Voxel-Based Hierarchical Approximate Convex Decomposition for Efficient 3D Representation of Objects in Robotic Applications

Approximate Convex Decomposition (ACD) is essential for industrial robotics, enabling efficient collision detection, motion planning, and physics-based simulation of robotic manipulators. However, traditional ACD methods, such as Hierarchical ACD (HACD) and Volumetric HACD (V-HACD), often suffer from high computational costs and over-segmentation, making them unsuitable for real-time robotic applications. This paper presents a novel voxel-based HACD (VX-HACD) approach designed to enhance computational efficiency while preserving the geometric fidelity of robotic manipulator components. The proposed approach first converts the input mesh into a structured voxel grid, simplifying the convex decomposition process. A gap-filling algorithm ensures topological continuity, preventing segmentation artifacts caused by voxel discretization. Additionally, a hierarchical voxel aggregation strategy reduces the number of convex components while maintaining accuracy, optimizing the representation for robotic applications. The methodology is validated on high-complexity robotic manipulator components, demonstrating reduced processing times, lower volumetric error, and fewer convex components compared to state-of-the-art ACD techniques. The proposed approach, while validated in the context of industrial robotics for collision-aware motion planning, can be applied to a wide range of applications requiring efficient convex decomposition in high-performance simulation (e.g., precision or surgical robotics, video games, and physics simulation).