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

2026-08-17

AI Summary

This study addresses the challenge of autonomous observation of cylindrical cavity bottoms by robots in confined spaces by proposing a joint-space viewpoint optimization method. Innovatively replacing single Cartesian poses with effective geometric sets to prevent loss of reachable viewpoints, the approach integrates semantic awareness and multi-start derivative-free search. A lexicographic prioritization strategy is employed to jointly optimize constraint satisfaction, motion economy, and joint margins. Simulations demonstrate a 92% success rate and 91.65% average bottom visibility, significantly outperforming baseline methods. Furthermore, real-world experiments validate the method’s effectiveness in achieving high-precision autonomous inspection under complex constraints, confirming its practical applicability for robotic inspection tasks in restricted environments.

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.

Problem

• Robotic Inspection
• Viewpoint Optimization
• Cylindrical Cavities
• Observation Constraints
• Joint-Space Planning

Innovation

• Joint-Space Optimization
• Observation-Constrained Inspection
• Viewpoint Geometry Set
• Derivative-Free Search
• Semantic Ellipse Fitting