Database-Driven Capability Maps for Pareto-Based Configuration Selection in Tendon-Driven Continuum Robots

Tendon-driven continuum robots (TDCRs) have been widely investigated for minimally invasive surgery, inspection, and cluttered-environment operation because of their compliance and dexterity. However, dense workspace characterization and configuration selection remain challenging because inverse kinematics must be solved in a highly redundant, nonlinear space under tendon-actuation constraints. Existing capability map frameworks do not directly address configuration-level tendon feasibility or dexterity-aware selection among feasible alternatives. This paper presents a database-driven CAP framework that combines piecewise constant curvature kinematics with particle swarm optimization to construct an offline library of configurations consistent with the prototype tendon-routing and command-mapping model over 180 workspace voxels and 54,000 voxel-orientation samples. For each feasible sample, pose residuals and Jacobian-based dexterity descriptors are computed offline and stored in the database. At runtime, the method retrieves feasible entries for a queried voxel and ranks them through a Pareto-based trade-off between combined pose residual and inverse manipulability. The output is a dexterity-aware representative configuration for the queried local neighbourhood, rather than a newly optimized exact-pose solution. Motion-capture validation on a custom two-segment TDCR shows millimetre-order position errors in the evaluated ROI.