Observation-Constrained Joint-Space Viewpoint Optimization for Robotic Inspection of Cylindrical Cavities
2026-08-17 • Robotics
Robotics
AI summaryⓘ
The authors developed a fully autonomous method for robots to inspect the bottom of cylindrical cavities, which is important in many fields like industrial monitoring and search and rescue. Instead of forcing the robot to look from one exact spot, their method considers many possible good viewing angles to find the best camera positions while avoiding joint limits and collisions. They use computer vision to estimate the opening and axis of the cavity, then optimize robot movements to satisfy constraints and ensure good visibility. Tests in simulation and with real robots show their method works better than previous approaches in completing inspection tasks and maximizing the visible area inside the cavity.
mobile roboticscylindrical cavity inspectionrobot joint spaceRGB perceptionarc-supported ellipse fittingconstraint optimizationcollision-aware motion planningvisibility estimationmultistart searchIsaac Sim
Authors
Yuezhong Wang, Rongshen Yin, Bichi Zhang, Sören Schwertfeger
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.