发表机构
Università di Trento; Istituto Italiano di Tecnologia; Leonardo Spa（特伦托大学; 意大利技术研究院; 莱昂纳多公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对自主移动机器人缺乏上下文感知的问题，本文探索两个互补方向：一是人类重新识别与跟踪以改善人机协作，二是增强机器人对环境的几何和语义感知能力，提升语义理解，推动机器人更具上下文感知，实现安全共存与高效协作。
AI 中文摘要
自主移动机器人从受控工业环境向动态、以人类为中心的环境（如制造、物流和医疗保健）的转变，使其安全自主运行成为关键研究领域。这些复杂机器必须能够感知、理解并与周围环境互动，以自由导航并执行复杂任务。实现这一目标的重大障碍是缺乏全面的上下文感知，即机器人识别空间环境及其中物体和参与者的能力。本文通过探索两个不同但互补的研究方向提出了新的解决方案。第一个方向是人类重新识别和跟踪以改善人机协作，开发的方法能让移动机器人识别特定人员，促进有针对性的协作。第二个方向是增强机器人对环境的整体感知能力，包括几何和语义理解。几何信息对运动规划和碰撞避免至关重要，语义知识能让机器人进行更高级的交互。这两种解决方案都由提升机器人语义理解驱动，增强其对周围环境的认识，实现机器人、人类和环境之间更顺畅自然的交互。这项工作在人类重新识别和环境理解方面的贡献，朝着机器人更具上下文感知的未来迈出了重要一步，实现更安全的共存和更有效的协作。
英文摘要
The transition of autonomous mobile robots from controlled industrial settings to dynamic, human-centric environments, such as manufacturing, logistics, and healthcare, has made their safe and autonomous operation a critical area of research. These sophisticated machines must be capable of perceiving, understanding, and interacting with their surroundings to navigate freely and perform complex tasks. A significant obstacle to achieving this is the lack of comprehensive contextual awareness, which requires a robot to recognize its spatial environment and identify the objects and actors within it. Without this perceptual knowledge, robots struggle to plan adaptive behaviors or engage in meaningful interaction with humans. This thesis presents novel solutions to this challenge by exploring two distinct but complementary research directions. The first direction involves human re-identification and tracking to improve Human-Robot Collaboration. Our developed approach enables a mobile robot to recognize a specific person, facilitating targeted collaboration while ignoring other individuals. The second direction focuses on enhancing the robot's overall perceptual capabilities to understand its environment geometrically and semantically. Geometric information is vital for motion planning and collision avoidance, while semantic knowledge provides the robot with a richer understanding for more advanced interaction. Both solutions are driven by the improvement of the semantical understanding of robots that enhance their knowledge of their surroundings, allowing a smoother and more natural interaction between robots, humans, and the environment. The contributions of this work in human re-identification and environmental understanding represent a significant step toward a future where robots are more contextually aware, enabling safer coexistence and more effective collaboration.
CommentsPh.D. thesis 2026. Officially published in the IRIS institutional repository of the University of Trento (https://hdl.handle.net/11572/482510) and deposited in the Italian National Legal Deposit for Ph.D. theses
