researcher and PhD student Anirudha Majumdar set up a forest-like cluttered space using pvc pipes and strings. The drone they chose was a quadrotor, 3.5 inches wide, weighing an ounce and capable of doing a little more than 1 meter per second. The video shows how the drone was able to put up a fine air show by changing courses and flipping on the fly.
Anirudha Majumdar explained how complicated drone flight algorithms can get, by numbering the different directions a flying object might have to change its directions in order to avoid collision. Apparently, the algorithm needs to constantly analyze 12 parameters to determine the drone’s exact location in space and how fast it is moving, while monitoring the obstacles simultaneously.
Watch drones do donuts around obstacles thanks to planning algorithms | MIT CSAIL
Majumdar’s software, generates conservative plans, and can do so in real-time. He first developed a library of 40 to 50 trajectories that are each given an outer bound that the drone is guaranteed to remain within. These bounds can be visualized as ”funnels” that the planning algorithm chooses between to stitch together a sequence of steps that allow the drone to plan its flying on the fly.
A flexible approach like this comes with a high level of guarantees that the software will work, even in the face of uncertainties with both the surroundings and the hardware itself. The algorithm can easily be extended to drones of different sizes and payloads, as well as ground vehicles and walking robots.
As for the environment, imagine the drone choosing between making a forceful roll maneuver that will avoid a tree by a large margin, versus flying straight and avoiding a tree by a small amount.
“A traditional approach might prefer the first since avoiding obstacles by a significant amount seems ‘safer,’” Majumdar says. “But a move like that actually may be riskier because it’s more susceptible to wind gusts. Our method makes these decisions in real-time, which is critical if we want drones to move out of the labs and operate in real-world scenarios.”
Related/Background:
- In Press | Anirudha Majumdar
- Anirudha Majumdar and Russ Tedrake, “Funnel Libraries for Real-Time Robust Feedback Motion Planning,” In Preparation, 2016. ArXiv preprint: http://arxiv.org/abs/1601.04037
- Anirudha Majumdar and Russ Tedrake, “Robust Online Motion Planning with Regions of Finite Time Invariance,” Workshop on the Algorithmic Foundations of Robotics, 2012. [.pdf] x
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