At this point my research has led me to ROS modules RTAB-Map which will create a three dimensional representation of a robot’s environment. It seems very promising… but building such a representation is only half the battle. How would a robot make good use of this data? My research has not yet uncovered applicable solutions.

The easy thing to do is to fall back to two dimensions, which will allow the use of standard ROS navigation stack. The RTAB-Map ROS module appears to make the super easy, with the option to output a two dimension occupancy grid just like what navigation wants. It is a baseline for handling indoor environments, navigating from room to room and such.

But where’s the fun in that? I could already do that with a strictly two-dimensional Phoebe. Sawppy is a six wheel rover for handling rougher terrain and it would be far preferable to make Sawppy autonomous with ROS modules that can understand and navigate outdoor environments. But doing so successfully will require solving a lot of related problems that I don’t have answers yet.

We can see a few challenges in the picture of Sawppy in a back yard environment:

• Grass is a rough surface that would be very noisy to robot sensors due to individual blades of grass. With its six wheel drivetrain, Sawppy can almost treat grassy terrain as flat ground. But not quite! There are hidden dangers – like sprinkler heads – which could hamper movement and should be considered in path planning.
• In the lower right corner we can see transition from grass to flat red brick. This would show as a transition to robot sensors as well, but deciding whether that transition is important will need to be part of path planning. It even introduces a new consideration in the form of direction: Sawppy has no problem dropping from grass to brick, but it takes effort to climb from brick back on to grass. This asymmetry in cost would need to be accounted for.
• In the upper left corner we see a row of short bricks. An autonomous Sawppy would need to evaluate those short bricks and decide if they could be climbed, or if they are obstacles to be avoided. Experimentally I have found that they are obstacles, but how would Sawppy know that? Or more interestingly: how would Sawppy perform its own experiment autonomously?

So many interesting problems, so little time!

(Cross-posted to Hackaday.io)

One of the ways a TurtleBot makes ROS easier and more approachable for beginners is by simplifying a robot’s world into two dimensions. It’s somewhat like the introductory chapters of a physics textbook, where all surfaces are friction-less and all collisions are perfectly inelastic. The world of a TurtleBot is perfectly flat and all obstacles have an infinite height. This simplification allows the robot’s environment to be represented as a 2D array called an occupancy grid.

Of course, the real world is more complicated. My TurtleBot clone Phoebe encountered several problems just trying to navigate my home. The real world do not have flat floors and obstacles come in all shapes, sizes, and heights. Fortunately, researchers have been working on problems encountered by robots venturing outside the simplified world, it’s a matter of reading research papers and following their citation links to find the tools.

One area of research improves upon the 2D occupancy grid by building data structures that can represent a robot’s environment in 3D. I’ve found several papers that built upon the octree concept, so that seems to be a good place to start.

But for a robot to build a representation of its environment in 3D, it needs 3D sensors. Phoebe’s Neato vacuum LIDAR works in a simplified 2D world but won’t cut it anymore in a 3D world. The most affordable entry point here is the Microsoft Kinect sensor bar from an old Xbox 360, which can function as a RGBD (red + blue + green + depth) input source for ROS.

Phoebe used Gmapping for SLAM, but that takes 2D laser scan data and generates a 2D occupancy grid. Searching for a 3D SLAM algorithm that can digest RGBD camera data, I searched for “RGBD SLAM” that led immediately to this straightforwardly named package. But of course, that’s not the only one around. I’ve also come across RTAB-Map which seems to be better maintained and updated for recent ROS releases. And best of all, RTAB-Map has the ability to generate odometry data purely from the RGBD input stream, which might allow me to bypass the challenges of calculating Sawppy’s chassis odometry from unreliable servo angle readings.

(Cross-posted to Hackaday.io)

A great side effect of giving a presentation is that it requires me to gather my thoughts in order to present them to others. Since members of RSSC are familar with ROS, I collected my scattered thoughts on ROS over the past few weeks and condensed the essence into a single slide that I’ve added to my presentation.

From building and running my Phoebe robot, I learned about the basics of ROS using a two-wheeled robot on flat terrain. Sticking to 2D simplifies a lot of robotics problems and I thought it would help me expand to a six-wheeled rover to rough terrain. Well, I was right on the former but the latter is still a big step to climb.

The bare basic responsibilities of a ROS TurtleBot chassis (and derivatives like Phoebe) is twofold: subscribe to topic /cmd_vel and execute movement commands published to that topic, and from the resulting movement, calculate and publish odometry data to topic /odom.

Executing commands sent to /cmd_vel is relatively straightforward when Sawppy is on level ground. It would not terribly different from existing code. The challenge comes from uneven terrain with unpredictable traction. Some of Sawppy’s wheels might slip and resulting motion might be very different from what was commanded. My experience with Phoebe showed that while it is possible to correct for minor drift, major sudden unexpected shifts in position or orientation (such as when Phoebe runs into an unseen obstacle) throws everything out of whack.

Given the potential for wheel slip on uneven terrain, calculating Sawppy odometry is a challenge. And that’s before we consider another problem: the inexpensive serial bus servos I use do not have fine pitched rotation encoders, just a sensor for servo angle that only covers ~240 of 360 degrees. While I would be happy if it just didn’t return any data when out of range, it actually returns random data. I haven’t yet figured out a good way to filter the signal out of the noise, which would be required to calculate odometry.

And these are just challenges within the chassis I built, there’s more out there!

(Cross-posted to Hackaday.io)