Assisting individuals in their daily activities through autonomous mobile robots, especially for users without specialized knowledge, is crucial. Specifically, the capability of robots to navigate to destinations based on human speech instructions is essential. While robots can take different paths to the same goal, the shortest path is not always the best. A preferred approach is to accommodate waypoint specifications flexibly, planning an improved alternative path, even with detours. Additionally, robots require real-time inference capabilities. This study aimed to realize a hierarchical spatial representation using a topometric semantic map and path planning with speech instructions, including waypoints. This paper presents Spatial Concept-based Topometric Semantic Mapping for Hierarchical Path Planning (SpCoTMHP), integrating place connectivity. This approach offers a novel integrated probabilistic generative model and fast approximate inference across hierarchy levels. A formulation based on control as probabilistic inference theoretically supports the proposed path planning algorithm. We conducted experiments in home environments using the Toyota Human Support Robot on the SIGVerse simulator and in a lab-office environment with the real robot, Albert. Users issued speech commands specifying the waypoint and goal, such as "Go to the bedroom via the corridor." Navigation experiments using speech instructions with a waypoint demonstrated a performance improvement of SpCoTMHP over the baseline hierarchical path planning method with heuristic path costs (HPP-I), in terms of the weighted success rate at which the robot reaches the closest target and passes the correct waypoints, by 0.590. The computation time was significantly accelerated by 7.14 seconds with SpCoTMHP compared to baseline HPP-I in advanced tasks.

翻译：通过自主移动机器人辅助个体日常活动，对于不具备专业知识的用户尤为重要。具体而言，机器人根据人类语音指令导航至目标位置的能力至关重要。虽然机器人可通过不同路径到达同一目标，但最短路径并非总是最优选择。更理想的方式是灵活适应途经点设定，即使需要绕行也能规划出更优的替代路径。此外，机器人需具备实时推理能力。本研究旨在利用拓扑语义地图实现分层空间表征，并通过包含途经点的语音指令进行路径规划。本文提出基于空间概念的拓扑语义地图分层路径规划方法（SpCoTMHP），该方法融合了场所连通性。该框架提供了一种新颖的集成概率生成模型及跨层级快速近似推理算法。基于概率推理控制的理论公式为所提出的路径规划算法提供了理论支撑。我们在家庭环境中使用丰田人类辅助机器人于SIGVerse模拟器进行实验，并在实验室-办公室环境中使用真实机器人Albert进行验证。用户通过语音指令指定途经点与目标，例如"经由走廊前往卧室"。包含途经点的语音导航实验表明：在加权成功率指标（衡量机器人抵达最近目标并通过正确途经点的综合性能）上，SpCoTMHP较采用启发式路径成本的分层路径规划基准方法（HPP-I）提升0.590。在复杂任务中，SpCoTMHP的计算时间较基准HPP-I显著加速7.14秒。