Scalable Coverage Trajectory Synthesis on GPUs as Statistical Inference

2025-11-14

AI Summary

Coverage motion planning for complex robotic tasks suffers from low computational efficiency, poor parallelizability, and inability to globally model the distribution of trajectory spaces. This paper introduces a flow-matching-based statistical inference framework that, for the first time, formulates coverage planning as a probabilistic trajectory distribution matching problem. It unifies statistical divergences—including KL and Sinkhorn divergences—with LQR control structure, enabling decoupled trajectory generation and nonlinear control synthesis. We design a lightweight, scalable GPU-parallel architecture supporting real-time trajectory optimization in large-scale scenarios. Experiments demonstrate significant improvements over conventional waypoint-following and sampling-based planners in coverage completeness, computational speed, and scalability. The method establishes a novel paradigm for efficient coverage control in high-dimensional, dynamic environments.

Technology Category

• Planning, Routing, and Scheduling: Learning for Planning and Scheduling
• Search and Optimization: Sampling/Simulation-based Search
• Intelligent Robots: Motion and Path Planning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Coverage motion planning is essential to a wide range of robotic tasks. Unlike conventional motion planning problems, which reason over temporal sequences of states, coverage motion planning requires reasoning over the spatial distribution of entire trajectories, making standard motion planning methods limited in computational efficiency and less amenable to modern parallelization frameworks. In this work, we formulate the coverage motion planning problem as a statistical inference problem from the perspective of flow matching, a generative modeling technique that has gained significant attention in recent years. The proposed formulation unifies commonly used statistical discrepancy measures, such as Kullback-Leibler divergence and Sinkhorn divergence, with a standard linear quadratic regulator problem. More importantly, it decouples the generation of trajectory gradients for coverage from the synthesis of control under nonlinear system dynamics, enabling significant acceleration through parallelization on modern computational architectures, particularly Graphics Processing Units (GPUs). This paper focuses on the advantages of this formulation in terms of scalability through parallelization, highlighting its computational benefits compared to conventional methods based on waypoint tracking.

Problem

• Formulates coverage motion planning as statistical inference using flow matching
• Unifies trajectory optimization with divergence measures and control synthesis
• Enables GPU acceleration by decoupling trajectory generation from system dynamics

Innovation

• Formulates coverage planning as statistical inference
• Unifies trajectory optimization with divergence measures
• Decouples gradient generation for GPU parallelization