Hierarchical Obstacle-Avoidance Motion Planning Framework for a Road-Rail Dual-Use Bridge Inspection Manipulator

Under-bridge inspection involves complex structural geometries, confined working spaces, and substantial safety risks for manual operation. To address these challenges, this study proposes a hierarchical obstacle-avoidance motion-planning framework for a large road-rail dual-use bridge inspection manipulator. First, a consistent kinematic model is established for an 11-DOF physical actuation system composed of six revolute joints and five prismatic telescopic joints. For inverse kinematics and template matching, the five physical telescopic joints are mapped to two equivalent prismatic variables, whereas collision checking and execution remain in the full physical joint space. Second, an improved bidirectional RRT-Connect planner is developed by integrating goal-biased sampling, multi-candidate expansion, soft low-lift constraints, and combined state and edge validity checking. Third, a pose-library-guided segmented planning strategy is introduced to reuse successful deployment sequences for known targets and to automatically generate intermediate poses for unseen targets. All post-processed trajectories are revalidated for collision and clearance before acceptance. Comparative simulations demonstrate that the proposed framework improves collision-free planning success and suppresses unreasonable high-lift configurations. The framework provides a reproducible planning solution for automated bridge inspection in confined under-bridge environments.