New algorithm to help robots avoid bumping into objects

Scientists, including one of Indian origin, have developed a new system that can help robots avoid colliding with other moving objects and weave through complex, rapidly changing environments in real time.
The algorithm "Fastron" - developed by researchers at the University of California San Diego in the US - runs up to eight times faster than existing collision detection algorithms.
Fastron will be useful for robots that operate in human environments where they must be able to work with moving objects and people fluidly.
One application they are exploring in particular is robot-assisted surgeries using the da Vinci Surgical System, in which a robotic arm would autonomously perform assistive tasks (suction, irrigation or pulling tissue back) without getting in the way of the surgeon-controlled arms or the patient's organs.

"This algorithm could help a robot assistant cooperate in surgery in a safe way," said Michael Yip, a professor at UC San Diego.
The team also envisions that Fastron can be used for robots that work at home for assisted living applications, as well as for computer graphics for the gaming and movie industry, where collision checking is often a bottleneck for most algorithms.

A problem with existing collision detection algorithms is that they are very computation-heavy.
They spend a lot of time specifying all the points in a given space - the specific 3D geometries of the robot and obstacles - and performing collision checks on every single point to determine whether two bodies are intersecting at any given time.

The computation gets even more demanding when obstacles are moving.
To lighten the computational load, researchers developed a minimalistic approach to collision detection.
The result was Fastron, an algorithm that uses machine learning strategies - which are traditionally used to classify objects - to classify collisions versus non-collisions in dynamic environments.
"We actually don't need to know all the specific geometries and points. All we need to know is whether the robot's current position is in collision or not," said Nikhil Das, a PhD student in Yip's group.
Fastron updates its classification boundaries very quickly to accommodate for moving scenes, something that has been challenging for the machine learning systems.