cs.ROAug 17, 2026

Observation-Constrained Joint-Space Viewpoint Optimization for Robotic Inspection of Cylindrical Cavities

Authors: Yuezhong Wang, Rongshen Yin, Bichi Zhang, Sören Schwertfeger

Organizations: Key Laboratory of Intelligent Perception and Human–Machine Collaboration, Ministry of Education, ShanghaiTech University, Shanghai, China · University of Pennsylvania, Philadelphia, PA, USA

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Abstract

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.

Figures & tables

Fig. 1: Inspection platforms used in this work: (a) the tabletop eye-in-hand setup and (b) the Piper manipulator mounted on a Unitree A2.
Fig. 2: Pipeline overview. Perception estimates task-specific cylindrical geometry, which defines an observation region. Joint-space search returns multiple feasible configurations; motion planning and post-execution checks operate on the ranked set.
Fig. 3: Isaac Sim evaluation visualization: (a) the manipulator and the sampled cylindrical-target configurations; (b) a representative eye-in-hand RGB observation. The RGB rendering is used for qualitative visualization in the controlled comparison, while all methods receive the same simulator ground-truth opening center and axis.
MethodPos.Ang.VisibleSuccess
 (mm)(deg)(%)(/100)
Fixed Pose + IK140.933.6044.5834
View Sampling + IK49.215.5180.265
Multi-Start CD37.928.4184.376
Proposed42.921.6891.6592
TABLE I: Simulation results over 100 target configurations. Position error, angular error, and visibility are averaged over trials with an executed camera pose; success is computed over all 100 configurations.
Fig. 4: Complete A2-mounted inspection sequence, ordered left-to-right and top-to-bottom. The arm leaves its initial posture, reorients toward the detected cylindrical opening, approaches the target, and reaches a camera configuration from which the cavity interior is visible.
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