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.
18568161
Analysis of locomotion of a planetary rover on a slope
For improvement of planetary rover locomotion, a new type of tracked vehicle was designed for planetary exploration based on the concept of reconfigurability. By applying the planetary wheel principle, the system can give output in different forms under various constraint conditions. As a reconfigurable module, the vehicle can be integrated into a larger system using two basic configurations. As a stand-alone rover, it has two moving modes. The whole design was analyzed by conducting dynamics simulations and experiments.
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.
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
14553676
Design and Basic Experiments of a Shape-shifting Mobile Robot for Urban Search and Rescue
The recent natural and man-made devastations have urged the research on the urban search and rescue (USAR) robot systems. This paper presents a novel shape-shifting mobile robot system named as Amoeba II (A-II) for the urban search and rescue application. It has been designed with three degrees of freedom (DOFs) and two tracked drive systems. This robot consists of two modular mobile units and a joint unit. The mobile unit is a tracked mechanism to enforce the propulsion of robot. The joint unit can transform the robot shape for getting high mobility. A-II robot not only can adapt to the environment but also can change its body corresponding to locus space. It behaves two states including the parallel state (named as II state) and the linear state (named as I state). The parallel state enables the robot with high mobility on rough ground. With the linear state the robot can climb upstairs and go through narrow space such as the pipe, cave etc. Also, the joint unit can propel the robot to roll in sidewise direction. Especially, two modular A-II robots can be connected through jointing common interfaces on the joint unit to compose a stronger shape-shifting robot, which can transform the body into four wheels-driven vehicle. Finally, the elementary experimental results validate the adaptation and its mobility
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.
7822301
A web-based aggregated platform for user-contributed interactive media broadcasting
In this paper, we present a web-based aggregated platform, DJ DreamFactory, which enables average users to effortlessly participate in and contribute to interactive media broadcasting over the Internet. The platform overcomes several shortcomings of existing Internet-based broadcasting systems, such as inconvenience in channel surfing and content browsing due to the scattering and isolating of broadcasting stations, difficulties in setting up a broadcasting station, lack of communications between broadcasters and audience, and little support for personalized experience. The proposed platform facilitates users' media access by seamlessly aggregating sporadic broadcasting stations run by individual hosts, and enables a virtual community where grassroots users can contribute to media broadcasting, sharing, organizing and annotating through social networking. In addition, it supports real-time multimodal interaction between audience and hosts, provides customized services for both broadcasters and audience, supports personalized media experiences by mining and managing audience's preferences, and facilitates the organization of unstructured media data collections as well as collective human intelligence on the Web.
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.
2854723
UTDallas at TREC 2008 Blog Track
Abstract : This paper describes our participation in the 2008 TREC Blog track. Our system consists of 3 components: data preprocessing, topic retrieval, and opinion finding. In the topic retrieval task, we applied Lemur IR toolkit and used various techniques for query expansion. In the opinion finding and polarization task, we employed a feature-based classification approach. Then re-ranking was performed using a linear combination of the opinionated score and the topic relevance score. Our system achieved reasonable performance in this evaluation.
10259496
An E-Government Platform Based on Multi-Tier Architecture
With the development of dynamic e-government and the network, the application system becomes more complicated. Traditional e-government system will have an inherent scalability problem as the number of applications increases continuously. To solve this problem, an e-government platform based on multi-tier architecture is presented. By integrating applications, resources and system modules, the platform provides an effective mechanism to reduce the development and communication cost. This paper intensively discusses the design rationale and practical solutions of the platform, including structured data models, a secure data exchange model, uniform interface specifications, a topic based publish/subscribe communication mechanism and an asynchronous execution mechanism. The security of the platform is also evaluated by formal logic method.
11266206
A robust incremental learning framework for accurate skin region segmentation in color images
In this paper, we propose a robust incremental learning framework for accurate skin region segmentation in real-life images. The proposed framework is able to automatically learn the skin color information from each test image in real-time and generate the specific skin model (SSM) for that image. Consequently, the SSM can adapt to a certain image, in which the skin colors may vary from one region to another due to illumination conditions and inherent skin colors. The proposed framework consists of multiple iterations to learn the SSM, and each iteration comprises two major steps: (1) collecting new skin samples by region growing; (2) updating the skin model incrementally with the available skin samples. After the skin model converges (i.e., becomes the SSM), a post-processing can be further performed to fill up the interstices on the skin map. We performed a set of experiments on a large-scale real-life image database and our method observably outperformed the well-known Bayesian histogram. The experimental results confirm that the SSM is more robust than static skin models.