发表机构
ShanghaiTech University; University of Pennsylvania（上海科技大学; 宾夕法尼亚大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种机器人关节空间中观测约束圆柱腔体检修的自主优化方法，通过多起点无导数搜索优化关节构型，在 Isaac Sim 实验中表现优于基准方法，完成 92%目标构型且平均底部可见度达 91.65%。
AI 中文摘要
检修是移动机器人诸多应用中的核心能力，包括工业设施监控、基础设施维护、农业及搜救。ASTM 响应机器人搜索任务基准要求观测圆柱腔体底部，这一典型挑战在于机器人必须精准定位相机，同时满足可见性、运动学及碰撞约束。本文提出一种面向圆柱腔体观测约束检修的完全自主方法，在机器人关节空间中执行。该方法不指定单一笛卡尔相机位姿，而是将检修目标表示为一组有效观测几何，从而避免拒绝可达视点及关节极限裕度较差的构型。RGB 感知前端借助语义掩码，利用圆弧支撑的椭圆拟合结合主体与侧母线线索，估计腔体开口中心及定向腔体轴。这些估计值参数化相机轴对齐、横向偏移及轴向距离的约束。多起点无导数搜索随后优化机器人关节构型，以字典序优先级满足约束；可行构型按运动经济性、关节极限裕度及观测质量排序。所得候选由感知碰撞的运动规划器评估，执行的相机位姿经几何验证及基于射线的底部可见性估计验证。在 Isaac Sim 中，所提方法成功完成 100 个目标构型中的 92 个，执行试验中平均底部可见度达 91.65%，而多起点坐标搜索基准分别为 100 个中的 76 个及 84.3%。桌面实验与 Unitree A2 搭载实验验证了完整的感知-规划-执行流程。
英文摘要
Inspection is a core capability in many mobile robotics applications, including industrial facility monitoring, infrastructure maintenance, agriculture, and search and rescue. Observing the bottom of a cylindrical cavity, as required by ASTM search-task benchmarks for response robots, presents a representative challenge: the robot must position its camera precisely while satisfying visibility, kinematic, and collision constraints. This paper presents a fully autonomous method for observation-constrained inspection of cylindrical cavities in robot joint space. Rather than prescribing a single Cartesian camera pose, the method represents the inspection objective as a set of valid viewing geometries, thereby avoiding the rejection of reachable viewpoints and configurations with poor joint-limit margins. An RGB perception front end estimates the opening center and directed cavity axis from semantic masks using arc-supported ellipse fitting together with body and side-generator cues. These estimates parameterize constraints on camera-axis alignment, lateral offset, and axial standoff. A multistart derivative-free search then optimizes robot joint configurations with lexicographic priority given to constraint satisfaction; feasible configurations are ranked according to motion economy, joint-limit margin, and view quality. The resulting candidates are evaluated by a collision-aware motion planner, and the executed camera pose is verified geometrically and using a ray-based estimate of bottom visibility. In Isaac Sim, the proposed method successfully completes 92 of 100 target configurations and attains 91.65% mean bottom visibility among executed trials, compared with 76 of 100 and 84.3% for a multistart coordinate-search baseline. Tabletop and Unitree A2-mounted experiments demonstrate the complete perception-planning-execution pipeline.
