Purpose The minimally invasive surgery (MIS) has shown advantages when compared to traditional surgery. However, there are two major challenges in the MIS technique: the limited field of view (FOV) and the lack of depth perception provided by the standard monocular endoscope. Therefore, in this study, we proposed a New Endoscope for Panoramic-View with Focus-Area 3D-Vision (3DMISPE) in order to provide surgeons with a broad view field and 3D images in the surgical area for real-time display. Method The proposed system consisted of two endoscopic cameras fixed to each other. Compared to our previous study, the proposed algorithm for the stitching videos was novel. This proposed stitching algorithm was based on the stereo vision synthesis theory. Thus, this new method can support 3D reconstruction and image stitching at the same time. Moreover, our approach employed the same functions on reconstructing 3D surface images by calculating the overlap region's disparity and performing image stitching with the two-view images from both the cameras. Results The experimental results demonstrated that the proposed method can combine two endoscope's FOV into one wider FOV. In addition, the part in the overlap region could also be synthesized for a 3D display to provide more information about depth and distance, with an error of about 1 mm. In the proposed system, the performance could achieve a frame rate of up to 11.3 fps on a single Intel i5-4590 CPU computer and 17.6 fps on a computer with an additional GTX1060 Nvidia GeForce GPU. Furthermore, the proposed stitching method in this study could be made 1.4 times after when compared to that in our previous report. Besides, our method also improved stitched image quality by significantly reducing the alignment errors or "ghosting" when compared to the SURF-based stitching method employed in our previous study. Conclusion The proposed system can provide a more efficient way for the doctors with a broad area of view while still providing a 3D surface image in real-time applications. Our system give promises to improve existing limitations in laparoscopic surgery such as the limited FOV and the lack of depth perception.

Minimally invasive surgery (MIS) minimizes the surgical incisions that need to be made and hence reduces the physical trauma involved during the surgical process. The ultimate goal is to reduce postoperative pain and blood loss as well as to limit the scarring area and hence accelerate recovery. It is therefore of great interest to both the surgeon and the patient. However, a major problem with MIS is that the field of vision of the surgeon is very narrow. We had previously developed and tested an MIS panoramic endoscope (MISPE) that provides the surgeon with a broader field of view. However, one issue with the MISPE was its low rate of video stitching. Therefore, in this paper, we propose using the region of interest in combination with the downsizing technique to improve the image-stitching performance of the MISPE. Experimental results confirm that, by using the proposed method, the image size can be increased by more than 160%, with the image resolution also improving. For instance, we could achieve performance improvements of 10× (CPU) and 23× (GPU) as compared to that of the original method.

The paper addresses a problem of efficiently controlling an autonomous underwater vehicle (AUV), where its typical underactuated model is considered. Due to critical uncertainties and nonlinearities in the system caused by unavoidable external disturbances such as ocean currents when it operates, it is paramount to robustly maintain motions of the vehicle over time as expected. Therefore, it is proposed to employ the hierarchical sliding mode control technique to design the closed-loop control scheme for the device. However, exactly determining parameters of the AUV control system is impractical since its nonlinearities and external disturbances can vary those parameters over time. Thus, it is proposed to exploit neural networks to develop an adaptive learning mechanism that allows the system to learn its parameters adaptively. More importantly, stability of the AUV system controlled by the proposed approach is theoretically proved to be guaranteed by the use of the Lyapunov theory. Effectiveness of the proposed control scheme was verified by the experiments implemented in a synthetic environment, where the obtained results are highly promising.

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This paper proposes a new adaptive controller for three-wheeled mobile robots (3WMRs) called the ABHSMC controller. This ABHSMC controller is developed through a cooperative approach, combining a backstepping controller and a Radial Basis Function (RBF) neural network-based Hierarchical Sliding Mode Controller (HSMC). Notably, the RBF neural network exhibits the remarkable capability to estimate both the uncertainty components of the model and systematically adapt its parameters, leading to enhanced output trajectory responses. A novel navigational model, constructed by the connection to the adaptive BHSMC controller, Timed Elastic Band (TEB) Local Planner, and A-star (A*) Global Planner, is called ABHSMC navigation stack, and it is applied to effectively solve the tracking issue and obstacle avoidance for the 3-Wheeled Mobile Robot (3WMR). The simulation results implemented in the Matlab/Simulink platform demonstrate that the 3WMRs can precisely follow the desired trajectory, even in the presence of disturbances and changes in model parameters. Furthermore, the controller’s reliability is endorsed on our constructed self-driving car model. The achieved experimental results indicate that the proposed navigational structure can effectively control the actual vehicle model to track the desired trajectory with a small enough error and avoid a sudden obstacle simultaneously.