This paper presents the design, characterization, and testing of a steerable needle robot for minimally invasive neurosurgery. The robot consists of a rigid outer tube and two telescopic tendon-driven steerable tubes. Through the rotation, translation, and bending of individual tubes, this telescopic tendon-driven needle robot can perform dexterous motion and follow the path of the tip. We presented the design of the needle robot and its actuation system, modeling of the robotic kinematics, characterization of the robot motion, results of the open-loop kinematic control, and demonstration of the follow-the-leader motion. The position error of the robot tip is 0.92 mm, and follow-the-leader motion error is 1.1 mm. Due to its small footprint and unique motion ability, the robot has the potential to be manipulated inside human brain and used for minimally invasive neurosurgery.

This paper presents the development of a fiber Bragg grating (FBG) bending sensor for shape memory alloy (SMA) bending modules. Due to the small form factor, low cost, and large-deflection capability, SMA bending modules can be used to construct disposable surgical robots for a variety of minimally invasive procedures. To realize a closed-loop control of SMA bending modules, an intrinsic bending sensor is imperative. Due to the lack of bending sensors for SMA bending modules, we have developed an FBG bending sensor by integrating FBG fibers with a superelastic substrate using flexible adhesive. Since the substrate is ultra-thin and adhesive is flexible, the sensor has low stiffness and can measure large curvatures. Additionally, due to the orthogonal arrangement of the sensor/actuator assembly, the influence of temperature variation caused by SMA actuation can be compensated. The working principle of the developed sensor was modeled followed by simulations. After experimentally evaluating the developed model, the sensor was integrated with an SMA bending module and cyclically bi-directionally deflected. The experimental results proved the relatively high measurement accuracy, high repeatability, and large measurable curvatures of the sensor, although hysteresis was observed due to friction.

Robotic artificial muscles are a subset of artificial muscles that are capable of producing biologically inspired motions useful for robot systems, i.e., large power-to-weight ratios, inherent compliance, and large range of motions. These actuators, ranging from shape memory alloys to dielectric elastomers, are increasingly popular for biomimetic robots as they may operate without using complex linkage designs or other cumbersome mechanisms. Recent achievements in fabrication, modeling, and control methods have significantly contributed to their potential utilization in a wide range of applications. However, no survey paper has gone into depth regarding considerations pertaining to their selection, design, and usage in generating biomimetic motions. In this paper, we discuss important characteristics and considerations in the selection, design, and implementation of various prominent and unique robotic artificial muscles for biomimetic robots, and provide perspectives on next-generation muscle-powered robots.

This paper presents the development of a skull-mounted robotic headframe for intracranial neurosurgery. The headframe is primarily comprised of a pedestal, a Stewart platform, and a linear actuation module interfacing with a mesoscale neurosurgical robot. In addition to kinematic modeling, motion planning is carried out for neurosurgical procedures using the headframe. Due to the symmetric neurosurgical robot and the insertion and retraction motion of the linear actuation module, the headframe includes kinematic redundancy and it is utilized to enhance the positioning accuracy of the system. In this paper, several simulations are conducted to evaluate the headframe workspace, followed by an experimental study of the positioning error of the neurosurgical robot and a proof-of-concept demonstration of the headframe interfacing with the neurosurgical robot.

This paper presents the design, modeling, and control of a meso-scale torsion actuator based on shape memory alloy (SMA) for image-guided surgical procedures. Developing a miniature torsion actuator is challenging, but it opens the possibility of significantly enhancing the robot agility and maneuverability. The proposed torsion actuator is bi-directionally actuated by a pair of antagonistic SMA torsion springs through alternate Joule heating and natural cooling. The torsion actuator is integrated into a surgical robot prototype to demonstrate its working performance in the humid environment under C-Arm CT image guidance.

This paper presents a prototype of a meso-scale medical robot for Neurosurgical Intracerebral Hemorrhage Evacuation (NICHE). We use shape memory alloy (SMA) actuators for actuating individual degrees-of-freedom (DoFs). Pairs of antagonistic SMA wires enable bidirectional motion of bending joints. A novel torsion joint formed by a pair of antagonistic SMA torsion springs is installed at the distal end together with a pair of antagonistic SMA wires. This design provides articulation within the hemorrhage at the robot tip, thereby enabling effective hemorrhage evacuation. In this paper we derive the forward kinematics of the robot and the constitutive model of the SMA-actuated torsion joint. We also present experimental procedures to characterize parameters of the constitutive model. Finally, a proof-of-concept experiment is presented to show the working of the robot.

This paper presents our work on design, modeling, and control of a meso-scale shape memory alloy (SMA) actuated torsion actuator for miniature robots. This novel torsion actuator is bi-directionally activated by a pair of antagonistic SMA torsion springs through alternate Joule heating. First, we present the mechanical design and fabrication approach of this miniature actuator. Then, we present the torsion spring mechanical model, SMA constitutive model, and kinematic and dynamic models, followed by the derivation of the quasi-static model of the actuator. Finally, the dynamic properties are tested by step response and sinusoidal tracking tests. A model-based controller is designed and its performance is evaluated by experiments.

The objective of this paper is to propose an approach for generating on-line walking patterns for a biped robot to navigate towards destination and reactively avoid obstacles. Kinect-based sensor system detects obstacles with velocity and dimension estimated for obstacle modeling. With identified obstacles, repulsion vectors of potential field given by obstacles modify walking path of a biped robot. On-line path generation (OPG) is proposed to generate walking path towards a target configuration including position and orientation. With reference mapping combined with walking pattern generator based on zero momentum point (ZMP), reactive walking patterns free of obstacle collision are thus created. The effectiveness of this method is confirmed on a biped robot developed in our lab, and the robot is capable of avoiding obstacles while maintaining ZMP stability. The contribution of this paper is to propose OPG for obstacle avoidance of biped robots by extending potential field method. The Demonstration of the proof of concept has been successfully conducted.

The objective of this paper is to develop a method of walking pattern generation with non-constant body height towards quasi natural walking. Unlike the traditional LIPM or GCIPM based method, our proposed method divides the walking cycle into 3 parts, including lowering down, rising up and inverted pendulum process, all described by specific trajectory equations. After modelling the dynamics of the biped walking robot with three mass points and properly designing ZMP reference, smooth spatial hip trajectory with non-constant height is obtained, as well as joint trajectories solved by inverse kinematics. The stability of biped walking pattern is verified through simulation and actual walking experiments on a biped robot developed in our lab.