In robotic needle steering, flexible asymmetric-tip needles can steer around obstacles to reach targets deep within tissue. Due to tissue inhomogeneity and needle flexibility, needle buckling can occur, preventing accurate placement. This paper focuses on detecting needle buckling using axial force and needle-tip position readings from sensors. Our algorithm uses errors between the force readings and a predictive force model generated from those readings to track rapid changes in the measured forces. Using this prediction error and needle-tip position, the algorithm detects unexpected force increase, strict needle buckling, and buckling with sliding events at the needle-tip. The metrics for the detections are derived using a standard three-sigma rule and a sigmoid function to ensure generalizability of this method to a variety of tissue types. Our algorithm was tested using insertions into a gelatin tissue with an embedded rectangular obstacle designed to elicit buckling events. Needle buckling was detected at a maximum of 2[Formula: see text]mm after collision with the obstacle. Our algorithm was tested for robustness with insertions in an ex vivo tissue under different boundary conditions. Our algorithm was also able to detect buckling events 1–2[Formula: see text]s sooner than human detection times, showing significance for future autonomous control.
Safety critical events in robotic applications can often be characterized by forces between the robot end-effector and the environment. One application in which safe interaction between the robot and environment is critical is in the area of medical robots. In this paper, we propose a novel Compact Form Dynamic Linearization Model-Free Prediction (CFDL-MFP) technique to predict future values of any time-series sensor data, such as interaction forces. Existing time series forecasting methods have high computational times which motivates the development of a novel technique. Using Autoregressive Integrated Moving Average (ARIMA) forecasting as benchmark, the performance of the proposed model was evaluated in terms of accuracy, computation efficiency, and stability on various force profiles. The proposed algorithm was 11% more accurate than ARIMA and maximum computation time of CFDL-MFP was 4ms, compared to ARIMA (7390ms). Furthermore, we evaluate the model in the special case of predicting needle buckling events, before they occur, by using only axial force and needle-tip position data. The model was evaluated experimentally for robustness with steerable needle insertions into different tissues including gelatin and biological tissue. For a needle insertion velocity of 2.5mm/s, the proposed algorithm was able to predict needle buckling 2.03s sooner than human detections. In biological tissue, no false positive or false negative buckling detections occurred and the rates were low in artificial tissue. The proposed forecasting model can be used to ensure safe robot interactions with delicate environments by predicting adverse force-based events before they occur.
Robot-assisted minimally invasive surgery is an emerging technology where the incision needle is operated by a robot manipulator to assist surgeons in performing interventional procedures such as biopsy and brachytherapy. Most robotic systems previously designed for needle interventions are stand-alone and operate in coplanar fashion, which require external mechanisms such as robot arms to align the needle onto the target tissue plane. In this work, we design a portable and light-weight needle steering platform that connects as an end-effector to a 6 degree-of-freedom industrial robot arm such as a FANUC robot. Standard FANUC operating functions would be used to control the motion of the end-effector and insert needles into the target tissues. Simulated gelatin tissues are used to perform needle insertions, and the performance of the end-effector is tested by changing the position and orientation of the tissue platforms. Finally, the proposed system will be tested for scalability by integrating with other industrial robot arms such as Yaskawa.
Minimally invasive surgeries use small incisions through needles for operations to be conducted from outside the patient's body. Therefore, an accurate map of the distribution of tissues in real-time is critical to ensure patient safety. In this work, we explore all optical sensing methods as simple, fast, and economic alternatives to commercial imaging modalities. Simulated tissues have been prepared using gelatin to conduct optical characterization experiments. Transmission and fluorescence spectra on homogenous and heterogenous gelatin with different concentrations would be reported, with a focus on developing an optoelectronic technique for mapping of tissue distribution. Finally, this technique would be validated through real-time needle insertion experiment into a gelatin sample to track the spectral data of the tissue environments. This work could help track biological tissues where the spectral data could help surgeons visualize the needle-tissue environments in real-time.
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