Strain-based non-intrusive approaches for measuring the pressure of pipes have attracted widespread attention due to their great convenience and ability to avoid destroying the integrity of structures. However, the mentioned method usually measures the dynamic pressure based only on the static strain–pressure sensitivity coefficients (SSSCs) instead of the dynamic strain–pressure sensitivity coefficients (DSSCs) due to its complicated calibration, which will inevitably affect the accuracy significantly. To address this issue, a model-driven scheme with dual stages is proposed in the present study to compensate the dynamic pressure measurement. The DSSCs are analytically derived for the first time from the axial governing equations of the pipe, considering the general boundary conditions for the thin-walled and thick-walled pipes simultaneously. In the first stage, the physical parameters involved in the DSSCs are calibrated by minimizing the residual of the experimental results and the theoretical counterparts. In the second stage, the DSSCs calculated from the calibrated analytical model are utilized to compensate the dynamic pressure based on the relationship between the DSSCs and the SSSCs. The proposed method is applied to an industrial hydraulic pipe system, and the experimental results show that the relative error is reduced greatly after the compensation is implemented, demonstrating the validity of the proposed compensation method.

Brain tumor, be it primary or metastatic, is usually life threatening for a person of any age. Primary surgical resection is one of the most effective ways of treating brain tumors and can have a tremendously increased success rate if the appropriate imaging modality is used for complete tumor resection. Magnetic resonance imaging (MRI) is the imaging modality of choice for brain tumor imaging because of its excellent soft-tissue contrast. MRI combined with continuum soft robotics has immense potential to be the next major technological breakthrough in the field of brain cancer diagnosis and therapy. In this paper, we present the design, kinematic, and force analysis of a flexible spring-based minimally invasive neurosurgical intracranial robot. It is comprised of an interconnected inner spring and an outer spring and is connected to actively cooled shape memory alloy (SMA) spring actuators via tendon driven mechanism. Our robot has three serially connected 2-DoF segments, which can be independently controlled due to the central tendon routing configuration. The kinematic and force analysis of the robot and the independent segment control were verified by experiments. Robot motion under forced cooling of SMA springs was evaluated as well as the MRI compatibility of the robot and its motion capability in brain-like gelatin environment.

Deep intracranial tumor removal can be achieved if the neurosurgical robot has sufficient flexibility and stability. Toward achieving this goal, we have developed a spring-based continuum robot, namely a minimally invasive neurosurgical intracranial robot (MINIR-II) with novel tendon routing and tunable stiffness for use in a magnetic resonance imaging (MRI) environment. The robot consists of a pair of springs in parallel, i.e., an inner interconnected spring that promotes flexibility with decoupled segment motion and an outer spring that maintains its smooth curved shape during its interaction with the tissue. We propose a shape memory alloy (SMA) spring backbone that provides local stiffness control and a tendon routing configuration that enables independent segment locking. In this paper, we also present a detailed local stiffness analysis of the SMA backbone and model the relationship between the resistive force at the robot tip and the tension in the tendon. We also demonstrate through experiments, the validity of our local stiffness model of the SMA backbone and the correlation between the tendon tension and the resistive force. We also performed MRI compatibility studies of the three-segment MINIR-II robot by attaching it to a robotic platform that consists of SMA spring actuators with integrated water cooling modules.

Since shape memory alloy has a high power density and is magnetic resonance imaging compatible, it has been chosen as the actuator for the meso-scale minimally invasive neurosurgical intracranial robot (MINIR-II) that is envisioned to be operated under continuous magnetic resonance imaging guidance. We have devised a water cooling strategy to improve its actuation frequency by threading a silicone tube through the spring coils to form a compact cooling module-integrated actuator. To create active bi-directional motion in each robot joint, we configured the shape memory alloy springs in an antagonistic way. We modeled the antagonistic shape memory alloy spring behavior and provided the detailed steps to simulate its motion for a complete cycle. We investigated the heat transfer during the resistive heating and water cooling processes. Characterization experiments were performed to determine the parameters used in both models, which were then verified by comparing the experimental and simulated data. The actuation frequency of the antagonistic shape memory alloys was evaluated for several motion amplitudes and we could achieve a maximum actuation frequency of 0.143 Hz for a sinusoidal trajectory with 2 mm amplitude. Lastly, we developed a robotic system to implement the actuators on the MINIR-II to move its end segment back and forth for approximately ±25°.

Continuum robotic manipulators are increasingly adopted in minimal invasive surgery. However, their nonlinear behavior is challenging to model accurately, especially when subject to external interaction, potentially leading to poor control performance. In this letter, we investigate the feasibility of adopting a model-free multiagent reinforcement learning (RL), namely multiagent deep Q network (MADQN), to control a 2-degree of freedom (DoF) cable-driven continuum surgical manipulator. The control of the robot is formulated as a one DoF, one agent problem in the MADQN framework to improve the learning efficiency. Combined with a shielding scheme that enables dynamic variation of the action set boundary, MADQN leads to efficient and importantly safer control of the robot. Shielded MADQN enabled the robot to perform point and trajectory tracking with submillimeter root mean square errors under external loads, soft obstacles, and rigid collision, which are common interaction scenarios encountered by surgical manipulators. The controller was further proven to be effective in a miniature continuum robot with high structural nonlinearitiy, achieving trajectory tracking with submillimeter accuracy under external payload.

This paper presents the use of shape memory alloy (SMA) spring actuators with real-time cooling to control the motion of the minimally invasive neurosurgical intracranial robot (MINIR-II). A new actuation mechanism involving the passage of water as the cooling medium and air as the medium to drive out the water has been developed to facilitate real-time control of the springs. Control parameters, such as current, water flow rates, SMA predisplacement, and gauge pressure of the compressed air, are identified from the SMA thermal model and from the actuation mechanism. In-depth modeling and characterization have been performed regarding these parameters to optimize the robot motion speed. Forced water cooling has also been compared with forced air cooling and proved to be the superior method to achieve higher robot speed. An improved robot design and a magnetic-resonance-imaging-compatible experimental platform have been developed for the implementation of the actuation mechanism.