Empirical Contact Models for Soft Spherical Robots in Drake
Micah Oevermann, Dhruv Datta, Dylan Hilburn, Derek Pravecek, Rishi Jangale, Aaron Villanueva, Robert Ambrose
AI summary
Problem
Accurate dynamic modeling of soft-shelled spherical robots is challenging due to coupled rigid–soft body interactions and pressure-dependent contact behavior that are often oversimplified in existing simulations.
Approach
The authors built a modular Drake simulation framework that integrates a custom URDF with empirically tuned joint friction models and three alternative outer shell contact models parameterized from physical experiments.
Key results
- Custom URDF and modular Drake simulation framework for pendulum-driven soft spherical robots
- Empirical characterization of pressure-dependent shell dynamics and joint friction models
- Tuned injected stiffness-damping model outperforms native hydroelastic and rigid point-contact baselines
- Validated ramp descent experiments show reduced drive velocity prediction error with appropriate contact modeling
Why it matters
Provides a reproducible, data-driven simulation pathway for soft robotics researchers and control engineers to accurately model and control soft-shelled mobile robots.
Abstract
Accurate dynamic modeling of soft-shelled spher- ical robots is challenging due to coupled rigid–soft body interactions and pressure-dependent contact behavior. This letter presents a modeling strategy for an empirically tuned pendulum-driven inflatable spherical robot. The approach combines a rigid-body dynamics engine in Drake with non- conservative effects. The robot’s rigid-body model is generated from a custom URDF and augmented with interchangeable joint friction modules. Three alternative outer shell contact models are also considered: Drake’s native hydroelastic contact, a pressure-dependent injected stiffness–damping model derived from isolated shell experiments, and a rigid point-contact base- line. Shell dynamics are characterized in the steering direction using a custom locking fixture, yielding empirical pressure- related frequency and damping relationships to parameterize the models. Ramp descent experiments across multiple inflation pressures validate the framework, showing that an appropriate model reduces drive velocity prediction error compared to a rigid point-contact case. The approach enables modular integration of additional dynamic effects, supports data-driven parameter tuning, and provides a reproducible pathway for accurate simulation of soft spherical robots. Soft Robotics; Dynamic Simulation; Contact Modeling