45662594
Neural Network Based Kinematic Control of the Hyper-Redundant Snake-Like Manipulator
In a sinusoid like curve configuration, the snake-like manipulator (also called snake arm) has a wide range of potential applications for its redundancy to overcome conventional industrial robot's limitation when carrying out a complex task. It can perform many kinds of locomotion like the nature snake or the animal's tentacle to avoid obstacles, follow designated trajectories, and grasp objects. Effectively control of the snake-like manipulator is difficult for its redundancy. In this study, we propose an approach based on BP neural network to kinematic control the hyper-redundant snake-like manipulator. This approach, inspired by the Serpenoid curve and the concertina motion principle of the nature snake, is completely capable of solving the control problem of a planar snake-like manipulator with any number of links following any desired direction and trajectory. With shape transformation and base rotation, the manipulator's configuration changes accordingly and moves actively to perform the designated tasks. By using BP neural networks in modeling the inverse kinematics, this approach has such superiorities as few control parameters and high precision. Simulations have demonstrated that this control technique for the snake-like manipulator is available and effective.
206544153
Analysis of creeping locomotion of a snake robot on a slope
Biological snakes' diverse locomotion modes and physiology make them supremely adapted for environment. To realize these snakes' noticeable features, we have developed a snake-like robot that has no any forward direction driving force. To enlarge the environment-adaptable ability of our robot, in this study we discuss the creeping locomotion of our snake-like robot on a slope. A computer simulator is presented for analysis of the creeping locomotion of our snake-like robot on a slope, and the environment-adaptable body shape for the creeping locomotion of the snake-like robot on slope is also derived through this simulator.
15225105
Development of an OpenGL based multi-robot simulating platform
Simulating platform plays a crucial role in multi-robot research, as a tool to quickly and efficiently test new concepts, strategies, and algorithms. In this paper, OpenGL and Visual C++ based modeling method has been discussed in detail to establish a platform for the 3D simulating of multi-link mobile robots and their collaboration. The cooperation strategies, the control methods and the communication mechanisms of multi-robot system can be explored and verified by this platform. A planetary robot system is used to verify the validity of the developed platform. The effectiveness and the practicability of the platform are successfully demonstrated by a four-robot motion coordination simulating results.
15051068
Study on Epipolar Geometry Restoration of Parallel Binocular Vision
The epipolar geometry restoration offline and online for the parallel binocular vision system were discussed in this paper. For the former, a method that uses a common calibration board to realize automatic and accurate matching of the image corner pairs was developed; for the latter, a connotative constraint was proved by geometry theory and the corresponding cross-correlation sparse matching algorithm was introduced. The constraint was that for any space point projected into two images, the horizontal coordinate in the left image of this point is bigger than that in the right image. Standard and real images experiments were made for each kind of problem and the results show that the algorithms for each problem were valid.
16248308
Locomotion of a Snake-like Robot Controlled by the Bidirectional Cyclic Inhibitory CPG Model
With slim and legless body, particular ball articulation and rhythmic locomotion, a nature snake adapted itself to many terrains under the control of neuron system. Based on analyzing the locomotion mechanism, the main functional features of the motor system in snakes are specified in detail. Furthermore, a bidirectional cyclic inhibitory CPG model is firstly applied to imitate the pattern generation for the locomotion control of the snake-like robot, and its characteristic is discussed particularly for the generation of different kinds rhythmic locomotion. Besides, we introduce the neuron network organized by the bidirectional cyclic inhibitory CPGs connected in line with unilateral excitation to switch automatically locomotion pattern of a snake-like robot under different command from high level neuron, and present the necessary condition for the CPG neuron network to sustain a rhythmic output. The validity for the generation of different kinds of rhythmic locomotion modes by the CPG network are verified by the dynamic simulations and experiments. This research provided a new method to model the generation mechanism of the rhythmic pattern of the snake.
18568161
Analysis of locomotion of a planetary rover on a slope
For improvement of planetary rover locomotion, a new type of tracked vehicle was designed for planetary exploration based on the concept of reconfigurability. By applying the planetary wheel principle, the system can give output in different forms under various constraint conditions. As a reconfigurable module, the vehicle can be integrated into a larger system using two basic configurations. As a stand-alone rover, it has two moving modes. The whole design was analyzed by conducting dynamics simulations and experiments.
30151415
Genetic Algorithm Based-On the Quantum Probability Representation
A genetic algorithm based on the quantum probability representation (GAQPR) is proposed, in which each individual evolves independently; a new crossover operator is designed to integrate searching processes of multiple individuals into a more efficient global searching process; a new mutation operator is also proposed and analyzed. Optimization capability of GAQPR is studied via experiments on function optimization, results of experiments show that, for multi-peak optimization problem, GAQPR is more efficient than GQA[4].
13209883
An Approach for Dynamic Hardware /Software Partitioning Based on DPBIL
HW/SW partitioning is one of the key steps in high-level modeling of embedded system co-design. In some kinds of data-dependence applications, the partitioning environment is dynamic. An approach based on a kind of evolutionary algorithm called DPBIL is proposed for dynamic HW/SW partitioning problem. DPBIL is inspired by Dualism or complementarity in the nature and operates on a pair of dual probability vectors to adapt itself to the dynamic environment. We systematically constructed the system model, defined the dynamic environment and conducted experimental analysis via comparison with the existing method. To enhance the exploration performance of DPBIL, a threshold constraint mechanism is introduced. Experimental results indicated that our approach is feasible and efficient for dynamic HW/SW partitioning, and has much better performance than traditional ones.
2728620
Mixture-based bone segmentation and its application in computer aided diagnosis and treatment planning
The developmental dysplasia of the hip (DDH) is a congenital malformation of the acetabulum with different degrees. Leaving it partially treated or untreated is a major cause of disability. In this paper, we implemented a computer-aided system for the diagnosis and treatment planning of DDH. Instead of labeling each voxel with a unique label or tissue type, the percentage of different tissues within each voxel, which we call a mixture, was considered in establishing a maximum a posterior probability (MAP) framework for more accurate segmentation. A graphical module displayed the segmented bone in a 3D view to show the spatial relationship between acetabulum and femurs. This virtual image, together with quantitative measurement indexes, helps physicians make diagnosis. The system also provides physicians a virtual treatment planning on screen for acetabular and femoral osteotomy. It is currently under clinical trial on patient datasets.
11266206
A robust incremental learning framework for accurate skin region segmentation in color images
In this paper, we propose a robust incremental learning framework for accurate skin region segmentation in real-life images. The proposed framework is able to automatically learn the skin color information from each test image in real-time and generate the specific skin model (SSM) for that image. Consequently, the SSM can adapt to a certain image, in which the skin colors may vary from one region to another due to illumination conditions and inherent skin colors. The proposed framework consists of multiple iterations to learn the SSM, and each iteration comprises two major steps: (1) collecting new skin samples by region growing; (2) updating the skin model incrementally with the available skin samples. After the skin model converges (i.e., becomes the SSM), a post-processing can be further performed to fill up the interstices on the skin map. We performed a set of experiments on a large-scale real-life image database and our method observably outperformed the well-known Bayesian histogram. The experimental results confirm that the SSM is more robust than static skin models.
26704614
A New Approach to Forecast Crude Oil Price Based on Fuzzy Neural Network
A new oil price forecasting model based on fuzzy neural network, which combings RBF neural network, Markov chain based semi parametric model and wavelet analysis is proposed in this paper. The high degree of prediction accuracy confirms the performance of the model in accurate forecasts, reinforcement learning properties and mapping capabilities.