基于时空高斯过程卡尔曼滤波的多机器人学习型信息路径规划

中文摘要 AI 辅助

面向持续目标监测的多机器人信息路径规划（IPP）要求机器人考虑空间不确定性、时间演化以及实际的感知和通信约束。近期基于学习的多机器人IPP方法使用高斯过程（GPs）来建模目标不确定性，但往往依赖简化的感知模型和集中式信念更新。我们提出了一种基于网格的时空GP-卡尔曼滤波框架，用于基于学习的多机器人IPP。我们不在每个目标上维护一个GP，而是将匿名目标存在表示为离散工作空间网格上的单一潜在场。所提出的递归更新考虑相机足迹内的所有可见单元，并支持任意视场和与距离相关的噪声。一种GP一致的时间过程更新通过随时间膨胀不确定性来处理移动目标和过时信息。对于分散式部署，每个机器人维护自己的映射器，并交换紧凑的信念摘要而非原始测量值。接收到的信念使用对角协方差交集进行融合，以在未知的机器人间相关性下保持保守性。我们将映射器与用于基于图的邻居选择的强化学习策略集成。仿真基准测试显示，与基于学习的和经典的拍卖/覆盖基线相比，平均目标不确定性降低约20%，目标访问率有所提高。真实世界的双无人机实验证明了在超过7000平方米的大面积户外多机器人搜索中的迁移能力。

英文摘要

Multi-robot informative path planning (IPP) for persistent target monitoring requires robots to reason about spatial uncertainty, temporal evolution, and practical sensing and communication constraints. Recent learning-based multi-robot IPP methods use Gaussian Processes (GPs) for target uncertainty, but often rely on simplified sensing models and centralized belief updates. We propose a grid-based spatio-temporal GP-Kalman filtering framework for learning-based multi-robot IPP. Instead of maintaining one GP per target, we represent anonymous target presence as a single latent field over a discrete workspace grid. The proposed recursive update considers all visible cells inside a camera footprint and supports arbitrary fields of view and range-dependent noise. A GP-consistent temporal process update accounts for moving targets and stale information by inflating uncertainty over time. For decentralized deployment, each robot maintains its own mapper and exchanges compact belief summaries rather than raw measurements. Received beliefs are fused using diagonal covariance intersection to remain conservative under unknown inter-robot correlations. We integrate the mapper with a reinforcement-learning policy for graph-based neighbor selection. Simulation benchmarks show about 20% lower average target uncertainty and improved target visitation compared with learning-based and classical auction/coverage baselines. Real-world two-UAV experiments demonstrate transfer to outdoor multi-robot search over a large field of more than 7000 square meters.

发表机构

• Carnegie Mellon University（卡内基梅隆大学）
• Singapore University of Technology and Design（新加坡科技设计大学）
• Daegu Gyeongbuk Institute of Science and Technology（大邱庆北科学技术院）

机构由 AI 辅助整理，请以论文原文为准。