1 / 21

Autonomous Soil Investigator (ASI): Enhancing Robotic Exploration

The ASI collects soil samples in designated areas using advanced robotics technology. Designed for the IEEE student robotics challenge, it features precise localization, sensor input, pathfinding capabilities, and cutting-edge hardware. Localization is achieved through Hypobots and a Particle Filter system for accurate positioning. Lessons learned focus on working with the robot's perception for robustness. Pathfinding includes probabilistic and planned methods for efficient traversal. The hardware components, such as the Panda Board and Microcontroller, enable high-level computing, localization algorithms, and computer vision capabilities. Collaboration and optimization methodologies are key aspects of this project structure.

brier
Download Presentation

Autonomous Soil Investigator (ASI): Enhancing Robotic Exploration

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Autonomous Soil Investigator

  2. What Is the ASI? • Collects "soil" samples from a simulated forest environment • Designed to complete the 2013 IEEE student robotics challenge

  3. The ASI Solution • Localization • Hypobots • Sensor Input • Traversal • Pathfinding • Wheels • Collection • Arma • OpenCV / Inverse Kinematics

  4. Localization Position and Orientation Awareness

  5. Localization sensors: Four eye modules, each with • Infrared rangefinder • Ultrasonic rangefinder • Sweeping Servo Motor

  6. How was Localization implemented Particle Filter: • Uses a cloud of discrete hypotheses (Hypobots) of the robot's position • Cloud mimics robot's intended motions • Each time a measurement is performed, each hypothesis is weighted

  7. Localization lessons learned: • Work with the robot's perception, rather than your own • Robustness is more important than efficiency

  8. Pathfinder Planning and Routing New Data: • Localization • IMU • Pucks Static Data: • Graph Data

  9. Pathfinders Probabilistic pathfinding Planned Pathing vs.

  10. Probabilistic pathfinding • Slow to navigate in X, Y, Theta space • Cannot find tricky solutions • Paths are often not optimal • Non-Voronoi solutions

  11. Planned Pathing • Quick to solutions • Location specific conditions • Trick solutions • Custom GUI

  12. Collection Target Acquisition

  13. OpenCV - Computer Vision tool library Used to precisely locate samples

  14. Inverse Kinematics

  15. Hardware -- Panda Board • Features • Dual Core, 1.2 GHz ARM Processor • Ubuntu 12.04 Linux Native • USB Host Controller • Purpose: High Level Computing • Localization Algorithms • Pathfinding Algorithms • Computer Vision

  16. Hardware -- Microcontroller • Features • Rapid prototyping • Common Tools • Purpose: Lower Deck Computing • Ackerman geometry • PWM for servo & motor control • ADC for infrared • Sonar sensor interfacing

  17. Architecture Project Structure and Optimization Operation Methodology Collaboration Optimization

  18. Methodology Panda Board Python • Operations per mode • Binds modules • Messenger communication central • Stages MODE MESSENGER BASE Arduino C/C++ CONTROL

  19. Methodology (continued) • Polymorphism • Transitions • Tailored behaviors Init Localize Pathfinding Collect . . . Abstract Mode

  20. Collaboration • Github • Distributed workflow • Quality • Recovery

  21. TEAM IMAGE

More Related