Haptic feedback can provide operators of hand-held robots with active guidance during challenging tasks and with critical information on environment interactions. Yet for such haptic feedback to be effective, it must be lightweight, capable of integration into a hand-held form factor, and capable of displaying easily discernible cues. We present the design and evaluation of HaPPArray - a haptic pneumatic pouch array - where the pneumatic pouches can be actuated alone or in sequence to provide information to the user. A 3x3 array of pouches was integrated into a handle, representative of an interface for a hand-held robot. When actuated individually, users were able to correctly identify the pouch being actuated with 86% accuracy, and when actuated in sequence, users were able to correctly identify the associated direction cue with 89 % accuracy. These results, along with a demonstration of how the direction cues can be used for haptic guidance of a medical robot, suggest that HaPPArray can be an effective approach for providing haptic feedback for hand-held robots.

Concentric tube robots (CTRs) show particular promise for minimally invasive surgery due to their inherent compliance and ability to navigate in constrained environments. Due to variations in anatomy among patients and variations in task requirements among procedures, it is necessary to customize the design of these robots on a patient- or population-specific basis. However, the complex kinematics and large design space make the design problem challenging. In this article, we propose a computational framework that can efficiently optimize a robot design and a motion plan to enable safe navigation through the patient’s anatomy. The current framework is the first fully gradient-based method for CTR design optimization and motion planning, enabling an efficient and scalable solution for simultaneously optimizing continuous variables, even across multiple anatomies. The framework is demonstrated using two clinical examples, laryngoscopy and heart biopsy, where the optimization problems are solved for a single patient and across multiple patients, respectively.