Real-Time Motion Planning for In-Hand Manipulation with a Multi-Fingered Hand

Dexterous manipulation of objects once held in hand remains a challenge. Such skills are, however, necessary for robotics to move beyond gripper-based manipulation and use all the dexterity offered by anthropomorphic robotic hands. One major challenge when manipulating an object within the hand is that fingers must move around the object while avoiding collision with other fingers or the object. Such collision-free paths must be computed in real-time, as the smallest deviation from the original plan can easily lead to collisions. We present a real-time approach to computing collision-free paths in a high-dimensional space. To guide the exploration, we learn an explicit representation of the free space, retrievable in real-time. We further combine this representation with closed-loop control via dynamical systems and sampling-based motion planning and show that the combination increases performance compared to alternatives, offering efficient search of feasible paths and real-time obstacle avoidance in a multi-fingered robotic hand.

Using an Improved Output Feedback MPC Approach for Developing a Haptic Virtual Training System

Haptic training simulators generally consist of three major components, namely a human operator, a haptic interface, and a virtual environment. Appropriate dynamic modeling of each of these components can have far-reaching implications for the whole system's performance improvement in terms of transparency, the analogy to the real environment, and stability. In this paper, we developed a virtual-based haptic training simulator for Endoscopic Sinus Surgery (ESS) by doing a dynamic characterization of the phenomenological sinus tissue fracture in the virtual environment, using an input-constrained linear parametric variable model. A parallel robot manipulator equipped with a calibrated force sensor is employed as a haptic interface. A lumped five-parameter single-degree-of-freedom mass-stiffness-damping impedance model is assigned to the operator's arm dynamic. A robust online output feedback quasi-min-max model predictive control (MPC) framework is proposed to stabilize the system during the switching between the piecewise linear dynamics of the virtual environment. The simulations and the experimental results demonstrate the effectiveness of the proposed control algorithm in terms of robustness and convergence to the desired impedance quantities.

A Virtual-Based Haptic Endoscopic Sinus Surgery (ESS) Training System: from Development to Validation

Simulated training platforms offer a suitable avenue for surgical students and professionals to build and improve upon their skills, without the hassle of traditional training methods. To enhance the degree of realistic interaction paradigms of training simulators, great work has been done to both model simulated anatomy in more realistic fashion, as well as providing appropriate haptic feedback to the trainee. As such, this chapter seeks to discuss the ongoing research being conducted on haptic feedback-incorporated simulators specifically for Endoscopic Sinus Surgery (ESS). This chapter offers a brief comparative analysis of some EES simulators, in addition to a deeper quantitative and qualitative look into our approach to designing and prototyping a complete virtual-based haptic EES training platform.