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.
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.
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.
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.
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.
13209883
An Approach for Dynamic Hardware /Software Partitioning Based on DPBIL
HW/SW partitioning is one of the key steps in high-level modeling of embedded system co-design. In some kinds of data-dependence applications, the partitioning environment is dynamic. An approach based on a kind of evolutionary algorithm called DPBIL is proposed for dynamic HW/SW partitioning problem. DPBIL is inspired by Dualism or complementarity in the nature and operates on a pair of dual probability vectors to adapt itself to the dynamic environment. We systematically constructed the system model, defined the dynamic environment and conducted experimental analysis via comparison with the existing method. To enhance the exploration performance of DPBIL, a threshold constraint mechanism is introduced. Experimental results indicated that our approach is feasible and efficient for dynamic HW/SW partitioning, and has much better performance than traditional ones.
35611392
CLUCGS and CLUCR - two matrix solution methods for general circuit simulation
Direct methods and iterative methods have been used in most analog circuit analysis tools for matrix solution. But neither can efficiently solve the large-scale sparse matrix or ill matrix because of the closely coupled relationship of execution time and matrix size for direct methods and the convergence problem for iterative methods. This paper presents two methods that are a combination of direct solution using complete LU factorization and iterative solution using conjugate gradient square (CGS) or conjugate residual (CR) method. The methods converge faster and more reliably than the LU factorization method and conjugate gradient (CG) method, and can achieve high resolution on a wide variety of circuits. A very efficient preconditioning method is used in our approach to accelerate convergence. Furthermore, we present an effective judgment method for re-factorization of matrix. Several numerical experiments results are included to compare their efficiencies.
28434248
Optimized Rate Allocation for Unbalanced Multiple Description Video Coding Over Unreliable Packet Network
15536271
Encouraging Good Interactions in Open Agent Systems
In the open agent systems, agents have to meet how, when and with who interact. Trust and reputation play an important role in reduces the uncertain. However, existing trust metric is not effective when an agent strategically behaviors, such as sudden behavior or alter 'name'. This paper presents a novel trust metric, which can penalize behavior of the suddenly falling behavior and change 'name'.
14897836
A Location-free Algorithm of Energy-Efficient Connected Coverage for High Density Wireless Sensor Networks
One of the most serious concerns for wireless sensor networks (WSN) is energy. To obtain long lifetime, one potential method is deploying redundant sensors in the WSN and let each sensor switch its state between ACTIVE and OFF. At the same time, the WSN should meet various requirements of quality of service (QoS). This paper focuses on two important measurements of OoS: sensing coverage and network connectivity. Existing researches have provided many algorithms to schedule the nodes’ states for coverage preserving, including location-free ones. Our work differs from existing location-free algorithms in several key ways: (1). We propose a novel principle on coverage preserving that is suitable for location-free schemes; (2). Following the principle, a distributed, localized and location-free node scheduling algorithm, Stand Guard Algorithm (StanGA), is proposed for treating coverage and connectivity in a unified scheme. Under certain conditions, StanGA can guarantee network connectivity and any degree of sensing coverage. Simulation results show that StanGA is scalable and robust, and also show that StanGA outperforms existing location-free algorithms even when only coverage issue is considered.