采用未校准传感器的概率多机器人气源定位：一种分布式估计方法
Probabilistic Multi-Robot Gas Source Localization with Uncalibrated Sensors: A Distributed Estimation Approach
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中文总结 AI 辅助
本文针对多机器人异构传感器未校准的气源定位问题，提出分布式概率框架，结合秩特征与专家乘积融合，并引入信息区域分配策略，经仿真验证其性能优于基准方法。
中文摘要 AI 辅助
当多机器人系统配备未校准的异构传感器时，其非线性且不一致的响应会阻碍可靠的信息融合，利用该系统估计环境状态会变得极具挑战性。本文针对气源定位任务提出了一种分布式概率框架，该框架可在传感器异构的情况下实现免校准估计。核心思路是，每个机器人使用基于秩的特征独立估计局部置信度，该特征能捕捉观测值的相对变化，且对传感器的缩放和非线性具有不变性。随后，这些局部置信度通过专家乘积公式进行融合，以获得整个团队一致的全局估计。为进一步提升团队协调效率，本文引入了一种信息区域分配与路径规划策略，该策略可减少冗余探索，同时平衡探索与利用。本文采用具有真实气体传感器模型的高保真仿真对所提框架进行验证，结果表明，尽管存在强烈的传感器异构性，本文方法仍显著优于基于标准测量聚合的基准方法，实现了可靠的气源定位精度。更广泛而言，本研究证明免校准感知表示可有效扩展至分布式机器人系统，为其应用于其他涉及异构传感器的估计任务铺平了道路。
英文摘要
Estimating environmental states with multi-robot systems becomes particularly challenging when robots are equipped with uncalibrated and therefore heterogeneous sensors, whose nonlinear and inconsistent responses prevent reliable information fusion. In this paper, we propose a distributed probabilistic framework for source localization tasks that enables calibration-free estimation in the presence of sensor heterogeneity. The key idea is that each robot independently estimates a local belief using a rank-based feature that captures the relative evolution of observations and is invariant to sensor scaling and nonlinearities. These local beliefs are then fused through a product of experts formulation to obtain a consistent global estimate across the team. To further improve the efficiency of team coordination, we introduce an informative region allocation and path planning strategy that reduces redundant exploration while balancing exploration and exploitation. We validate the proposed framework using high-fidelity simulations with realistic gas sensor models. Results demonstrate that our method significantly outperforms a benchmark method based on standard measurement aggregation, achieving reliable source localization accuracy despite strong sensor heterogeneity. More broadly, this work demonstrates how calibration-free sensing representations can be effectively extended to distributed robotic systems, paving the way for their application to other estimation tasks involving heterogeneous sensors.
发表机构
- Distributed Intelligent Systems and Algorithms Laboratory（分布式智能系统与算法实验室）
- School of Architecture, Civil and Environmental Engineering（建筑、土木与环境工程学院）
- École Polytechnique Fédérale de Lausanne (EPFL)（洛桑联邦理工学院）
机构由 AI 辅助整理，请以论文原文为准。
