Database-driven capability maps for Pareto-based configuration selection in tendon-driven continuum robots
Abstract
Tendon-Driven Continuum Robots (TDCRs) are increasingly used in minimally invasive surgery and cluttered environments 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.
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BibTeX
@article{jabari2026databasedriven,
title = {{Database-driven capability maps for Pareto-based configuration selection in tendon-driven continuum robots}},
author = {Mohammad Jabari and Carmen Visconte and Giuseppe Quaglia and Abdelbadia Chaker and Med Amine Laribi},
journal = {Mechanism and Machine Theory},
year = {2026},
month = sep,
doi = {10.1016/j.mechmachtheory.2026.106606},
url = {https://isae-ensma.hal.science/hal-05767241v1},
note = {CC BY},
}
RIS
TY - JOUR
TI - Database-driven capability maps for Pareto-based configuration selection in tendon-driven continuum robots
AU - Mohammad Jabari
AU - Carmen Visconte
AU - Giuseppe Quaglia
AU - Abdelbadia Chaker
AU - Med Amine Laribi
PY - 2026
DA - 2026/09/28
JO - Mechanism and Machine Theory
DO - 10.1016/j.mechmachtheory.2026.106606
UR - https://isae-ensma.hal.science/hal-05767241v1
AB - Tendon-Driven Continuum Robots (TDCRs) are increasingly used in minimally invasive surgery and cluttered environments 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.
SN - 0094-114X
ER -