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.
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.
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.
5812894
Configuration Analysis for Reconfigurable Modular Planetary Robots Based on MSV and CSM
A reconfigurable modular planetary robot system (RMPRS) consists of the parent body and multiple asymmetric wheel-manipulator child-robot modules. The module, which can independently locomote and manipulate, possesses the orientation of posture and the direction of locomotion. The modules have reconfiguration capability so that a group of the modules can construct a variety of configurations. The aim of the robot reconfiguration is to generate better configuration with respect to the directional locomotion adapted to the environment. Module state vector (MSV) and configuration state matrix (CSM) are presented and constructed for representing the asymmetric module and the configurations, and supporting the transformation operation for triggering the elementary motions of the module and the reconfiguration. The algorithm for optimizing the assembly reconfiguration of discrete modules is proposed and the result is evaluated through numerical simulation in an example
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.
483866
3D Locomotion of a Snake-like Robot Controlled by Cyclic Inhibitory CPG Model
With 3D (3-dimensional) movement's ability and rhythmic locomotion mode, a nature snake makes itself survive in rugged terrains. The rhythmic activities of most creatures are generated by the CPG (central pattern generator). Based on this fact, the sustained-type neuron has been adopted to construct a cyclic inhibitory CPG model for a snake-like robot whose joints are perpendicularly connected in series. Having compared with the sustained-type neuron and the mutual inhibitory CPG, the cyclic inhibitory CPG was proven to generate capably rhythmic output with the least number of differential equations. In this paper, we introduce the neuron network organized by the cyclic inhibitory CPGs connected in line with unilateral excitation to control the 3D locomotion of a snake-like robot, and present the necessary condition for the CPG neuron network to sustain a rhythmic output. By implementing this control architecture to a simulator with consideration of mechanical dynamics of a real snake-like robot "Perambulator", preliminary parameter setting of the CPG neuron network for its 3D locomotion is obtained. Moreover, it is shown that "Perambulator" can successfully exhibit 3D locomotion by using the output of the proposed CPG network. The obtained results have also provided a bran new approach to understand the unknown neuron network of nature snakes
15542808
An Agent-based Policy Aware Framework for Web Services Security
The security of Web services has become one of the important research topics about the application of Web services. In this paper, we propose an agent-based policy aware framework for Web services security. In this framework, a policy language called ReiT which is a declarative language based on the rules and ontologies is introduced. The non-structural knowledge is represented by rules and the structural temporal knowledge is represented by ontology. Moreover, we propose a mixed 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. In addition, we present a policy aware BOID agent to authorize the access control of the Web services. And we implement the policy aware BOID agent by extending the JADE platform.
7090731
Anomaly Intrusion Detection Methods for Peer-to-Peer System
Peer-to-peer systems and applications are the hotspot of research of network applications. As peer-to-peer system has no central system and is deployed on an open network, new concerns regarding security have been raised. As an additional security measure, the intrusion detection system would help determine whether unauthorized users are attempting to access, have already accessed, or have compromised the network Intrusion detection, as the second line of defense, is an indispensable tool for highly survivable networks. In this paper two anomaly intrusion detection methods are proposed for peer-to-peer system. The main characters of the methods are that they can detect intrusion in real-time without any expert knowledge and attack signatures. One method uses hidden Markov model to check reflector DoS attacks, another based on adaptive resonance theory, which can learn the normal behavior with unsupervised method. The experimental P2P system is built on FreePastry 1.401 and JDK 1.5.0. The results have indicated that the methods can find DoS attacks immediately and find new intruders with low false alarm rate.
13758087
Support cluster machine
For large-scale classification problems, the training samples can be clustered beforehand as a downsampling pre-process, and then only the obtained clusters are used for training. Motivated by such assumption, we proposed a classification algorithm, Support Cluster Machine (SCM), within the learning framework introduced by Vapnik. For the SCM, a compatible kernel is adopted such that a similarity measure can be handled not only between clusters in the training phase but also between a cluster and a vector in the testing phase. We also proved that the SCM is a general extension of the SVM with the RBF kernel. The experimental results confirm that the SCM is very effective for largescale classification problems due to significantly reduced computational costs for both training and testing and comparable classification accuracies. As a by-product, it provides a promising approach to dealing with privacy-preserving data mining problems.
206574982
Battle Damage Assessment based on Bayesian Network
With the quickening rhythm of the war and the complication of battlefield environment,the battle damage assessment(BDA) has had a more important effect on the commander's decision.Because of the complexity of modern battlefield and the uncertainty of battle damage information,it is more and more difficult to determine the damage level of target.Bayesian network is a reasoning and predicting tool coming into vogue only in recent decades.It can correctly reason a problem of interest based on the uncertainty and incomplete information,which is suited to evaluate the battle damage based on the uncertainty and incomplete target damage information.According to the study of BDA based on Bayesian network,the building and updating process of Bayesian network model were discussed.The feasibility and validity of Bayesian network for BDA were analyzed by taking airport runway for example.
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.