Toward Error-Tolerant Robot Navigation: Sequential Inducement Based on Intent Conveyance from Robot to Human and Its Achievement

Abstract

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.