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Algorithm for Robotic Sensing, Movement

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A heat map of UCLA'S Court of Sciences, as mapped by a quadcopter drone using new algorithms for improved speed and accuracy.

Credit: Verifiable and Control-Theoretic Robotics Laboratory/UCLA

The University of California, Los Angeles (UCLA)'s Kenny Chen, Brett Lopez, and Ryan Nemiroff formulated an algorithm that enhances autonomous robots' navigational abilities.

The researchers based the algorithm on direct LiDAR-inertial odometry and mapping (DLIOM) to produce "precise geometric maps of nearly any environment in real time using a compact sensor and computing suite," Chen explained.

He added that the program computes faster, maps more accurately, and is more reliable than other current solutions.

The researchers said DLIOM recalls previously visited locations, adapts to fluid environments, corrects hazy sensor imaging, and merges data collection and processing to accelerate robot performance.

Test flights of a DLIOM-outfitted quadcopter drone around UCLA's campus showed the aircraft functioned 20% faster and 12% more accurately than drones embedded with state-of-the-art algorithms.

From UCLA Samueli School of Engineering
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Abstracts Copyright © 2023 SmithBucklin, Washington, D.C., USA


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