Autonomous Soil Investigator. What Is the ASI? Designed to complete the 2013 IEEE student robotics challenge Collects "soil" samples from a simulated.

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Presentation transcript:

Autonomous Soil Investigator

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

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

Localization Position and Orientation Awareness

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

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

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

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

Pathfinders Probabilistic pathfinding Planned Pathing vs.

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

Quick to solutions Location specific conditions Trick solutions Custom GUI Planned Pathing

Collection Target Acquisition

OpenCV - Computer Vision tool library Used to precisely locate samples

Inverse Kinematics

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

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

Architecture Project Structure and Optimization Collaboration Optimization Operation Methodology

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

Methodology (continued) Polymorphism Transitions Tailored behaviors Init Abstract Mode CollectLocalize Pathfinding...

Collaboration Github o Distributed workflow o Quality o Recovery

TEAM IMAGE