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A model for autonomous navigation and obstacle avoidance in UAVs

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Autonomous unmanned aerial vehicles (UAVs) have shown great potential for a wide range of applications, including automated package delivery and the monitoring of large geographical areas. To complete missions in real-world environments, however, UAVs need to be able to navigate efficiently and avoid obstacles in their surroundings.

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New perception metric balances reaction time, accuracy

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Researchers at Carnegie Mellon University have developed a new metric for evaluating how well self-driving cars respond to changing road conditions and traffic, making it possible for the first time to compare perception systems for both accuracy and reaction time.

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