Efficient Trajectory Generation Based on Traversable Planes in 3D Complex Architectural Spaces
Abstract
With the increasing integration of robots into human life, their role in architectural spaces where people spend most of their time has become more prominent. While motion capabilities and accurate localization for automated robots have rapidly developed, the challenge remains to generate efficient, smooth, comprehensive, and high-quality trajectories in these areas. In this paper, we propose a novel efficient planner for ground robots to autonomously navigate in large complex multi-layered architectural spaces. Considering that traversable regions typically include ground, slopes, and stairs, which are planar or nearly planar structures, we simplify the problem to navigation within and between complex intersecting planes. We first extract traversable planes from 3D point clouds through segmenting, merging, classifying, and connecting to build a plane-graph, which is lightweight but fully represents the traversable regions. We then build a trajectory optimization based on motion state trajectory and fully consider special constraints when crossing multi-layer planes to maximize the robot’s maneuverability. We conduct experiments in simulated environments and test on a CubeTrack robot in real-world scenarios, validating the method’s effectiveness and practicality.
Chao Xu 许超
Full Professor
My research interests include Geometries and Control of Mechanical Systems, Kinematic Agents and Cybernetics, Multi-Physics Driven Robotics, AI-Driven Science.
Fei Gao 高飞
Associate Professor
My research interests include aerial robotics, autonomous navigation, swarm cooperation, and embodied intelligence.
Yanjun Cao 曹燕军
Research Professor
My research interests focuse on key challenges in multi-robot systems, such as collaborative localization, perception, communication, and system organization.