35160170
RBF Neural Network Based Shape Control of Hyper-redundant Manipulator with Constrained End-Effector
Hyper-redundant manipulator has more degrees of freedom than the least necessary to perform a given task, thus it has the features of overcoming conventional industrial robot’s limitation to carry out a designated difficult task. When the manipulator carries out the missions such as brushing or writing on a surface, drilling or inspection in a hole, the end-effector of the manipulator usually has both position and orientation requirement. Effective control of the hyper-redundant manipulator with such constrained end-effector is difficult for its redundancy. In this paper, a novel approach based on RBF neural network has been proposed to kinematically control the hyper-redundant manipulator. This technique, using variable regular polygon and RBF neural networks models, is completely capable of solving the control problem of a planar hyper-redundant manipulator with any number of links following any desired direction and path. With the shape transformation of variable regular polygon, the manipulator’s configuration changes accordingly and moves actively to perform the tasks. Compared with other methods to our knowledge, this technique has such superiorities as fewer control parameters and higher precision. Simulations have demonstrated that this control technique is available and effective.
206600042
Multiagent Reinforcement Learning for a Planetary Exploration Multirobot System
In a planetary rover system called “SMC rover”, the motion coordination between robots is a key problem to be solved. Multiagent reinforcement learning methods for multirobot coordination strategy learning are investigated. A reinforcement learning based coordination mechanism is proposed for the exploration system. Four-robot climbing a slope is studied in detail as an instance. The actions of the robots are divided into two layers and realized respectively, which simplified the complexity of the climbing task. A Q-Learning based multirobot coordination strategy mechanism is proposed for the climbing mission. An OpenGL 3D simulation platform is used to verify the strategy and the learning results.
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.
21075507
BP Networks Based Trajectory Planning and Inverse Kinematics of a Reconfigurable Mars Rover
The inverse kinematics of series manipulators presents an inherent complexity due to their various structures and kinematics constraints. To a novel reconfigurable Mars rover's arm, the inverse kinematics had been solved by numerical method combined with space geometry relations, but the complex calculating process can not be utilized in real-time control. In this paper, some actions in common use are programmed in the child rover, and the BP neural network was used to solve the inverse kinematics and trajectory planning. To a desired trajectory, some solutions by direct kinematics algorithms and geometry relations corresponding to the trajectory were used as the BP network's training data set, and the non-linear mapping from joint-variable space to operation-variable space was obtained with iterative training and learning method. The results show the output of training is consistent with the desired path, and the obtained reference trajectory is smooth.
152660
Gaits-transferable CPG controller for a snake-like robot
With slim and legless body, particular ball articulation, and rhythmic locomotion, a nature snake adapted itself to many terrains under the control of a 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 (BCI) CPG model is applied for the first time to imitate the pattern generation for the locomotion control of the snake-like robot, and its characteristics are discussed, particularly for the generation of three kinds of rhythmic locomotion. Moreover, we introduce the neuron network organized by the BCI-CPGs connected in line with unilateral excitation to switch automatically locomotion pattern of a snake-like robot under different commands from the higher level control neuron and present a 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.
14083445
Network-based reconfiguration routes for a self-reconfigurable robot
This paper presents a network-based analysis approach for the reconfiguration problem of a self-reconfigurable robot. The self-reconfigurable modular robot named “AMOEBA-I” has nine kinds of non-isomorphic configurations that consist of a configuration network. Each configuration of the robot is defined to be a node in the weighted and directed configuration network. The transformation from one configuration to another is represented by a directed path with nonnegative weight. Graph theory is applied in the reconfiguration analysis, where reconfiguration route, reconfigurable matrix and route matrix are defined according to the topological information of these configurations. Algorithms in graph theory have been used in enumerating the available reconfiguration routes and deciding the best reconfiguration route. Numerical analysis and experimental simulation results prove the validity of the approach proposed in this paper. And it is potentially suitable for other self-reconfigurable robots’ configuration control and reconfiguration planning.
12820927
A Policy Language for Adaptive Web Services Security Framework
How to resolve access control on Web services has become one of the important research tasks about the application of Web services. In this paper, we propose a policy based adaptive Web services security framework. In this framework, the ReiT policy is a declarative language based on the rules and ontologies. The non-structural knowledge is represented by rules and the structural temporal knowledge is represented by ontology. Moreover, we propose a mixer reasoning mechanism to evaluate the ReiT policy. The access control policy including the context of the user and Web services is evaluated by the reasoner. According to the evaluated result agent authorizes the access control of the Web services.
33522682
Influence of connecting units' thicknesses on tandem organic devices' performances
The present work investigates the influence of the Alq"3:Mg and MoO"3 thicknesses in the connecting unit on the performance of tandem organic light-emitting devices (OLEDs). By systematically varying the Alq"3:Mg and MoO"3 thicknesses, we obtained a higher current efficiency of 37.3cd/A for a device with 30nm Alq"3:Mg and 3nm MoO"3 layer as connecting units. The optimal device performance is enhanced by at least 14%, compared with those of devices we fabricated in this paper. It suggests that appropriate Alq"3:Mg and MoO"3 thicknesses can enhance the charge generating ability for connecting units. On the other hand, it was found that the charge transporting layer would decrease strongly because of much thicker or thinner MoO"3 thicknesses. The results demonstrate that it is an effective method to improve the performance of OLEDs by using a optimal thickness for Alq"3:Mg and MoO"3 layers.
29928622
An event-state approach to handling temporal queries in an extended relational database system
Many database applications such as office information systems, computer aided design and manufacturing require temporal support. The requirement for storing all the historical information has resulted in performance and storage problems. This paper presents and illustrates an event-state approach to effectively and efficiently store and retrieve temporal data. This model demonstrates that by using the events that have occurred, facilities can be constructed that would permit the processes of 'rolling-back' to a historical state and/or 'rolling-forward' to the then current state. The proposed temporal features have been incorporated in a relational database system. Processing efficiency and temporal query capabilities have been illustrated and discussed.<<ETX>>
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.
36321696
Adaptive Notch Filter Control for the Torsion Vibration in Lead-Screw Feed Drive System Based on Neural Network
In the machining process, the nature vibration characteristics of feed drive frames are in time-varying. The nature frequencies of the torsion vibration in the lead-screw feed drives are notably changed with the table position, which further influences the stability of servo control. Building the model of servo drive system under consideration of torsion vibration of rotor-screw system, this paper analyzes the effect of changing torsion vibration modes on the notch filter control. The results display that the compensation using the conventional notch filter cannot suppress those disturbances induced by the time-varying vibration modes. Therefore, an adaptive notch filter control method is presented based on an improved BP neural network in this paper. A case study demonstrates that the parameters of the notch filter can be adjusted rightly according to the different table position. Consequently, the torsion vibration modes are compensated efficiently for the time-varying table positions.