24 January 2014

Team Waterloo Research Paper on SRR

Team Waterloo published about their work on a robot for the 2012 and 2013 NASA Sample Return Robot Centennial Challenges.
Mapping, Planning, and Sample Detection Strategies for Autonomous Exploration
This paper presents algorithmic advances and field trial results for autonomous exploration and proposes a solution to perform simultaneous localization and mapping (SLAM), complete coverage, and object detection without relying on GPS or magnetometer data. We demonstrate an integrated approach to the exploration problem, and we make specific contributions in terms of mapping, planning, and sample detection strategies that run in real-time on our custom platform. Field tests demonstrate reliable performance for each of these three main components of the system individually, and high-fidelity simulation based on recorded data playback demonstrates the viability of the complete solution as applied to the 2013 NASA Sample Return Robot Challenge.
 It is the Journal of Field Robotics in the Wiley Online Library. I could, and probably will, spend a lot of time with the back issues in the Journal.

14 January 2014

Accelerating SRR Development While Gyrating Wildly

Decoding the title, I am experimenting with the Phidgets 1042 spatial sensor, also known as an Inertial Measurement Unit (IMU). This IMU contains an accelerometer, a gyroscope, and a compass. The compass is not allowed for competing in the SRR so it is being ignored.

I worked with this IMU for the 2013 SRR but could not get the results needed so I put it aside. Since the first of the year and getting more serious about the 2014 SRR, I began working with it more.

As I did last year, I began working with code I found that would fuse the accelerometer and gyroscope data into a single reading of the global pose of the robot. The results never came out correct. The main problem was the reading for bearing, primarily based on the gyroscope data, was inaccurate. I setup a servo to rotate the IMU through 90 degrees (or a fairly close approximation) but the results usually were less, somewhere in the mid-80 degree range.

After fussing with the code I decided to try a really basic test. First, some background information.

A gyroscope of this nature reports the angular change during a period of time. Specifically, this IMU reports degrees / second and the amount of time between readings. Multiplying the reading by the amount of time tells you the actual rotational movement for that period. Integrating those results provides the current angular position of the IMU. Thus:
\[
\large \theta_{t} = \sum \omega_{t}dt \\
\theta_{t} \text{ is angular movement at time } t  \]
The test is simple. Setup the servo to rotate 90 degrees with the IMU flat. The z axis, up and down, of the gyro should have a rotation of 90 degrees following the equation above.