Elephant in the cupboard

The register has good article called "Why do robot experts build such lousy robots?" which explorers some of the less talked about aspects of this "cusp of a revolution", such as why "making and playing with robots evidently beats using and ignoring them" and Thrun's "apparently simple challenge: how a computer vision system might detect moving objects and predict their motion, a task at which frogs are still in the lead".

Most of it is bang on, but there are some hopeful signs out there in machine learning (most of which fall into the overflowing "soon to be released" category.

Sentience stereo vision slam
braintech volts-iq *** ITS ALIVE! ***
SRI Slam as a webservice
Incremental of Linear Model trees PDF


[*** Update June 12th 2007] Volts-IQTM Community Technical Preview
is released. Look forward to checking it out...

How to build a COTS PC Robot - Part 1

This first part will deal with how to fit the physical and electrical parts together to make a pile of hardware that can be called a robot because it can move, not because it will actually be intelligent or autonomous.

At the end of this part, you will have a platform that with the help of part 2 (software / drivers) can be used as an expensive remote controlled 2 wheel car that can report on its sensors, and be in a position to have intelligence and automomy programmed into it. It will be similar in hardware composition to the Whitebox 914, but will have cost you $4000 less. It will of course be missing a beautiful shell and software to make it do anything. ( No disrespect to whitebox – the hardware part of this project was completed before the whitebox, but turns out to be very similar. Their product certainly has a beautiful shell and probably has some good software. If you want a finished system: check them out. )


What makes a robot?
For the purposes of this articles we will say that to be able to be called a robot it must:
1) Be able to move using wheels or legs
2) Be able to sense its surroundings, at least a bit.
3) Be able to respond to its senses and choose a move.

There are many ways to accomplish the above, and a $10 BEAM robot, may in many cases perform the above more reliably and with greater fluidity. This article will assume that you want a full PC as brain, either an existing desktop machine or a small PC that will move with the robot.

Advantages:
1) You can program with familiar languages.
2) You have access to large memory and storage.
3) You can use web cams and other commodity parts.
4) You can have access to wifi and the power of the network.

Disadvantages:
1) Complex system with many parts
2) Slow start up time.
3) Only as reliable as your software.
4) Expensive.

How do you make a PC move and sense?
These days there are many devices that can plug directly into a PC using serial, or USB and give you access to a vast range of actuators and sensors. Popular examples include, Phidgets, the Serializer and I2C devices. This article will concentrate on I2C devices from a UK company called Devantech mainly because it is what I have most experience with. Although I personally think they are a good option by price, performance and ease, many other choices are possible, and your own research many lead you in another direction.

Easy option: the RD01 Drive system from Devantech. £96.91
It contains the I2C controlled MD23 dual 3A motor controller, two gear motors with 360 step encoders, complete with wheels and mounting brackets. You could just about screw these directly into a laptop and you would nearly have a robot. We won’t be following that route.

How do you talk to it?
We will follow two possible routes:
1) The USBI2C adaptor from Devantech. £16.56
Plug one end into a USB port and plug some I2C wires into the other side. Done.
2) The RF04/CM02 from Devantech £79.88
Plug the RF04 into a desktop PC, and plug the CM02 via some I2C wires into your MD23. The RF04 will speak over radio waves to the CM02. This way you can use your desktop PC as the robot’s brain, and avoid you having to buy another computer for the robot. This will be much cheaper, but communication will have more latency and it won’t be as easy to stick a webcam on your robot.

Both of the above will let you speak to I2C devices through a virtual COM port. You simply send a few bytes to the COM port to tell the device to do something, or send another few bytes asking the device to send you something back. Higher level drivers exist which can do all of this low level communication for you and give you access to the devices via a web page or a service based architecture (more details in part 2).

Other sensors:
Once you have established access from your PC to the I2C bus, you can keep on adding sensors and actuators, up to about 122 devices in theory, and even more if you use several USB2I2C adaptors, or I2C bus splitters, but this is plenty already.

SD21 Servo Driver £21.99. This lets you control up to 21 servos, easily enough for a pan-tilt head, a couple of arms, a simple walker, or some other custom mechanisms that you may dream up.

If you are tight on budget and have chosen not to carry the PC brain around with you, you could even use a couple of continuous rotation servos (or hacked servos) as wheels.





PC
Beware, this is a fairly full PC that with added screen and speakers would make a fine home entertainment system. As the software will rely on XP, we have steered away from using compact flash as the hard disk. If you already have a CD drive lying around you could use that while installing the motherboard drivers and XP far enough to get network access, and use terminal services thereafter.

The exact part listing is not important. This list is for a mini-itx system, but an old laptop would be just as good. There are other variants with dual cores, pico-itx, or much lower power consumption – the choice is yours.



Motherboard / CPU: EPIA CN 13000G C7 Mini-ITX Board £109
Ram: 512MB PC4200 DDR2 533 DIMM £45
Hard Disk: 40GB Western Digital Scorpio 2.5" IDE 5400RPM Hard Drive - 8MB Cache £39
Slimline 24x CDROM Drive £25
Netgear 54Mpbs Wireless PCI Card £27
M2-ATX 160W Vehicle DC-DC Power Supply £59
Windows XP Pro £89



Power
This depends on the automomy you require. Obviously the more battery power you have, the longer it will be able to live without recharging, but that power will come at the cost of weight which will slow down your robot. You need 12V for the ATX, but you may also want 6V-7.2V for powering the servos, and 5V for some of the sensors. To avoid having another battery for the servos, we will use two 6V batteries in series, to produce a total of 12V, and we can take 6V from the middle for the servos. In the end, another two batteries and an extra 5V regulator was added to supply sufficient noise-free power to all the sensors.

Chassis
You can build the chassis out of just about anything, with wood and plastic being amoung the easiest. Basically all you need is a platform to put the pc parts on, with a sturdy frame on which to attach the wheels. Later on you may want to attach bumpers and a higher level for more sensors.

The chassis in the pictures was made out of aluminium profiles bought from a hardware store. It can be cut with a metal bladed hacksaw with a bit of patience and the sharp corners rounded off with a file. You will also need a small drill with some metal bits and a vice to hold things in place while sawing or drilling. Many designs are possible.

The pictures show the wheels in the middle which makes it a little easier to turn on the spot, but the castors at front and back could easily make it get stuck on a small edge of carpet. Many commercial designs sensibly use a circular frame and three wheels which is much less likely to get stuck on corners of furniture and can get over bumps without becoming grounded.




All pretty obvious stuff. The chassis and the wiring take a good deal of patience, so hopefully there will be commercially available cheap kits made by someone in the near future.

Is it finished?
No, not really. I need to make some much shorter USB cables to connect the web cam and the USBI2C. I'll be adding a second webcam for some stereo vision tests, and an arm or two to make use of all the spare SD21 channels. The servos go a bit jumpy while the PC starts, so I'll need to add a few capacitors or re-route power a bit. Also the plastic head is not pretty - I'll probably make a prettier metal head when the second cam arrives.

What software is it running?
XP with MSRS and Devantech Drivers

Is is autonomous?
No, not yet. It can be controlled wirelessly via a Joystick and web pages using MSRS and can report on all its sensors. Autonomy is planned: See: Grid SLAM Explorer

Can it actually do anything?
Ha! No, not really. It could push something around, I guess. It does telepresence and could warn me about a fire or intruder, or speak RSS feeds if I really wanted it to. For the moment though I'll be concentrating on mapping then give it an arm or two.

Humanoid Robotics

Last week I went to the JNRH (Journees Nationales de la Robotique Humanoide) at the LIRMM in Montpellier France where many of the French Humanoid robotics community presented their latest research. Many of the group are involved in the JRL (Joint Robotics Laboratory), a French - Japanese effort at making control software for the HRP-2 humanoid from Kawada and the HOAP-3 from Fujitsu (pictured).

From having seem videos of ASIMO and QRIO, I had thought that the basics of locomotion were quite well understood, but the reality is far from this. In fact most of the time the researchers work in simulation, dead scared of running their algorithms on their rare and expensive real robots. When they do work on the real robots, there is always someone holding a safety harness just in case. The videos you often see on the web are mostly pre-scripted actions resulting from extensive offline optimization. That is why the ASIMO fell down the stairs - unable to react in real time to the situation.

There was a quick demonstration of the HOAP-3 standing on one leg. Amusingly, it had to be powered down to get it to return to a natural position as they had only optimised the movement in one direction.

Many of the researchers are working on rather narrow aspects of control such as controlling the ZMP (Zero Moment Point) (Pierre-Brice Wieber PDF, landing after a jump, controlling contact forces (Christine Chevalereau PDF), kicking, moving a box by pivoting etc. The French approach seems to concentrate on finding theoretical perfection, which may be where an ultimate solution will be found if available. But do really humans work like this? Do they really pre-plan perfection, or do they learn by error and react to feedback?

The heavy theoretical approach contrasted somewhat with the work of Oussama Khatib from Stanford who, rather than pre-plan an optimal path, just applies an external force and as if by magic, his control system incrementally pulls the robot along a very natural path by exploiting the prime law of human effort, namely laziness. He demoed this in simulation running in real time, by moving around where he wanted the hand to be (primary task), the movement obeyed all of the constraints (joint limits, balancing, contact points, collision avoidance) and moved towards the goal while using any available redundancy to minimize effort, maintain body symmetry etc. (MPG) (PDF) You could see that some of the crowd where exasperated by the apparent simplicity - there were groans of 'if only we had the budget' etc. One of the problems he mentioned is that most humanoid platforms control movement and give feedback in terms of joint angle rather than angular effort which is needed for his laziness concept to work.

David Gouaillier from Aldebaran Robotics gave a presentation of their forthcoming Nao humanoid (not pictured)- a very pretty 23DOF humanoid with cam, speakers, microphones and wifi. (See minute 36 of MPG) It is likely to be able to do face tracking, play music, localize sounds and download new behaviors from the web. They will be making some hand-crafted models for sale in the near future and move to industrial production thereafter with an expected retail price of €2000-€3000.

Although they are open to making interfaces to several robot programming environments, their initial effort is centered around the URBI (Universal Real-time Behavior Interface) language from Gostai. In contrast to MSRS, URBI uses some very simple language constructs which let you do pretty much the same thing without explicitly using ports and interleaves.

A typical little behavior (for an aibo) might be:

whenever (sensorFront.val < threshold)
{

robot.stop();
neck.val = 10 time:450ms &

leg.val = -45 speed:7.5 &

tail.val = 14 sin:4s ampli:45;

};

'whenever' creates a persistent background task that checks for the condition and triggers the action. In this case the action stops the robot, and *simultaneously* moves the neck, leg and tail according to the time, speed and oscillation parameters supplied. Not bad for just a few lines.

Grid SLAM animation

This is an animation of the best aggregate map of 200 particles doing grid SLAM. It wiggles when another particle becomes the best aggregate particle. The map represents 10cm per pixel with a robot motion of 30cm per second. It is just dumbly wandering around, using a Vector Field Histogram to avoid walls - no purposeful entropy reduction.

It is using 24 simulated sonars modeled as fairly short range (200cm), narrow beam, medium motion noise and a beam probability model whose results are trusted to the power of 0.1 (i.e. not much, so as to encourage a smooth distribution without heavy particle duplication upon resampling - typically only 5%).

When the particles are moving into unknown territory, they diverge. When they see parts of the map a second time, they converge. As all of the particles' poses are overlaid on the current best particle's map, it can look as if they go through walls - if you were to see their own map, you would see that they think they are in the middle of the corridoor, and that the corridoor is in a different place.

You can see that the particle diversity is only just enough to close the loops.

Image Tracking

The last few weeks I've been playing with Image tracking algorythms. After searching around a bit I decided to start with Edard Rosten's FAST Corner Detector (PDF).

It seems to produce good results and has been used by Andrew Davison to do real time monocular SLAM (i.e. one web cam). Although Bob Motram has already done a great c# port of Davison's code (MonoSLAM), it relies on seeing a dark rectangle of known size before starting, which didn't seem appropriate for my usage on a robot and I wanted to get to know the algorythms at first hand.

Finding corners is pretty easy, the hard part is tracking them. The first phase is to find corners in the next frame that look similar, but this also generates a good deal of randomly placed matches.

The first method I attempted created a mean move from all the moving corners, then fine tunned by iteratively re-calculating a mean move by weighing moves based on the probability of being within a gaussian distribution of the last mean move. This has the effect of ignoring outliers a often converging on the true move. In retrospect, the initial mean should have been calculated according to weights based on how similar the corners looked, but it didn't seem to matter too much.

Although this worked OK-ish, unless the camera is on a perfect horizontal and all the objects in the scene are far away, the result of panning a camera really wants to be part of a sphere. My actual robot is likely to have a fixed camera, so I should have been content with only optimising the x displacement (rotation) and leaving it at that, but alas curiosity got the better of me, so I decided to try to look to higher dimensions. Bad move.

Really, there is no escaping it - full 3D is the only proper representation that can account for the movement in the pictures. Alas this isn't just an average or weighted average of pixel movement. It requires simultaneous gradient descent in all the dimensions of the model, and projection of corners into the real world. This involves some hard maths that I wasn't quite prepared for and am far from mastering...