1356080
Obstacle Avoidance for Redundant Nonholonomic Mobile Modular Manipulators via Neural Fuzzy Approaches
This paper addresses an obstacle avoidance issue for redundant nonholonomic mobile modular manipulators. On the basis of modular robot concept, an integrated dynamic modeling method is proposed, which takes both the mobile platform and the onboard modular manipulator as an integrated structure. A new obstacle avoidance algorithm is proposed which is mainly composed of two parts: a self-motion planner (SMP) and a robust adaptive neural fuzzy controller (RANFC). One important feature of this algorithm lies in that obstacles are avoided by online adjusting self-motions so that the end-effector task will not be affected unless the obstacles are just on the desired trajectory. The RANFC does not rely on exact aprior dynamic parameters and can suppress bounded external disturbance effectively. The effectiveness of the proposed algorithm is verified by simulations.
16004582
Formation Control for a Multiple Robotic System Using Adaptive Neural Network
Due to the limitations of sensors, each member of a decentralized system can only deal with local information respectively. A description of local relationship within formation pattern is proposed in this paper. Furthermore, a NN control approach with robust term is proposed to control individual motion. By using such individual control method, all robots will finally form a unique formation. Based on properties of such control strategy, we propose a modified artificial potential approach to realize obstacle avoidance.
12730309
Adaptive Neural-Network Control for Redundant Nonholonomic Mobile Modular Manipulators
This paper discusses the trajectory following issue for redundant nonholonomic mobile modular manipulators. Dynamic model is established and an adaptive neural-network controller is developed to control the end-effector to follow a desired spacial trajectory. The proposed algorithm doesn't need any priori dynamics and provides a new solution for stabilization of redundant robotic self-motions. Simulation results for a real robot demonstrate the proposed algorithm is effective.
7167027
Power and thermal effects of SRAM vs. latch-mux design styles and clock gating choices
This paper studies the impact on energy efficiency and thermal behavior of design style and clock-gating style in queue and array structures. These structures are major sources of power dissipation, and both design styles and various clock gating schemes can be found in modern, high-performance processors. Although some work in the circuits domain has explored these issues from a power perspective, thermal treatments are less common, and we are not aware of any work in the architecture domain. We study both SRAM and latch and multiplexer ("latch-mux") designs and their associated clock-gating options. Using circuit-level simulations of both design styles, we derive power-dissipation ratios which are then used in cycle-level power/performance/thermal simulations. We find that even though the "unconstrained" power of SRAM designs is always better than latch-mux designs, latch-mux designs dissipate less power in practice when a structure's average occupancy is low but access rate is high, especially when "stall gating" is used to minimize switching power. We also find that latch-mux designs with stall gating are especially promising from a thermal perspective, because they exhibit lower power density than SRAM designs. Overall, when combined with implementation and verification challenges for SRAMs, latch-mux designs with stall gating appear especially promising for designs with thermal constraints. This paper also shows the importance of considering the interaction between architectural and circuit design choices when performing early-stage design exploration.
1371524
Continuous-time digital signal processors
In this paper, we discuss how asynchronous design techniques can be used in the implementation of continuous-time signal processors. Such processors are presented by signals developed by continuous-time analog-to-digital converters which involve no sampling, and thus do not exhibit aliasing; in addition, the resulting in-band quantization error is lower than in conventional techniques. Several design considerations are given, and preliminary experimental results are presented.
15368149
Hierarchical fuzzy filter method for unsupervised feature selection
The problem of feature selection has long been an active research topic within statistics and pattern recognition. So far, most methods of feature selection focus on supervised data where class information is available. For unsupervised data, the related methods of feature selection are few. The presented article demonstrates a way of unsupervised feature selection, which is a two-level filter model removing the redundant and irrelevant features, respectively. The redundant features are eliminated using any clustering algorithm, and a new method is proposed to remove the irrelevant features: first rank the features according to their relevance to cluster and then a subset of relevant features is selected using the Fuzzy Feature Evaluation Index (FFEI) with some changes and extensions. The experimental results have shown the effectiveness of the proposed method for high-dimensional data. Our major contributions are: (1) to present a new hierarchical filter method for unsupervised feature selection; (2) to propose a new algorithm for removing the irrelevant features; (3) to extend the FFEI, and present a method for calculating the approximate weight of feature in FFEI, which improves the efficiency and robustness of the method.
5262698
An integrated algorithm of incremental and robust PCA
Principal component analysis (PCA) is a well-established technique in image processing and pattern recognition. Incremental PCA and robust PCA are two interesting problems with numerous potential applications. However, these two issues have only been separately addressed in the previous studies. In this paper, we present a novel algorithm for incremental and robust PCA by seamlessly integrating the two issues together. The proposed algorithm has the advantages of both incremental PCA and robust PCA. Moreover, unlike most M-estimation based robust algorithms, it is computational efficient. Experimental results on dynamic background modelling are provided to show the performance of the algorithm with a comparison to the conventional batch-mode and nonrobust algorithms.
3890576
Design and experimental analysis of continuous location tracking techniques for Wizard of Oz testing
Wizard of Oz (WOz) testing has shown promise as an effective way to test location-enhanced applications. However, it is challenging to conduct a location-based WOz test because of the dynamic nature of target settings in the field. In particular, continuous location tracking, a major task in such a test, requires a wizard to frequently update a user's location to simulate a location system. This imposes a heavy task load on a wizard. To ease wizards' tasks for location tracking, we designed two techniques, Directional Crossing and Steering, and conducted a field experiment to investigate the performance of the two techniques. A quantitative analysis shows that Directional Crossing and Steering significantly lowered a wizard's task load for location tracking without sacrificing accuracy.