Error-Tolerant Robot Navigation with Sequential Inducement Based on Intent Conveyance from Robot to Human and Its Achievement
Error-Tolerant Robot Navigation with Sequential Inducement Based on Intent Conveyance from Robot to Human and Its Achievement
复制标题
基于机器人到人类意图传递的顺序诱导容错机器人导航及其实现
DOI：
10.1007/s12369-023-00965-7
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发表时间：
2023
影响因子：
4.7
通讯作者：
and Shigeki Sugano
中科院分区：
文献类型：
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作者：
Mitsuhiro Kamezaki;Kono Ryosuke;Ayano Kobayashi;Hayato Yanagawa;Tomoya Onishi;Yusuke Tsuburaya;Moondeep Shrestha;and Shigeki Sugano
人类共生移动机器人需要在避开人类的同时顺利到达目的地。人的意图信息，例如移动方向，必须仔细估计，因为缺乏它会导致犹豫和反复回避。传统的研究着眼于人类意图的估计和传达，但没有将沟通失败的情况作为一个系统的框架，尽管由于其内在的原因，人类基本上很难估计意图。针对这一问题，我们提出了一种容错导航(ETN)框架，其过程是通过机器人的迭代交互来主动估计人类的意图。作为初步研究，我们将重点放在“从机器人到人的意图传达”及其“实现”这两个核心信息上。ETN估计干扰可能性以确定是否需要诱因，人类意识(HA)以选择诱因方法，以及诱因成就(IA)以判断是否需要再次采取行动。如果ETN估计干扰，则机器人根据HA提供诱导，例如，当HA较高时的路线指示或当HA较低时的语音/物理交互。每一种诱因都对应于人类预期的行为变化。IA是根据预期变化和实际变化之间的差额计算的。如果机器人在诱导后的指定时间内没有观察到变化，则它以更强烈的意图传送来执行诱导。当IA在最强动作后为NONE时，它会选择另一条路由。ETN中的这个错误收集循环可以通过识别并恢复一个小错误来防止致命错误。静态和动态实验结果表明，与传统的导航系统相比，ETN可以通过纠正机器人的行为来实现更平稳的人体运动，减轻心理负担，有助于构建实用的ETN框架。
Human symbiotic mobile robots are required to smoothly reach destinations while avoiding humans. Human-intent information, e.g., a moving direction, must be carefully estimated since its lack leads to the hesitation and repetitious avoidance. Conventional studies address human intent estimation and conveyance but do not consider cases of failing communication as a systematic framework, even though the intent is essentially difficult for humans to estimate due to its interiority. In response to this problem, we propose a framework of error-tolerant navigation (ETN) with a process to actively estimate the human intent by iterative interaction from the robot. As a preliminary study, we focus on ‘the intent conveyance from robot to human’ and ‘its achievement’ as core information. The ETN estimates interference possibility to determine the need for inducement, human awareness (HA) to select an inducement method, and inducement achievement (IA) to judge the need for action again. If the ETN estimates the interference, the robot provides inducements according to HA, e.g., route indication when HA is high or voice/physical interaction when HA is low. Each inducement corresponds to an expected behavior change in the human. IA is calculated from the difference between the expected and actual changes. If the robot observes no change within a specified time after the inducement, it executes inducements with a stronger intent conveyance. When IA is none after the strongest action, it selects another route. This error-collection loop in the ETN could prevent a fatal mistake by recognizing a small mistake and recovering it. The static and dynamic experimental results indicated that the ETN could achieve smoother human movement and reduce psychological burden by correcting the robot behaviors, compared with a conventional navigation system, which can contribute to constructing a practical ETN framework.