Real-Time Trajectory Generation for Soft Robot Manipulators Using Differential Flatness
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arXiv
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| Format: | Preprint |
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2024
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| author | Dickson, Akua Garcia, Juan C. Pacheco Jing, Ran Anderson, Meredith L. Sabelhaus, Andrew P. |
| author_facet | Dickson, Akua Garcia, Juan C. Pacheco Jing, Ran Anderson, Meredith L. Sabelhaus, Andrew P. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_08568 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Real-Time Trajectory Generation for Soft Robot Manipulators Using Differential Flatness Dickson, Akua Garcia, Juan C. Pacheco Jing, Ran Anderson, Meredith L. Sabelhaus, Andrew P. Robotics Systems and Control 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. |
| title | Real-Time Trajectory Generation for Soft Robot Manipulators Using Differential Flatness |
| topic | Robotics Systems and Control |
| url | https://arxiv.org/abs/2412.08568 |