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Surgical robot research is driven by the desire of improving surgical outcomes. This paper proposed a Recurrent Neural Network based controller to address two problems: 1) improving control precision, 2) increasing adaptiveness for robot motion (explained in Section I). RNN was adopted in this work mainly because 1) the problem formulation naturally matches RNN structure, 2) RNN has advantages as...
Minimally Invasive Surgery (MIS) can reduce iatrogenic injury and decrease the possibility of surgical complications. This paper presents a novel flexible and bendable endoscopic device, “Roboscope”, which delivers two instruments, two miniature scanning fiber endoscopes, and a suction/irrigation port to the operation site through a single portal. Compared with existing bendable and steerable robotic...
Haptic feedback plays a key role in surgeries, but it is still a missing component in robotic Minimally Invasive Surgeries. This paper proposes a dynamic model-based sensorless grip force estimation method to address the haptic perception problem for commonly used elongated cable-driven surgical instruments. Cable and cable-pulley properties are studied for dynamic modeling; grip forces, along with...
In this paper, we propose model of longitudinally loaded cable based on the Bouc-Wen hysteresis model and within the framework of the Duhem operator. By optimizing the 9 hysteresis model parameters with a genetic algorithm, the proposed model is shown to be capable of representing quasi-static response of two different diameter cables, 0.61 mm (thin) and 1.19 mm (thick), used for the RAVEN II surgical...
Cable driven manipulators are popular in surgical robots due to compact design, low inertia, and remote actuation. In these manipulators, encoders are usually mounted on the motor, and joint angles are estimated based on transmission kinematics. However, due to non-linear properties of cables such as cable stretch, lower stiffness, and uncertainties in kinematic model parameters, the precision of...
Cable driven power transmission is popular in many manipulator applications including medical arms. In spite of advantages obtained by removing motors from the mechanism, cable transmission introduces higher non-linearity and more uncertainties such as cable stretch and cable coupling. In order to improve the control precision and robustness of the Raven-II surgical robot, particularly for automation...
Fourier transform is the basic operation between time and frequency domain transformation. As a key operation of digital signal processing system, Fast Fourier Transform (FFT) is widely used in many fields such as communication, biomedical signal processing and image processing, which require a high precision of processed signal. To meet the requirement, the floating point number can be used to improve...
One of the fundamental challenges in robotics is data-association: determining which sensor observations correspond to the same physical object. A common approach is to consider groups of observations simultaneously: a constellation of observations can be significantly less ambiguous than the observations considered individually. The Joint Compatibility Branch and Bound (JCBB) test is the gold standard...
The detection of features from Light Detection and Ranging (LIDAR) data is a fundamental component of feature-based mapping and SLAM systems. Classical approaches are often tied to specific environments, computationally expensive, or do not extract precise features. We describe a general purpose feature detector that is not only efficient, but also applicable to virtually any environment. Our method...
The detection of features from Light Detection and Ranging (LIDAR) data is a fundamental component of feature-based mapping and SLAM systems. Existing detectors tend to exploit characteristics of specific environments: corners and lines from indoor (rectilinear) environments, and trees from outdoor environments. While these detectors work well in their intended environments, their performance in different...
The paper proposed a particle filter based and Wireless Sensor Networks (WSN) aided Simultaneous Localization and Mapping (SLAM) strategy. The proposed method aims at solving two troublesome problems in the traditional particle filter based SLAM algorithms. The first problem is high dimension of question space; and the second one is multi-date association. Firstly, the paper analysed the model of...
The paper proposed a range-based particle filtering localization method for static WSN (Wireless Sensor Network). In the method, every blind node in WSN localizes itself through local distance measurements. So the method is parallel. With the development of hardware, RSSI (Received Signal Strength Indicator) measurements were already hardwired in some chips, and then almost no extra hardware was required...
Analyzing the measuring errors of nodes using received signal strength indications (RSSI) method, such as the parameter error of RSSI theoretical model and the obstacle error between neighbor nodes, this paper proposed a localization algorithm based on analyzing of RSSI errors in wireless sensor networks called ERSSI. Based on the two errors mentioned, it got the online parameter changing method for...
In this paper, a newly vision-based localization method for mobile robots is presented. The cost of hardware for this localization system is very low. Based on background subtraction and optical flow tracking, the presented method overcomes the shortcoming of general background subtracting method- being sensitive to noises and interferences of other mobile objects. Besides, the presented method is...
Energy problem is a key issue for the wireless sensor network with limited batteries. It's a good idea to select a head node for data aggregation to save the energy of data transmitting. However, many algorithms don't consider the whole integrated network situation for head node selection. We proposed an energy efficient head node selection method for data aggregation. In this algorithm, three aspects...
Localization is one of important functions in Wireless Sensor Networks (WSNs). And Data fusion is commonly regarded as an efficient method that can improve precision of localization. The paper proposed a novel method based on nonparametric estimation techniques and Radial Basis Function (RBF) Neural Networks to decrease the indeterminacy of Time Difference of Arrival (TDOA) and Received Signal Strength...
In this paper, a neural network approach named shortest path neural networks (SP-NN) is proposed for real-time on-line path planning. Based on grid-based map and mapping this kind of map to neural networks, this proposed method is capable of generating the globally shortest path from the target position to the start position without collision with any obstacles. The dynamics of each neuron is distinctive...
The paper proposed combining traditional quadtrees and framed-quadtrees with the shunting equation based neural network model to improve the efficiency of path planning. The introduction of quadtrees is used for improving the efficiency of the trajectory generation and enlarging the representation capability of maps, especially in sparse environments. And the introduction of framed-quadtree is used...
Wireless sensor networks is one of important information acquisition methods and also has been a promising technique for many applications in recent years. Node localization is one of challenging and fundamental topics in distributed WSNs research. The goal of this paper is to propose an efficient range-free localization algorithm in WSN. We use directional antennas other than omnidirectional antennas...
Considering on the insufficiency of using different routing algorithms for different applications, we proposed an auto-adaptive routing algorithm for wireless sensor networks. It firstly proposed two different routing methods using for the sensor node's localization and the for the object's localization respectively. When we had obtained the nodes' positions through some localization algorithms, it...
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