Vision-based shape reconstruction and control of soft continuum arms

Interest in soft continuum arms (SCAs) has increased as their inherent material elasticity enables safe and adaptive interactions with the environment. However, to achieve full autonomy in these arms, further development in accurate shape sensing and control methods is needed. Vision-based solutions have been found to be effective for such tasks. This dissertation investigates the use of vision-based methods to estimate and control a SCA. The developed shape estimator utilizes a geometric strain based representation for the SCA's shape. This representation reduces the dimension of the curved shape to a finite set of strain basis functions, thereby allowing for efficient optimization for the shape that best fits the observed image. Experimental results demonstrate the effectiveness of the proposed approach in estimating the end effector with accuracy less than the soft arm's radius. Vision-based control, i.e. Visual Servoing (VS), for SCAs is challenging due to the difficulty in modelling them. Lately, more researchers are using the Coserrat Rod Model to accurately model SCAs, since it accounts for the material properties of the SCA and the effect of external forces. In this dissertation, a model-based VS method that relies on the Coserrat Rod model is developed. A model-free VS method is also developed for cases where the material properties are not available. Both eye-in-hand and eye-to-hand VS are demonstrated using these methods. Trade-offs arise when deciding to control SCAs with eye-in-hand or eye-to-hand VS. Cameras placed at a distance (eye-to-hand) can observe a larger workspace area and the SCA tip, while a camera at the end effector (eye-in-hand) can more accurately survey the target. In this dissertation, a hybrid eye-to-hand and eye-in-hand VS scheme to track a desired object in the SCA's worksapce is introduced. When the target is not in the field-of-view of the tip camera, hand-to-eye VS is implemented using a wide field-of-view camera on the soft robot's base, to servo the soft robot's tip to a feasible region where the target is expected to be seen by the tip camera. This region is estimated by solving an optimization problem that finds the best region to place the SCA, assuming a constant curvature model for the SCA. When the target is seen by the tip camera, the system switches to a hand-in-eye controller that keeps the target in the desired image position of the tip camera. Experimental results on the BR² SCA demonstrate the effectiveness of the hybrid VS scheme under practical settings that include external disturbances.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Electrical & Computer Engr

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2022

Author dc:creator

• Albeladi, Ali

Contributors dc:contributor

• Krishnan, Girish
• Hutchinson, Seth
• Belabbas, Mohamad-Ali
• Kim, Joohyung

• Robotics
• Soft Robotics
• Visual servoing
• Control

Statement dc:rights

• Copyright 2022 Ali Albeladi

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/116106

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F116106