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

• Localizationo Hypobotso Sensor Input

• Traversalo Pathfindingo Wheels

• Collectiono Armao 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 Pathingvs.

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

• Featureso Dual Core, 1.2 GHz ARM Processoro Ubuntu 12.04 Linux Nativeo USB Host Controller

• Purpose: High Level Computingo Localization Algorithmso Pathfinding Algorithmso Computer Vision

Hardware -- Microcontroller

• Featureso Rapid prototypingo Common Tools

• Purpose: Lower Deck Computingo Ackerman geometryo PWM for servo & motor controlo 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