Team Robo Monster™

Wednesday, June 01, 2005

The 'sensor dense' approach

Our team is pursuing a 'sensor dense' approach inspired by biology. This approach is not just adding a lot of sensors to the vehicle - it is a particular design philosophy inspired by biology.

Here are some of its features:

1. Use lots of simple sensors, instead of a few complex ones.
2. Use a variety of sensor types
3. Organize the sensors into an electronic 'skin'
4. If you aren't getting enough information, throw more sensors at the system.
5. Don't throw away simple sensors if you add complex ones.
6. Use overlapping, redundant sensor networks
7. Connect a lot of sensors to a smaller number of microprocessors. Connect these to a smaller number of computers integrating data from several microprocessors. Connect these to a still smaller number of computers.
8. Use ultra-simple arbitration - no advanced "AI"
9. The environment is modeled via a "body-centered" coordinate system

Our motivation for this design is biology, though we are not trying to duplicate the details of biological structure (e.g. no neural nets). Instead, we are trying to duplicate the ratio of sensors versus "thinking" neurons versus body size found in simple animals.

In our opinion, the smartest robots today are comparable to a jellyfish or at best a mollusk in their computational complexity. So we look at how these systems organize senses and brains - and see a "sensor dense" approach in action. Lots of sensors compensate for a small brain, rather than a complex brain enabling lots of sensors!

Case in point - the Bay Scallop. This creature is essentially a clam that decided to swim - in our minds like a car that decides to drive itself. What do we find? An extremely simple "brain" (actually three ganglia or sub-brains) plus LOTS of sensors. Scallops have upwards of 60 eyes with lenses. They don't form perfect images, but give an idea of general direction and motion of critters around the scallop.

Instead of a scallop eye tracking an object, each eye in turn fires as an object moves past the scallop - an alternate, sensor-dense way of registering motion.

Case in point - the Box Jelly. These jellyfish are unlike others in that they have eyes (24-40 of them) with lenses which focus images. Box Jellies can swim to shelter, food, and other box jellies. Close examination of their eyes (recently reported in Nature at this link) demonstrate the "sensor dense" strategy at work. The animal has several stalks, with 6 eyes on each stalk. One eye per stalk is large, and forms blurry images. A second eye on each stalk is smaller, but has an iris on it to adjust for ambient light. 4 additional eyes are simple, non-focusing light sensors.

Note the following features comparable to the "sensor dense" approach:
1. Several different kinds of light sensors are used
2. Simple light sensors were not "thrown away" when the more complex eyes evolved - instead they continue to function in the 6-eye system.
3. Simple arbitration - like other jellyfish, box jellies have no brain at all. Instead, they have a network of nerve cells distributed evenly over their body. In the light, the multiple eyes make up for the lack of a central brain.

But this is different from our usual assumptions about robotics. It is usually assumed that you should pair lots of computers with lots of sensors. If you have limited computing power, you should limit sensors. But this clearly isn't what biology does - in fact it appears to do the opposite. We find small numbers of eyes in the most advanced animals, e.g. ourselves.

Our equivalent to the Box Jelly in Robo Monster is use of a variety of "eyes":

1. Light sensors scattered over all the body panels of the robot, detecting ambient light and recording absolute lux levels.
2. Low-resolution (160x120) webcams detecting motion and "optic" flow in all directions
3. A few high-resolution stereo cameras.
4. Repeat.

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