Trajectory Generation for Underactuated Soft Robot Manipulators using Discrete Elastic Rod Dynamics
This work addresses motion planning for soft robots in contact-rich tasks, offering a method that balances physical accuracy and computational efficiency, though it is incremental as it builds on existing dynamics models.
The paper tackled the challenge of generating dynamically feasible trajectories for underactuated soft robots by introducing a control-oriented reformulation of Discrete Elastic Rod dynamics, resulting in improved trajectory tracking performance over a baseline method in hardware experiments.
Soft robots are well suited for contact-rich tasks due to their compliance, yet this property makes accurate and tractable modeling challenging. Planning motions with dynamically-feasible trajectories requires models that capture arbitrary deformations, remain computationally efficient, and are compatible with underactuation. However, existing approaches balance these properties unevenly: continuum rod models provide physical accuracy but are computationally demanding, while reduced-order approximations improve efficiency at the cost of modeling fidelity. To address this, our work introduces a control-oriented reformulation of Discrete Elastic Rod (DER) dynamics for soft robots, and a method to generate trajectories with these dynamics. The proposed formulation yields a control-affine representation while preserving certain first-principles force-deformation relationships. As a result, the generated trajectories are both dynamically feasible and consistent with the underlying actuation assumptions. We present our trajectory generation framework and validate it experimentally on a pneumatic soft robotic limb. Hardware results demonstrate consistently improved trajectory tracking performance over a constant-curvature-based baseline, particularly under complex actuation conditions.