10266533
Configuration representation and reconfiguration optimization for the reconfigurable robots with independent manipulation
Single module of the reconfigurable robots with independent manipulation can perform the actions of locomotion and manipulation. In conformity with the request for achieving autonomous operation in the unstructurized environment instead of fixed operation in the structurized environment, these robots are applied in the complicated and dangerous environment. The existing researches on the configuration theory focus on the reconfigurable robots with limited locomotion and the ones with independent locomotion, not being applicable to the reconfigurable robots with independent manipulation. The vector configuration is put forward, the research content of which contains the topology and locomotion direction of configuration, the posture and orientation and connection relation between modules. Module state vector and configuration state matrix are proposed for representation methodology for the swarm configuration of these reconfigurable robots, which supports transformation operation to represent and trigger behavior motion of the module and reconfiguration between configurations. Optimization algorithm of assembly reconfiguration applying workload as the optimization target is presented, as well as optimization algorithm of transformation reconfiguration applying the integration of posture orientation workload and connection workload. The result of optimization is the relation of state transformation between the initial configuration and the object one as the basic of reconfiguration plan and control.
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.
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.
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.
1210599
Joint error concealment and error recovery for consecutive frame losses under the unbalanced multiple description coding architecture
Real-time video transmission over error-prone wireless networks often experiences consecutive frame losses due to either temporary link outages or traffic congestion. Although error concealment (EC) techniques have been extensively studied, they usually cannot handle the problem. Thus, we envision using EC under the unbalanced multiple description coding (UMDC) architecture. UMDC has almost no coding delay and can produce two descriptions at any bit-rates adaptive to different path bandwidths. In this paper, we propose an iterative EC algorithm able to adaptively exploit both the high-resolution (HR) and low-resolution (LR) information via multi-hypothesis weights. It is applied to both lost HR frames and following undecoded ones. Considering error propagation, we design an interframe error recovery (ER) algorithm for the undecoded HR frames. It iteratively uses multi-frame recovery principle to frame-by-frame reduce error drift in the HR stream with respect to the intermediate information from EC. The joint design of EC and ER can be applied to most UMD approaches. Extensive experiments have been carried out under different conditions. The proposed EC technique exhibits high PSNR gains versus the usual ones under the UMDC architecture and the classical ones without the support of UMDC. The proposed ER technique is efficient in reducing error drift. In conclusion, joint EC and ER can provide satisfactory performance on both PSNR and visual quality.
2307602
A Multi-Dimensional K-Anonymity Model for Hierarchical Data
For improving the usability of the anonymous result, it is important to comply with the hierarchical structure when generalizing quasi-identifying attributes with hierarchical characteristics. We propose an unrestricted multi-dimensional anonymization model which combines global recoding and local recoding methods. The bottom-up anonymization algorithm with the minimal coverage subgraph constraint and the anonymization metric are proposed. The experiment results justify the effectiveness and scalability of this model.
2517026
On Dynamic and Concurrent Model of Web Service Components
The researches of Web service fasten on composition process, and are lack of the formalized description on dynamic attributes of service component itself. Aiming to these, a new dynamic and concurrent model of Web service component is presented. Firstly, this paper analyzes the process of state transition of service component under certain actions, and then depicts the dynamic transition process of service component with the state transition graph. At last, the physical representation method in memory and the algorithms to judge equivalence of state transition graph are given. Comparing with existing researches, the dynamic evolvement of service component under the actions is considered, and the algorithm to judge equivalence of service components provides an effective tool for model verification.
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].
7440868
Hybrid quantum probabilistic coding genetic algorithm for large scale hardware-software co-synthesis of embedded systems
Hardware-software co-synthesis is a key step of future design of embedded systems. It involves three interdependent subproblems: allocation of resources, assignment of tasks to resources, and scheduling the execution of tasks. Both assignment and scheduling are known to be NP-complete. So it is a really hard and challenging task to optimization algorithms. Both heuristic and evolutionary algorithms are commonly used in real world. Heuristic algorithms converge rapidly but often be trapped in local minima and evolutionary algorithms own high exploration capacity but become time-consuming when handling large-scale systems. In this paper, a new hybrid evolutionary algorithm, called Hybrid Quantum probabilistic coding Genetic Algorithm, is proposed to implement the co-synthesis of large scale multiprocessor embedded systems, in which a heuristic algorithm is combined with the Quantum probabilistic coding Genetic Algorithm to enhance the performance on the hard task. The experimental results show that HQGA has better performance than both HA and QGA on large scale HW/SW co-synthesis problems.