College of Computer Science and Technology, Jilin University, Changchun, Jilin, China.
Key Laboratory of Symbolic Computation and Knowledge Engineering, Jilin University, Changchun, Jilin, China.
PLoS One. 2023 Nov 13;18(11):e0287791. doi: 10.1371/journal.pone.0287791. eCollection 2023.
Positioning technology is an important component of environmental perception. It is also the basis for autonomous decision-making and motion control of firefighting robots. However, some issues such as positioning in indoor scenarios still remain inherent challenges. The positioning accuracy of the fire emergency reaction dispatching (FERD) system is far from adequate to support some applications for firefighting and rescue in indoor scenarios with multiple obstacles. To solve this problem, this paper proposes a fusion module based on the Blackboard architecture. This module aims to improve the positioning accuracy of a single sensor of the unmanned vehicles within the FERD system. To reduce the risk of autonomous decision-making of the unmanned vehicles, this module uses a comprehensive manner of multiple channels to complement or correct the positioning of the firefighting robots. Specifically, this module has been developed to fusion a variety of relevant processes for precise positioning. This process mainly includes six strategies. These strategies are the denoising, spatial alignment, confidence degree update, observation filtering, data fusion, and fusion decision. These strategies merge with the current scenarios-related parameter data, empirical data on sensor errors, and information to form a series of norms. This paper then proceeds to gain experience data with the confidence degree, error of different sensors, and timeliness of this module by training in an indoor scenario with multiple obstacles. This process is from data of multiple sensors (bottom-level) to control decisions knowledge-based (up-level). This process can obtain globally optimal positioning results. Finally, this paper evaluates the performance of this fusion module for the FERD system. The experimental results show that this fusion module can effectively improve positioning accuracy in an indoor scenario with multiple obstacles. Code is available at https://github.com/lvbingyu-zeze/gopath/tree/master.
定位技术是环境感知的重要组成部分，也是消防机器人自主决策和运动控制的基础。然而，一些问题，如室内场景中的定位，仍然存在固有挑战。火灾应急反应调度（FERD）系统的定位精度远远不能满足一些室内场景中存在多障碍物的消防和救援应用的需求。为了解决这个问题，本文提出了一种基于黑板架构的融合模块。该模块旨在提高 FERD 系统中无人车单传感器的定位精度。为了降低无人车自主决策的风险，该模块采用多通道综合方式对消防机器人的定位进行补充或修正。具体来说，该模块已开发用于融合各种相关过程以实现精确定位。这个过程主要包括六个策略。这些策略是去噪、空间对齐、置信度更新、观测滤波、数据融合和融合决策。这些策略与当前场景相关参数数据、传感器误差的经验数据以及信息融合在一起，形成一系列规范。然后，本文通过在具有多个障碍物的室内场景中进行训练，获得了该模块的置信度、不同传感器的误差和及时性的经验数据。这个过程是从多个传感器（底层）的数据到基于知识的控制决策（上层）。这个过程可以获得全局最优的定位结果。最后，本文评估了该融合模块在 FERD 系统中的性能。实验结果表明，该融合模块可以有效提高多障碍物室内场景中的定位精度。代码可在 https://github.com/lvbingyu-zeze/gopath/tree/master 上获取。
