How does one go about programming a drone to fly itself through the real world to a location without crashing into something? This is a tough problem, made even tougher if you’re pushing speeds higher and high. But any article with “MIT” implies the problems being engineered are not trivial.
The folks over at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have put their considerable skill set to work in tackling this problem. And what they’ve come up with is (not surprisingly) quite clever: they’re embracing uncertainty.
Why Is Autonomous Navigation So Hard?
Suppose we task ourselves with building a robot that can insert a key into the ignition switch of a motor vehicle and start the engine, and could do so in roughly the same time-frame that a human could do — let’s say 10 seconds. It may not be an easy robot to create, but we can all agree that it is very doable. With foreknowledge of the coordinate information of the vehicle’s ignition switch relative to our robotic arm, we can place the key in the switch with 100% accuracy. But what if we wanted our robot to succeed in any car with a standard ignition switch?
Now the location of the ignition switch will vary slightly (and not so slightly) for each model of car. That means we’re going to have to deal with this in real time and develop our coordinate system on the fly. This would not be too much of an issue if we could slow down a little. But keeping the process limited to 10 seconds is extremely difficult, perhaps impossible. At some point, the amount of environment information and computation becomes so large that the task becomes digitally unwieldy.
This problem is analogous to autonomous navigation. The environment is always changing, so we need sensors to constantly monitor the state of the drone and its immediate surroundings. If the obstacles become too great, it creates another problem that lies in computational abilities… there is just too much information to process. The only solution is to slow the drone down. NanoMap is a new modeling method that breaks the artificial speed limit normally imposed with on-the-fly environment mapping.
All autonomous drones have a speed limit, which is dependent on the amount of obstacles it must avoid. If the forward speed is too great or the amount of obstacles are too man it becomes impossible for the drone to keep track of them all. Once this line is breached, a probability of a collision is incurred. Traditionally, if you can’t reduce obstacles you must slow the drone down to await the calculations.
MIT CSAIL’s idea is to stop trying to keep track of every single obstacle. It accepts the fact that it cannot know exactly where it is… that there is a fundamental uncertainty of position that exists for the drone in space over a period of time. NanoMap accounts for this uncertainty and attempts to keep it as low as possible. This allows a drone to operate at a much higher speed in an obstacle-rich environment while keeping the probability of a collision relatively low.
Understanding Uncertainty Is The Key
NanoMap uses forward-looking depth sensors that put together an idea of its immediate environment, creating a local 3D data structure. It then uses an algorithm to search that structure. It searches back in time to find a view from its past that resembles its current view. Basically, it gathers just enough information to know that it’s in a “certain area”, and then plans its flight path accordingly. It doesn’t attempt to calculate its exact location and orientation as other models do. It only gets the data it needs not to run into something, and isn’t concerned with exact position and location. They’re calling this idea “pose uncertainty”.
Be sure to check out the white paper for full details, but we suggest blocking out some spare time. It’s a lot to wrap your brain around. If you can do that, the determined hacker or maker can give this a try themselves; the research team’s incredible work is open-source. Let us know in the comments if you plan to use this new and exciting technology in your next autonomous project!