaussian problems like SLAM.
Lecture 08: Localization and Mapping II
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Lecture 05
This document discusses various computer vision techniques for obtaining range and depth information from images, including depth from focus, depth from stereo, and optical filters. It introduces the pinhole camera model and explains how depth can be estimated from a single image by analyzing focus versus from multiple images using stereo vision. Examples of image processing techniques like convolution and morphological operations are also provided. The challenges of object recognition are discussed, noting that perception depends on assumptions about lighting, geometry, frequency, color and other factors that can be ambiguous.
Lecture 03 - Kinematics and Control
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Lecture 02: Locomotion
This document provides an introduction to robot locomotion. It defines locomotion as moving from place to place and discusses various forms of locomotion including crawling, sliding, running, jumping, walking and rolling. It also covers locomotion in nature like swimming, gliding and flying. Key relationships between different forms of locomotion are examined. The document discusses walking versus rolling locomotion and how to characterize different locomotion schemes. Designing robots that can crawl, slide, gallop, jump, walk and roll is proposed as an exercise.
Lecture 01
This document outlines the syllabus for an introduction to robotics course. It will cover topics like locomotion, kinematics, perception, localization, planning and navigation. Students will gain hands-on experience programming robots in simulation and debates. Assessments include weekly reading assignments, a midterm, debate and class participation. The course will not cover components building or software engineering, focusing instead on simulation. Students will design controllers for robots in a competition called Ratslife to practice perception, navigation and planning skills.
Lectures 11+12: Debates
What are the safety risks with autonomous robots? Are autonomous robot a threat to human workers? How can robots achieve intelligent behavior? What mechanisms are needed to reach human agility?
Lecture 09: Localization and Mapping III
This document provides an overview of localization and mapping techniques for robotics, including:
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- The Kalman filter for optimally fusing uncertain sensor measurements and updating location estimates.
- Simultaneous localization and mapping (SLAM) and the "hen-egg" problem of needing a map to localize and a location to build a map.
- Feature-based SLAM approaches that build maps from distinct environmental features.
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- Key challenges in SLAM like recognizing previously visited places and handling dynamic environments.
Lecture 10: Navigation
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Lecture 08: Localization and Mapping II
This document discusses algorithms for localization and mapping in robotics, including Dijkstra's algorithm, A* search, odometry, GPS, landmarks, and gyroscopes. It introduces probabilistic localization using Markov localization on discrete state spaces like grids or topological maps. Bayes' rule is used to update the probability of a robot's location based on sensor readings and motion. Particle filters are presented as a way to reduce complexity by random sampling rather than maintaining a full probability distribution over all locations. Examples show mapping and localization in a grid world using a particle filter approach. The homework assignment is to read about Markov localization and particle filters, and next week will include design reviews from student groups.
Lecture 07: Localization and Mapping I
This document discusses localization and mapping for robotics. It introduces topics like gyroscopes, odometry, GPS, and landmarks for localization. It discusses uncertainty models using Gaussian distributions and error propagation. Methods for belief representation are presented, including parametric single/multi hypothesis and non-parametric particle filters. Environment representations like continuous, discrete, and topological maps are described. The document provides an example of Google Maps and discusses belief representation in topological maps. It also covers multi-hypothesis belief representation, sensor data to topological maps using exact and Voronoi decompositions, and adaptive cell-size. The document assigns homework on navigation algorithms and reactive vs. deliberative planning.
Lecture 06: Features
This document provides an introduction to robotics features for computer vision. It discusses low-level image features like edges, colors and corners that can be extracted from sensors. Higher level features allow reasoning about the environment like object detection and segmentation. Examples demonstrate visual servoing using image features to grasp objects as well as fruit detection using filtering, segmentation and the Hough transform. The document also covers corner detection methods like Harris corners and scale-invariant feature transform (SIFT). It concludes with discussing using features to implement a robot controller and assigning project groups to work on different aspects.
Lecture 05: Vision
Vision is a challenging task for robots due to various ambiguities, including: depth discontinuities, surface orientation changes, reflectance changes, illumination changes, and different perspectives. Basic image processing techniques can help with tasks like edge detection, thresholding, morphological operations, and stereo vision calculations, but object recognition still requires combining these techniques with filters tuned to the specific objects. For homework, students should read about these topics and complete a questionnaire.
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Lecture 04: Sensors
- 1. Introduction to RoboticsPerception I CSCI 4830/7000 February 7, 2010 NikolausCorrell
- 2. Review: Kinematics and Control Concepts Forward Kinematics “Odometry” Feed-back Control Inverse Kinematics
- 3. Forward Kinematics How does the robot move in world space given its actuator speed and geometry? “Odometry”: forward kinematics for mobile platform Example: from exercise 3
- 4. More on robot kinematics (arms) John Craig Introduction to Robotics Mark Spong, Seth Hutchinson and M.Vidyasagar Robot Modeling and Control
- 5. Inverse Kinematics How do we need to control the actuators to reach a certain position? Inversion of forward kinematics Examples: Differential wheel drive (Exercise 3
- 6. Feedback control Use error between reference and actual state to calculate next control input Change in speed proportional to error Error zero -> speed zero Problem: find stable controllers Example: from exercise K. Ogata Modern Control Engineering
- 7. Today Perception: Basis for reasoning about the world Understand how a sensor works before using it Case studies
- 8. iRobotRoomba 4 Bumpers 2 Floor sensors 1 infrared distance (side) Infrared Wheel encoders
- 9. PrairieDog Roomba 5.6m, 240 degrees laser scanner Indoor localization system Camera Microphone 5 Position encoders (arm)
- 10. Nao 2 VGA cameras 4 Microphones 2-axis gyroscope 3-axis accelerometer 2 bumpers (feet) Tactile sensors (hands + feets) Hall-effect encoders 2 Sonar 2 Infrared Proprioceptive or Exteroceptive?
- 12. Laser Range Scanner Measures phase-shift of reflected signal Example: f=5MHz -> wavelength 60m
- 13. Examples 2 D 3D (PR2 sweep) (after classification)
- 14. Sensor performance Dynamic range: lowest and highest reading Resolution: minimum difference between values Linearity: variation of output as function of input Bandwidth: speed with which measurements are delivered Sensitivity: variation of output change as function of input change Cross-Sensitivity: sensitivity to environment Accuracy: difference between measured and true value Precision: reproducibility of results Hokuyo URG
- 15. Relation between sensor physics and performance (solutions) Dynamic range: Range: limited by power of light and modulated frequency, smallest wave-length difference measurable Angle: limited by physical setup / trade-off between bandwidth and angular resolution Resolution: Range: Precision of phase-shift measurement Angle: limited by bandwidth / encoder Linearity: Range: phase shift is linear -> signal is linear, but: weak reception makes determination of phase harder Angle: depends on motor implementation Bandwidth Range: speed of light, calculating phase shift Angle: motor speed Sensitivity: Range: Doppler effect -> not relevant in robotics, Confidence in the range (phase/time estimate) is inversely proportional to the square of the received signal amplitude Angle: n.a. Cross-Sensitivity: Range: Glass / reflection properties, 785nm light Accuracy: Range: Precision of phase-shift measurement, strength of reflected light Angle: motor quality Precision: range / variance
- 16. Infra-red distance sensors Principle: measure amount of reflected light The closer you get, the more light gets reflected Digitized with analog-digital converter Sharp IR Distance Sensor GP2Y0A02YK 20-150cm Miniature IR transceiver 0-3cm
- 17. Sensor performance Dynamic range: lowest and highest reading Resolution: minimum difference between values Linearity: variation of output as function of input Bandwidth: speed with which measurements are delivered Sensitivity: variation of output change as function of input change Cross-Sensitivity: sensitivity to environment Accuracy: difference between measured and true value Precision: reproducibility of results Sharp IR Distance Sensor
- 18. Relation between sensor physics and performance (solutions) Dynamic range: limited by power of light Resolution: limited by ADC, e.g. 10bit -> 1024 steps Linearity: highly non-linear (intensity decays quadratically) Bandwidth: limited by ADC bandwidth (sample&hold) Sensitivity: varies over range due to resolution Cross-Sensitivity: sun-light, surface properties Accuracy: limited by ADC, varies over range Precision: varies over range
- 19. Infra-red distance sensors in Webots (Exercise 1) Color of the bounding object affects sensor Non-linear relation between distance and signal strength Distance-dependent resolution and noise Software linearization Noise
- 20. Exercise Design a robot that can Vacuum a room Mow a lawn Collect golf-balls on a range Collect tennis balls on a court Address Sensors Algorithm Mechanism
- 21. Homework Read section 4.1.7 (pages 117 – 145) Questionnaire on CU Learn