Abstract

The robotic platform presented in this thesis is underdamped with high inertia and can store potential energy in its compliant joints, making it ideal for many different types of real-world tasks for which traditional rigid-robots are ill-suited. Some real-world tasks suited to soft robots include high impact tasks like hammering a nail into a wall or moving heavy objects with other robot or human teammates while using relatively little power. Some of the same characteristics which make soft robots useful, such as their underdamped, highly compliant joints, also make motion planning and control for soft robots difficult. In this thesis, a novel method is introduced to take high-level, real world tasks and generate trajectories for soft robots to complete those real world tasks. The generated trajectories are designed to be dynamically and kinematically feasible for a soft robot. An optimization is introduced that uses a cost function to move the tip of a robot from point A to point B in task space. Experiments conducted in simulation and on hardware show that a soft robot with a length of 1.19 meters is able to track a high-speed and dynamic trajectory generated with this optimization with a reported median magnitude of error of 0.032 meters between the planned and actual end effector trajectories. This thesis also introduces an adaptation of a Model Reference Adaptive Controller (MRAC) that causes a high degree of freedom and nonlinear soft robot to behave like a 2nd-order, critically damped system. This allows us to approximate the dynamics of the robot as 2nd-order to more easily generate trajectories for highly dynamic tasks like throwing a ball. An optimization is developed to generate ball-throwing trajectories. Experiments conducted in simulation and on hardware show that a soft robot can throw a ball within 0.13 meters of a goal point in simulation (where the goal point is 2.85 meters from the robot) and 0.5 meters of a goal point on hardware (where the goal point is 2 meters from the robot). The methods developed in this thesis enable soft robots to more easily complete high-level tasks through dynamically and kinematically feasible task and joint space trajectory generation.

Degree

MS

College and Department

Ira A. Fulton College of Engineering; Mechanical Engineering

Rights

https://lib.byu.edu/about/copyright/

Date Submitted

2023-12-08

Document Type

Thesis

Handle

http://hdl.lib.byu.edu/1877/etd13445

Keywords

trajectory planning, control, soft robotics, trajectory optimization, dynamic constraints

Language

english

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