Flying through cluttered and dynamic environments with LiDAR (2504.17569v1)

Abstract: 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.

Navigating UAVs through Cluttered and Dynamic Environments with LiDAR

The paper presents a LiDAR-based system designed to enhance the navigation capabilities of unmanned aerial vehicles (UAVs) in environments characterized by clutter and dynamic obstacles. It addresses core challenges these aircraft face, such as detecting fast-moving obstacles and sudden changes in surroundings. Recognizing the limitations posed by UAVs' onboard computing resources, the authors have engineered a solution integrating advanced perception and planning methodologies, tailored to execute with minimal latency and high accuracy.

The foundational elements of the proposed system include the M-detector for perception and the DynIPC for integrated planning and control. The M-detector enables UAVs to reliably identify moving objects, irrespective of their attributes like size or color, through efficient classification of point clouds. This capability is critical in real-time scenarios where perception accuracy cannot be compromised, especially in cluttered environments. Meanwhile, the DynIPC component extends previous integrated planning frameworks by incorporating predictive data about dynamic obstacles into the UAV's maneuver strategy, which is essential for proactive obstacle avoidance.

The researchers validate their solution through both simulated and real-world experiments. Numerical results highlight the system's superior performance compared to existing methodologies, such as Panther and Chen's framework, with respect to success rate, flight time, and obstacle avoidance efficacy. Notably, the UAVs managed successful navigation scenarios where other frameworks showed reduced adaptability, particularly in complex environments filled with both static and dynamic obstacles.

Practical implications of this research lie in its potential applications across industries where UAVs operate in dynamic settings, such as rescue missions, delivery logistics, or agricultural surveillance. The theoretical contributions are significant, as they demonstrate an enhanced integration of dynamic obstacle prediction within control algorithms, paving the way for future explorations in robust UAV autonomy.

Looking forward, extending this framework can include advancements in multi-sensor fusion to further enhance environmental perception and improve resilience against adverse climates or conditions that impair sensor data quality. Engaging with other software and hardware integrations, exploring alternative control paradigms like reinforcement learning to exploit real-time adaptation capabilities, and investigating scalability to swarms for collaborative tasks present rich avenues for exploration.

In conclusion, this paper provides a comprehensive and technically robust approach to overcoming UAV operational challenges in cluttered and dynamic environments using LiDAR. Through their developments, the researchers offer a blueprint for future innovations aimed at advancing UAV perceptions and control strategies, ensuring safer, more efficient, and adaptable aerial navigation.