3666760
The Adaptive Path Planning Research for a Shape-Shifting Robot Using Particle Swarm Optimization
A shape-shifting robot with diverse configurations, named "AMOEBA-I" has been developed for search and rescue tasks. The accessibility of this robot to uneven environments was efficiently enlarged by changing its configurations. In this paper, for optimizing the path of the robot through an environment containing static polygonal obstacles, we present an adaptive path planning method for the AMOEBA-I, which integrates the reconfigurable ability of the robot into the particle swarm optimization (PSO). The unique accessibility of the AMOEBA-I is thus fully displayed. The modified PSO method can reduce the computational load and has certain degree of robustness on selecting dimension of particles. It can generate smooth paths by string of cubic splines. Experiment results show that the robot can change its configurations to perform the adaptive path planning corresponding to the environmental variation and current reconfigurable ability of AMOEBA-I. As a result, the path length has been reduced successfully and effectively.
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.
10605413
Center-configuration selection technique for the reconfigurable modular robot
The reconfigurable modular robot has an enormous amount of configurations to adapt to various environments and tasks. It greatly increases the complexity of configuration research in that the possible configuration number of the reconfigurable modular robot grows exponentially with the increase of module number. Being the initial configuration or the basic configuration of the reconfigurable robot, the center-configuration plays a crucial role in system’s actual applications. In this paper, a novel center-configuration selection technique has been proposed for reconfigurable modular robots. Based on the similarities between configurations’ transformation and graph theory, configuration network has been applied in the modeling and analyzing of these configurations. Configuration adjacency matrix, reconfirmation cost matrix, and center-configuration coefficient have been defined for the configuration network correspondingly. Being similar to the center-location problem, the center configuration has been selected according to the largest center-configuration coefficient. As an example of the reconfigurable robotic system, AMOEBA-I, a three-module reconfigurable robot with nine configurations which was developed in Shenyang Institute of Automation (SIA), Chinese Academy of Sciences (CAS), has been introduced briefly. According to the numerical simulation result, the center-configuration coefficients for these nine configurations have been calculated and compared to validate this technique. Lastly, a center-configuration selection example is provided with consideration of the adjacent configurations. The center-configuration selection technique proposed in this paper is also available to other reconfigurable modular robots.
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.
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.
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.
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.
39230310
SSOA: a Semantic Service-Oriented Architecture Based on Fuzzy Assertion System
37868959
A Method of Raster Data Mining Based on Multi Dimension Data Set
Spatial Data Mining can mine automatically or semi-automatically unknown, creditable, effective, integrative or schematic knowledge which can be understood from the increasingly complex spatial database and enhance the ability of interpreting data to generate useful knowledge. There are large amounts of data existing in the database of Government GIS. However, a great deal of the data is idle, which has caused a huge waste of data due to rarely effectively utilization in practice. It is very necessary to deal with the task of data mining based on Government GIS. In this paper, an example on land utilization and land cover in a certain region of Guizhou Province was presented to describe the course of spatial data mining. There into, a derived star-type model was used to organize the raster data to form multi dimension data set. Under these conditions, clustering method was utilized to carry out data mining aiming at the raster data. Based on corresponding analyses, users can find out which types of vegetation were suitable for being cultivated in this region by using related knowledge in a macroscopic view. Therefore, feasible service information could be provided to promote economic development in the region.
37867009
Spatial Clustering with Obstacles Constraints Using Ant Colony and Particle Swarm Optimization
Spatial clustering is an important research topic in Spatial Data Mining (SDM). This paper proposes an Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) method for solving Spatial Clustering with Obstacles Constraints (SCOC). In the process of doing so, we first use improved ACO to obtain the shortest obstructed distance, which is an effective method for arbitrary shape obstacles, and then we develop a novel PKSCOC based on PSO and K-Medoids to cluster spatial data with obstacles. The PKSCOC algorithm can not only give attention to higher local constringency speed and stronger global optimum search, but also get down to the obstacles constraints and practicalities of spatial clustering. The experimental results demonstrate the effectiveness and efficiency of the proposed method, which performs better than Improved K-Medoids SCOC (IKSCOC) in terms of quantization error and has higher constringency speed than Genetic K-Medoids SCOC (GKSCOC).
11982779
A Wireless Intrusion Detection Method Based on Dynamic Growing Neural Network
In this paper an intrusion detection method based on dynamic growing neural network (DGNN) for wireless networking is presented. DGNN is based on the Hebbian learning rule and adds new neurons under certain conditions. When DGNN performs supervised learning, resonance will happen if the winner can't match the training example; this rule combines the ART/ARTMAP neural network and WTA learning rule. When DGNN performs unsupervised learning, post-prune is carried out to prevent overfitting the training data just like decision tree learning. The intrusion detection method is an anomaly detection method and the feature is selected from the packets. In the experiments, we first check the ability of the neural network and then use it to perform detection in a WLAN. The results show that it can detect new intrusion behavior and some improving methods are presented in the conclusions