Researchers from Carnegie Mellon University have developed a new technique that could lead to faster and more efficient drone exploration.
By creating both low-resolution and high-resolution maps, a new visualization strategy enables autonomous robots to explore their surroundings thoroughly. During simulations and real-world experiments with single robots, this method was 80% more efficient and had a 50% lower computational run time than existing strategies. These improvements were also evident with multi-robot teams and ground and aerial robots.
Because they typically resort to generating high-resolution maps, the techniques consume computational power and time. Here, Chao Cao and colleagues designed a two-pronged visualization framework designed to optimize how single robots and robot teams accomplish missions. One step generates a high-resolution map for detailed analyses of robots’ immediate surroundings, which is useful for gathering fine-scale details of the local environment.
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