Taniguchi Akira, Ito Shuya, Taniguchi Tadahiro
Emergent Systems Laboratory, Ritsumeikan University, Kusatsu, Shiga, Japan.
Front Robot AI. 2024 Aug 1;11:1291426. doi: 10.3389/frobt.2024.1291426. eCollection 2024.
Assisting individuals in their daily activities through autonomous mobile robots is a significant concern, especially for users without specialized knowledge. Specifically, the capability of a robot to navigate to destinations based on human speech instructions is crucial. Although robots can take different paths toward the same objective, the shortest path is not always the most suitable. A preferred approach would be to accommodate waypoint specifications flexibly for planning an improved alternative path even with detours. Furthermore, robots require real-time inference capabilities. In this sense, spatial representations include semantic, topological, and metric-level representations, each capturing different aspects of the environment. This study aimed to realize a hierarchical spatial representation using a topometric semantic map and path planning with speech instructions by including waypoints. Thus, we present a hierarchical path planning method called spatial concept-based topometric semantic mapping for hierarchical path planning (SpCoTMHP), which integrates place connectivity. This approach provides a novel integrated probabilistic generative model and fast approximate inferences with interactions among the hierarchy levels. A formulation based on "control as probabilistic inference" theoretically supports the proposed path planning algorithm. We conducted experiments in a home environment using the Toyota human support robot on the SIGVerse simulator and in a lab-office environment with the real robot Albert. Here, the user issues speech commands that specify the waypoint and goal, such as "Go to the bedroom via the corridor" Navigation experiments were performed using speech instructions with a waypoint to demonstrate the performance improvement of the 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 (0.590) and passes the correct waypoints. The computation time was significantly improved by 7.14 s with the SpCoTMHP than the baseline HPP-I in advanced tasks. Thus, hierarchical spatial representations provide mutually understandable instruction forms for both humans and robots, thus enabling language-based navigation.
通过自主移动机器人协助个人进行日常活动是一个重要问题，尤其对于没有专业知识的用户而言。具体来说，机器人根据人类语音指令导航到目的地的能力至关重要。尽管机器人可以通过不同路径到达同一目标，但最短路径并不总是最合适的。一种更好的方法是灵活地适应航路点规范，以便即使有迂回也要规划出一条改进的替代路径。此外，机器人需要实时推理能力。从这个意义上讲，空间表示包括语义、拓扑和度量级表示，每种表示都捕捉环境的不同方面。本研究旨在通过包含航路点，使用拓扑度量语义地图实现分层空间表示并进行语音指令路径规划。因此，我们提出了一种用于分层路径规划的基于空间概念的拓扑度量语义映射（SpCoTMHP）分层路径规划方法，该方法整合了地点连通性。这种方法提供了一种新颖的集成概率生成模型以及层次级别之间相互作用的快速近似推理。基于“控制即概率推理”的公式从理论上支持了所提出的路径规划算法。我们在家庭环境中使用丰田人类支持机器人在SIGVerse模拟器上进行了实验，并在实验室办公室环境中使用真实机器人阿尔伯特进行了实验。在这里，用户发出指定航路点和目标的语音命令，例如“经由走廊前往卧室”。使用带有航路点的语音指令进行了导航实验，以证明SpCoTMHP相对于具有启发式路径成本的基线分层路径规划方法（HPP-I）在机器人到达最接近目标（0.590）并通过正确航路点的加权成功率方面的性能提升。在高级任务中，与基线HPP-I相比，SpCoTMHP的计算时间显著缩短了7.14秒。因此，分层空间表示为人类和机器人提供了相互理解的指令形式，从而实现基于语言的导航。
