Parallel Simulation of Contact and Actuation for Soft Growing Robots

TL;DR

This paper addresses how to plan, design, and simulate actuation for soft growing (vine) robots that must navigate cluttered environments using contact with obstacles. It introduces a unified modeling framework that couples growth, bending, sPAM actuation, and environmental contact, and implements a fast GPU-parallel simulator augmented by a neural surrogate for actuator models. It then uses a long-horizon, sampling-based kinodynamic planner to optimize designs that minimize actuator count by leveraging obstacle interactions, validated by physical experiments with multi-obstacle settings. The results show accurate prediction of contact–actuation behavior, robust performance under uncertainty, and clear funneling effects that aid navigation, enabling practical deployment of optimized vine robots in complex environments.

Abstract

Soft growing robots, commonly referred to as vine robots, have demonstrated remarkable ability to interact safely and robustly with unstructured and dynamic environments. It is therefore natural to exploit contact with the environment for planning and design optimization tasks. Previous research has focused on planning under contact for passively deforming robots with pre-formed bends. However, adding active steering to these soft growing robots is necessary for successful navigation in more complex environments. To this end, we develop a unified modeling framework that integrates vine robot growth, bending, actuation, and obstacle contact. We extend the beam moment model to include the effects of actuation on kinematics under growth and then use these models to develop a fast parallel simulation framework. We validate our model and simulator with real robot experiments. To showcase the capabilities of our framework, we apply our model in a design optimization task to find designs for vine robots navigating through cluttered environments, identifying designs that minimize the number of required actuators by exploiting environmental contacts. We show the robustness of the designs to environmental and manufacturing uncertainties. Finally, we fabricate an optimized design and successfully deploy it in an obstacle-rich environment.

Figures (12)

• Figure 1: (a), (b): Variable definitions for the sPAM and inflated beam model. (c): Illustration of sPAM states from completely deflated with zero strain (top) to saturated with maximal strain (bottom). Darker color indicates higher input pressure and greater contraction.
• Figure 2: (a): Vine segment bending under sPAM actuation. (b): Vine segment wrinkled and tensioned regions depicting $\gamma_0$. (c): Comparison of measured bending angles with theoretical estimations using wrinkling based model and constant moment model.
• Figure 3: Left: The neural surrogate demonstrates a major speed up over the baseline unbatched method, especially as the batch size increases. Batched GPU processing can greatly accelerate parallelizable simulation tasks. Right: Distribution of neural surrogate squared error as compared with ground truth numeric method. We uniformly sampled 40,000 values over the surrogate's input space and normalized the outputs.
• Figure 4: Progression of planning in the Long environment, illustrating rapid initial exploration followed by convergent optimization. The planner identifies an initial solution by iteration 5, then refines the design through exploration of alternative contact-exploitation strategies. The colorbar on the right represents the number of actuated segments.
• Figure 5: Single-obstacle environment experiment comparing simulated predictions for different obstacle contact conditions. (a) Acute angle of contact, (b) Obtuse angle of contact, (c) Head-on contact. The robot buckles at a point near its base.