This paper addresses the problem of autonomous robotic inspection in complex and unknown environments. This capability is crucial for efficient and precise inspections in various real-world scenarios, even when faced with perceptual uncertainty and lack of prior knowledge of the environment. Existing methods for real-world autonomous inspections typically rely on predefined targets and waypoints and often fail to adapt to dynamic or unknown settings. In this work, we introduce the Semantic Belief Behavior Graph (SB2G) framework as a novel approach to semantic-aware autonomous robot inspection. SB2G generates a control policy for the robot, featuring behavior nodes that encapsulate various semantic-based policies designed for inspecting different classes of objects. We design an active semantic search behavior to guide the robot in locating objects for inspection while reducing semantic information uncertainty. The edges in the SB2G encode transitions between these behaviors. We validate our approach through simulation and real-world urban inspections using a legged robotic platform. Our results show that SB2G enables a more efficient inspection policy, exhibiting performance comparable to human-operated inspections.

翻译：本文研究了在复杂未知环境中实现自主机器人巡检的问题。该能力对于各类现实场景中高效、精确的巡检至关重要，即使在面临感知不确定性且缺乏环境先验知识的情况下亦然。现有的现实世界自主巡检方法通常依赖预定义目标与路径点，往往难以适应动态或未知环境。本工作提出语义信念行为图（SB2G）框架作为一种新颖的语义感知自主机器人巡检方法。SB2G为机器人生成控制策略，其行为节点封装了针对不同类别物体设计的多种基于语义的策略。我们设计了主动语义搜索行为以引导机器人定位待检物体，同时降低语义信息的不确定性。SB2G中的边编码了这些行为间的转移关系。我们通过仿真实验和采用足式机器人平台的实际城市巡检验证了所提方法。结果表明，SB2G能实现更高效的巡检策略，其性能可与人工操作巡检相媲美。