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
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.
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.
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.
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.
6852082
Multi-strategy ensemble particle swarm optimization for dynamic optimization
Optimization in dynamic environments is important in real-world applications, which requires the optimization algorithms to be able to find and track the changing optimum efficiently over time. Among various algorithms for dynamic optimization, particle swarm optimization algorithms (PSOs) are attracting more and more attentions in recent years, due to their ability of keeping good balance between convergence and diversity maintenance. To tackle the challenges of dynamic optimization, several strategies have been proposed to enhance the performance of PSO, and have gained success on various dynamic optimization problems. But there still exist some issues in dynamic optimization which need to be studied carefully, i.e. the robustness of the algorithm to problems of various dynamic features. In this paper, a new multi-strategy ensemble particle swarm optimization (MEPSO) for dynamic optimization is proposed. In MEPSO, all particles are divided into two parts, denoted as part I and part II, respectively. Two new strategies, Gaussian local search and differential mutation, are introduced into these two parts, respectively. Experimental analyses reveal that the mechanisms used in part I can enhance the convergence ability of the algorithm, while mechanisms used in part II can extend the searching area of the particle population to avoid being trapped into the local optimum, and can enhance the ability of catching up with the changing optimum in dynamic environments. The whole algorithm has few parameters that need to be tuned, and all of them are not sensitive to problems. We compared MEPSO with other PSOs, including MQSO, PHPSO and Standard PSO with re-initialization, on moving peaks Benchmark and dynamic Rastrigin function. The experimental results show that MEPSO has pretty good performance on almost all testing problems adopted in this paper, and outperforms other algorithms when the dynamic environment is unimodal and changes severely, or has a great number of local optima as dynamic Rastrigin function does.
35117974
Online signature verification based on null component analysis and principal component analysis
This paper describes a method for stroke-based online signature verification using null component analysis (NCA) and principal component analysis (PCA). After the segmentation and flexible matching of the signature, we extract stable segments from each reference signature in order that the segment sequences have the same length. The reference set of feature vectors are transformed and separated into null components (NCs) and principal components (PCs) by K-L transform. Online signature verification is a special two-category classification problem and there is not a single available forgery set in an actual system. Therefore, it is different from the typical application of PCA in pattern recognition that both NCA and PCA are used to respectively analyze stable and unstable components of genuine reference set. Experiments on a data set containing a total 1,410 signatures of 94 signers show that the NCA/PCA-based online signature verification method can achieve better results. The best result yields an equal error rate of 1.9%.
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.
16012768
Continuous Optimization based-on Boosting Gaussian Mixture Model
A new estimation of distribution algorithm (EDA) based on Gaussian mixture model (GMM) is proposed, in which boosting, an efficient ensemble learning method, is adopted to estimate GMM. By boosting simple GMM with two components, it has the ability of learning the model structure and parameters automatically without any requirement for prior knowledge. Moreover, since boosting can be viewed as a gradient search for a good fit of some objective in function space, the new EDA is time efficient. A set of experiments is implemented to evaluate the efficiency and performance of the new algorithm. The results show that, with a relatively smaller population and less number of generations, the new algorithm can perform as well as compared EDAs in optimizing multimodal functions
5261874
Integrating software into PRA
Probabilistic Risk Assessment is a technique to assess the probability of failure or success of a mission. Results provided by the risk assessment methodology are used to make decisions concerning choice of upgrades, scheduling of maintenance, decision to launch, etc. However, current PRA neglects the contribution of software to the risk of failure of the mission. This paper presents a framework for "Integrating Software into PRA", a methodology for systematic integration of the software contribution to risk in system failure analysis. In particular, we established a software-related failure mode taxonomy and a three-level PRA sub-model to account for the impact of software to the classical PRA structure. Application and validation of the taxonomy are discussed in this paper. Future research is also summarized.