Efficient Trajectory Generation Based on Traversable Planes in 3D Complex Architectural Spaces

2025-03-11

AI Summary

Autonomous trajectory generation for ground robots navigating multilevel, complex building environments remains challenging due to geometric discontinuities and kinematic constraints across floors, stairs, and ramps. Method: This paper proposes a structured planning approach based on traversable planar graphs. First, traversable planes—including floors, stairs, and ramps—are extracted, classified, and topologically connected from 3D point clouds to construct a lightweight planar graph. Second, inter-level path planning is performed on this graph, followed by joint optimization incorporating kinematic feasibility and transition smoothness to generate high-quality trajectories. Contribution/Results: The method innovatively reduces 3D navigation to a graph-structured inter-planar planning problem and establishes, for the first time, an integrated framework unifying plane extraction, connectivity modeling, and trajectory optimization. Extensive evaluations in simulation and on the real-world CubeTrack platform demonstrate significant improvements in trajectory feasibility, smoothness, and cross-level robustness, enabling seamless navigation across stairs, ramps, and other complex architectural elements.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Multiagent Systems: Multiagent Planning

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• Generate efficient trajectories for ground robots
• Navigate complex multi-layered architectural spaces
• Optimize motion state trajectories with constraints

Innovation

• Extracts traversable planes from 3D point clouds
• Builds lightweight plane-graph for navigation
• Optimizes trajectories considering multi-layer constraints