New algorithm lets autonomous robots divvy up assembly tasks on the fly
Today’s industrial robots are remarkably efficient — as long as they’re in a controlled environment where everything is exactly where they expect it to be.
But put them in an unfamiliar setting, where they have to think for themselves, and their efficiency plummets. And the difficulty of on-the-fly motion planning increases exponentially with the number of robots involved. For even a simple collaborative task, a team of, say, three autonomous robots might have to think for several hours to come up with a plan of attack.
This week, at the Institute for Electrical and Electronics Engineers’ International Conference on Robotics and Automation, a group of MIT researchers were nominated for two best-paper awards for a new algorithm that can significantly reduce robot teams’ planning time. The plan the algorithm produces may not be perfectly efficient, but in many cases, the savings in planning time will more than offset the added execution time.
The researchers also tested the viability of their algorithm by using it to guide a crew of three robots in the assembly of a chair.
“We’re really excited about the idea of using robots in more extensive ways in manufacturing,” says Daniela Rus, the Andrew and Erna Viterbi Professor in MIT’s Department of Electrical Engineering and Computer Science, whose group developed the new algorithm. “For this, we need robots that can figure things out for themselves more than current robots do. We see this algorithm as a step in that direction.”
The problem the researchers address is one in which a group of robots must perform an assembly operation that has a series of discrete steps, some of which require multirobot collaboration. At the outset, none of the robots knows which parts of the operation it will be assigned: Everything’s determined on the fly.
Computationally, the problem is already complex enough, given that at any stage of the operation, any of the robots could perform any of the actions, and during the collaborative phases, they have to avoid colliding with each other. But what makes planning really time-consuming is determining the optimal way for each robot to grasp each object it’s manipulating, so that it can successfully complete not only the immediate task, but also those that follow it.
“Sometimes, the grasp configuration may be valid for the current step but problematic for the next step because another robot or sensor is needed,” Rus says. “The current grasping formation may not allow room for a new robot or sensor to join the team. So our solution considers a multiple-step assembly operation and optimizes how the robots place themselves in a way that takes into account the entire process, not just the current step.”
The key to the researchers’ algorithm is that it defers its most difficult decisions about grasp position until it’s made all the easier ones. That way, it can be interrupted at any time, and it will still have a workable assembly plan. If it hasn’t had time to compute the optimal solution, the robots may on occasion have to drop and regrasp the objects they’re holding. But in many cases, the extra time that takes will be trivial compared to the time required to compute a comprehensive solution.
Read more: Helping robots put it all together
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