无人机与机器人协作导航，用智能压缩地图信息降低通信量。
Collaborative Navigation and Exploration with $β$-Sparse Gaussian Processes
- 用β-稀疏高斯过程动态选传地图点，兼顾精度与带宽限制。
- 相比无通信，路径成本降低18%；相比原始数据传输，通信量减少76%。
- 适合资源受限的异构机器人协同探索任务，如火星探测。
在未知环境中，异构机器人协同导航面临感知、通信和计算能力的限制。本文提出一种框架：由领航机器人向目标前进，移动传感器机器人（如无人机）在带宽约束下传输其局部观测地图。传感器机器人在线联合选择发送的地图点与自身导航动作，同时预测未探索区域。为此，我们提出β-稀疏高斯过程，一种在基数约束下实现任务感知诱导点选择的鲁棒变分稀疏高斯过程模型。此外，设计了一种平衡任务相关性与探索性的动作选择策略。在火星与地球地图上的仿真结果表明，该框架相较无通信方案可降低18%路径成本，相较于原始数据传输基线减少76%传输信息量。
原文摘要 · Abstract (English)
Collaborative navigation of heterogeneous robots in unknown environments poses significant challenges due to sensing, communication, and computational limitations. In this work, a lead robot navigates toward a target while a mobile sensor robot (e.g., a drone) assists by transmitting information about its locally observed map under bandwidth constraints. We propose a framework that enables the sensor to jointly select its transmitted map points and navigation actions online, while also predicting unexplored regions of the environment. To this end, we present $β$-Sparse Gaussian Processes, a robust variational sparse Gaussian Process model for task-aware inducing point selection under cardinality constraints. Furthermore, we develop an action-selection strategy that balances task relevance with exploration. Simulations on Mars and Earth maps show that the framework can reduce path cost by 18% relative to no communication and decrease transmitted information by 76% compared to raw-data transmission baselines.
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