Real-Time Trajectory Generation for Soft Robot Manipulators Using Differential Flatness
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 traj...
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creator | Dickson, Akua Garcia, Juan C. Pacheco Jing, Ran Anderson, Meredith L Sabelhaus, Andrew P |
description | 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. |
doi_str_mv | 10.48550/arxiv.2412.08568 |
format | Article |
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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.</description><identifier>DOI: 10.48550/arxiv.2412.08568</identifier><language>eng</language><subject>Computer Science - Robotics ; Computer Science - Systems and Control</subject><creationdate>2024-12</creationdate><rights>http://creativecommons.org/licenses/by/4.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,780,885</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2412.08568$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2412.08568$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Dickson, Akua</creatorcontrib><creatorcontrib>Garcia, Juan C. Pacheco</creatorcontrib><creatorcontrib>Jing, Ran</creatorcontrib><creatorcontrib>Anderson, Meredith L</creatorcontrib><creatorcontrib>Sabelhaus, Andrew P</creatorcontrib><title>Real-Time Trajectory Generation for Soft Robot Manipulators Using Differential Flatness</title><description>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.</description><subject>Computer Science - Robotics</subject><subject>Computer Science - Systems and Control</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNqFzrEOgjAUheEuDkZ9ACfvC4CAYNhVdHFBjCO5mltzTWlJW428vUjcnc5w_uETYh5HYZpnWbRE--ZXmKRxEkZ5ts7H4lISqqDihqCy-KCbN7aDPWmy6NlokMbCyUgPpbkaD0fU3D4V9pmDs2N9hy1LSZa0Z1RQ9Jcm56ZiJFE5mv12IhbFrtocgoFQt5YbtF39pdQDZfW_-AD06D-F</recordid><startdate>20241211</startdate><enddate>20241211</enddate><creator>Dickson, Akua</creator><creator>Garcia, Juan C. Pacheco</creator><creator>Jing, Ran</creator><creator>Anderson, Meredith L</creator><creator>Sabelhaus, Andrew P</creator><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20241211</creationdate><title>Real-Time Trajectory Generation for Soft Robot Manipulators Using Differential Flatness</title><author>Dickson, Akua ; Garcia, Juan C. Pacheco ; Jing, Ran ; Anderson, Meredith L ; Sabelhaus, Andrew P</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-arxiv_primary_2412_085683</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Computer Science - Robotics</topic><topic>Computer Science - Systems and Control</topic><toplevel>online_resources</toplevel><creatorcontrib>Dickson, Akua</creatorcontrib><creatorcontrib>Garcia, Juan C. Pacheco</creatorcontrib><creatorcontrib>Jing, Ran</creatorcontrib><creatorcontrib>Anderson, Meredith L</creatorcontrib><creatorcontrib>Sabelhaus, Andrew P</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Dickson, Akua</au><au>Garcia, Juan C. Pacheco</au><au>Jing, Ran</au><au>Anderson, Meredith L</au><au>Sabelhaus, Andrew P</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Real-Time Trajectory Generation for Soft Robot Manipulators Using Differential Flatness</atitle><date>2024-12-11</date><risdate>2024</risdate><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.</abstract><doi>10.48550/arxiv.2412.08568</doi><oa>free_for_read</oa></addata></record> |
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subjects | Computer Science - Robotics Computer Science - Systems and Control |
title | Real-Time Trajectory Generation for Soft Robot Manipulators Using Differential Flatness |
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