VCC: Efficient Voxel-Based Collision Checking Framework for Real-Time Robotic Motion Planning

Abstract

To navigate the environment with dynamic obstacles, a robot must continuously scan for them and find collision-free paths to reach a goal position. This process starts with receiving obstacle information in the form of a point cloud, followed by a pre-planning stage that involves preprocessing to remove unnecessary points and constructing an environment data structure. However, the pre-planning stage can consume more than 16times the runtime of the planning stage, slowing the robot’s reaction speed. Thus, in this work, we propose textit{VCC}, an efficient collision checking framework that primarily targets the pre-planning bottleneck. VCC first cleans the point cloud using Center-selective Voxel Filtering. It then divides the environment into voxels using Adaptive Workspace Voxelization and organizes them in a Multilevel Voxel Table (MVT). In addition, VCC manages the MVT in two memory pools to ensure high data locality and SIMD-aligned data layout. During motion planning, the planner can perform low-latency SIMD-accelerated collision checking using the MVT. Compared to the state-of-the-art method, the experimental results show a 3.63times speedup in filtering. In terms of environment data structure, MVT achieves a 220.48times speedup during construction and reduces memory usage by 97.73%. Additionally, VCC accelerates sampling-based planning by 1.94times. Altogether, VCC achieves an end-to-end speedup of 7.71times on the desktop CPU platform and 4.23times on the embedded computer platform, making real-time motion planning practical for resource-constrained edge devices.