Xuhui Chen

Jiayi Kong, Chen Zong, Junkai Deng et al.

Unsigned Distance Fields (UDFs) provide a flexible representation for 3D shapes with arbitrary topology, including open and closed surfaces, orientable and non-orientable geometries, and non-manifold structures. While recent neural approaches have shown promise in learning UDFs, they often suffer from numerical instability, high computational cost, and limited controllability. We present a lightweight, network-free method, Voronoi-Assisted Diffusion (VAD), for computing UDFs directly from unoriented point clouds. Our approach begins by assigning bi-directional normals to input points, guided by two Voronoi-based geometric criteria encoded in an energy function for optimal alignment. The aligned normals are then diffused to form an approximate UDF gradient field, which is subsequently integrated to recover the final UDF. Experiments demonstrate that VAD robustly handles watertight and open surfaces, as well as complex non-manifold and non-orientable geometries, while remaining computationally efficient and stable.

Yanze Liu, Xuhui Chen, Yanhai Du et al.

Recently unmanned aerial vehicles (UAV) have been widely deployed in various real-world scenarios such as disaster rescue and package delivery. Many of these working environments are unstructured with uncertain and dynamic obstacles. UAV collision frequently happens. An UAV with high agility is highly desired to adjust its motions to adapt to these environmental dynamics. However, UAV agility is restricted by its battery power output; particularly, an UAV's power system cannot be aware of its actual power need in motion planning while the need is dynamically changing as the environment and UAV condition vary. It is difficult to accurately and timely align the power supply with power needs in motion plannings. This mismatching will lead to an insufficient power supply to an UAV and cause delayed motion adjustments, largely increasing the risk of collisions with obstacles and therefore undermine UAV agility. To improve UAV agility, a novel intelligent power solution, Agility-Enhanced Power Supply (AEPS), was developed to proactively prepare appropriate amount powers at the right timing to support motion planning with enhanced agility. This method builds a bridge between the physical power system and UAV planning. With agility-enhanced motion planning, the safety of UAV in complex working environment will be enhanced. To evaluate AEPS effectiveness, missions of "patrol missions for community security" with unexpected obstacles were adopted; the power supply is realized by hybrid integration of fuel cell, battery, and capacitor. The effectiveness of AEPS in improving UAV agility was validated by the successful and timely power supply, improved task success rate and system safety, and reduced mission duration.