Navigating unmanned aerial vehicles (UAVs) through cluttered and dynamic
environments remains a significant challenge, particularly when dealing with
fast-moving or sudden-appearing obstacles. This paper introduces a complete
LiDAR-based system designed to enable UAVs to avoid various moving obstacles in
complex environments. Benefiting the high computational efficiency of
perception and planning, the system can operate in real time using onboard
computing resources with low latency. For dynamic environment perception, we
have integrated our previous work, M-detector, into the system. M-detector
ensures that moving objects of different sizes, colors, and types are reliably
detected. For dynamic environment planning, we incorporate dynamic object
predictions into the integrated planning and control (IPC) framework, namely
DynIPC. This integration allows the UAV to utilize predictions about dynamic
obstacles to effectively evade them. We validate our proposed system through
both simulations and real-world experiments. In simulation tests, our system
outperforms state-of-the-art baselines across several metrics, including
success rate, time consumption, average flight time, and maximum velocity. In
real-world trials, our system successfully navigates through forests, avoiding
moving obstacles along its path.

Este artículo explora los viajes en el tiempo y sus implicaciones.

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