Model-Based Design and Control of Deformable Robots and Haptic Devices
Margaret Koehler | PhD Thesis Defense
Advisor: Allison Okamura
Haptic interfaces are used in training, guidance, and teleoperation to provide force and tactile feedback to a user from a virtual or remote environment. In contrast to the rigid components typically comprising haptic devices, compliant materials could enable new haptic devices that move via deformation rather than via joints. However, the design and control of devices consisting of soft or deformable components is challenging, due to the complex coupling between actuation and end-effector and a lack of sensing. This thesis considers the potential for computational model-based methods to address challenges in the design and control of deformable haptic devices.
In the first half of this thesis, we introduce a new haptic shape display made of soft materials for organ simulation for medical training. The device is a continuous, fully 3D shape-changing surface that a user can touch and hold. We develop a mass-spring model of the device that allows us to understand how different pneumatic actuators affect the shape. Further, we develop an automated design algorithm based on a heuristic controller and a simulation of the device to determine how to arrange a limited number of actuators to reproduce target shapes. This device and its associated algorithms show how a model can be used to understand complex deformations due to pneumatic actuation of a compliant system.
The second half of this thesis focuses on the precise control of deformable haptic devices. Two deformable kinesthetic haptic devices are introduced: a 2-DOF planar device and a 5-DOF device. In these devices, forces are transmitted from the actuators to the user via deformable transmissions which allow for easy fabrication and reduced mass and friction in the device. Using these devices, we demonstrate methods for design, sensing, and control. We derive a general formula for the mapping of actuator stiffness to end-effector stiffness and verify it using the planar device. For sensing, we present results in model-based sensing using only actuator information or using model-calibrated embedded sensors. With these techniques, we can use the deformation of the device to measure the force applied to the device without an external force sensor. We extend techniques from rigid robot workspace analysis to deformable robots. Finally, we develop control techniques for haptic rendering which compensate for device mechanics and characterize the resulting forces.
Together, these results in design and control show that model-based methods can overcome some of the challenges of deformable devices to enable new haptic interfaces.