Real-Time Trajectory Generation for Soft Robot Manipulators Using Differential Flatness

TL;DR

This paper presents a novel real-time trajectory generation method for soft robot manipulators using differential flatness, enabling fast and feasible motion planning by algebraically computing control inputs from curvature-based models.

Contribution

The paper introduces the first differential flatness-based approach for soft robot trajectory planning, allowing algebraic control input calculation for arbitrary paths.

Findings

Achieves 23x faster than real-time trajectory generation at 100 Hz.

Validates approach through simulations on a soft robot manipulator.

Enables rapid, safe replanning in complex environments.

Abstract

Soft robots have the potential to interact with sensitive environments and perform complex tasks effectively. However, motion plans and trajectories for soft manipulators are challenging to calculate due to their deformable nature and nonlinear dynamics. This article introduces a fast real-time trajectory generation approach for soft robot manipulators, which creates dynamically-feasible motions for arbitrary kinematically-feasible paths of the robot's end effector. Our insight is that piecewise constant curvature (PCC) dynamics models of soft robots can be differentially flat, therefore control inputs can be calculated algebraically rather than through a nonlinear differential equation. We prove this flatness under certain conditions, with the curvatures of the robot as the flat outputs. Our two-step trajectory generation approach uses an inverse kinematics procedure to calculate a motion plan of robot curvatures per end-effector position, then, our flatness diffeomorphism generates corresponding control inputs that respect velocity. We validate our approach through simulations of our representative soft robot manipulator along three different trajectories, demonstrating a margin of 23x faster than real-time at a frequency of 100 Hz. This approach could allow fast verifiable replanning of soft robots' motions in safety-critical physical environments, crucial for deployment in the real world.