Date of Award
5-2022
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Electrical and Computer Engineering
Committee Chair/Advisor
Ian Walker
Committee Member
Richard Groff
Committee Member
Jacob Sorber
Committee Member
Yongkai Wu
Abstract
Continuum robots offer unique potential benefits for environmental exploration, notably in using their maneuverability to navigate congested environments. In this dissertation, we show how circumnutation, a motion strategy, commonly employed by plants, can be implemented, and usefully exploited, with continuum robots. We discuss how the kinematics of circumnutation, which combines local backbone growth with periodic backbone bending, can be created using extensible continuum robot hardware. The underlying kinematics are generated by adapting kinematic models of plant growth. We illustrate the effectiveness of that approach with experimental results with a tendril robot exploring a congested environment. However, significant challenges remain in environmental sensing using continuum structures, within which space for local sensing is often extremely limited. In this dissertation, we discuss the use of novel impulsive interaction, i.e. active tapping, using continuum robots to sense and identify features within their environment. We introduce an impact model-based tapping approach for environmental feature detection with continuum robots which does not require the addition of specialized sensors, and demonstrate its utility in hardware. We contrast the method to two alternative approaches to contact detection. The methods are compared empirically on two different types of continuum robot hardware, the pneumatically actuated ``OctArm'' and a tendon actuated ``Tendril''. The results identify relative strengths of the approaches. The impact model based approach is shown to include information not accessible to the other approaches.
Recommended Citation
Wooten, Michael Benjamin, "Environmental Exploration with Long, Thin Tendril Robots, or Building Plant Based Robots and Teaching Them How to Feel" (2022). All Dissertations. 3020.
https://open.clemson.edu/all_dissertations/3020
Author ORCID Identifier
0000-0002-3861-0344