A Transient-Goal Driven Communication-Aware Navigation Strategy for Large Human-Populated Environments
A Transient-Goal Driven Communication-Aware Navigation Strategy for Large Human-Populated Environments
复制标题
适用于大型人类居住环境的瞬态目标驱动的通信感知导航策略
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
N. Hagita
中科院分区：
文献类型：
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作者：
Vishnu K. Narayanan;T. Miyashita;Yukiko Horikawa;N. Hagita
部署在大型人口稠密的室内环境中的机器人，如商场、机场等，无意中通过无线网络进行通信，以增强感知和决策能力。由于这种环境中信号的高度动态衰减特性，在机器人导航过程中可能会出现连通性问题，导致信息流中断，造成潜在危险。为了估计空间信号的变化，信号传播的精确建模通常是具有挑战性的。此外，动态人类的存在还增加了一层时间信号变化的复杂性。因此，本文介绍了一种在大规模人口环境中将无线电信号强度约束嵌入到网络化服务/社交机器人导航中的创成式方法。首先，我们提出了一种基于高斯过程的在线时空信号强度预测模型，与现有技术不同，该模型还旨在考虑由于人群的存在而引起的时间衰落。然后，我们设计了一种瞬时目标驱动的导航策略，以实现通往目标的次最优路径，旨在解决通信感知和人类感知的规划约束。对所提出的信号预测模型的评估表明，我们的方法相对于当前的技术水平具有优势。在大型购物中心的机器人轮椅上进行的模拟和硬件实验也证明了导航策略的有效性。
Robots deployed in large human-populated indoor environments such as shopping malls, airports etc., inadvertently communicate via wireless networks for enhanced perception and decision making capabilities. Owing to highly dynamic signal attenuation characteristics in such environments, connectivity issues may arise during robotic navigation, leading to disruption in information flow causing potential danger. Exact modeling of signal propagation for estimating spatial signal variation is usually challenging. Moreover, the presence of dynamic humans also add a layer of temporal signal variation complexities. Thus, this paper introduces a generative approach for embedding radio signal strength constraints within networked service/social robot navigation in large human-populated environments. Initially, we propose a Gaussian Process based online spatio-temporal signal strength prediction model that, as opposed to the current state of the art, also aims to take into account the temporal fading arising due to the presence of human crowds. We then devise a transient-goal driven navigation strategy to realize a sub-optimal path towards a goal, that is aimed at resolving both communication-aware and human-aware planning constraints. Evaluations of the proposed signal prediction model demonstrate the advantages of our approach with respect to the current state of the art. The efficacy of the navigation strategy in also demonstrated simulations and using hardware experiments conducted on a robotic wheelchair operating in a large shopping mall.