Qing-Hao Meng

Adapting an Ant Colony Metaphor for Multi-Robot Chemical Plume Tracing

We consider chemical plume tracing (CPT) in time-varying airflow environments using multiple mobile robots. The purpose of CPT is to approach a gas source with a previously unknown location in a given area. Therefore, the CPT could be considered as a dynamic optimization problem in continuous domains. The traditional ant colony optimization (ACO) algorithm has been successfully used for combinatorial optimization problems in discrete domains. To adapt the ant colony metaphor to the multi-robot CPT problem, the two-dimension continuous search area is discretized into grids and the virtual pheromone is updated according to both the gas concentration and wind information. To prevent the adapted ACO algorithm from being prematurely trapped in a local optimum, the upwind surge behavior is adopted by the robots with relatively higher gas concentration in order to explore more areas. The spiral surge (SS) algorithm is also examined for comparison. Experimental results using multiple real robots in two indoor natural ventilated airflow environments show that the proposed CPT method performs better than the SS algorithm. The simulation results for large-scale advection-diffusion plume environments show that the proposed method could also work in outdoor meandering plume environments.

Electronic nose with a new feature reduction method and a multi-linear classifier for Chinese liquor classification

An electronic nose (e-nose) was designed to classify Chinese liquors of the same aroma style. A new method of feature reduction which combined feature selection with feature extraction was proposed. Feature selection method used 8 feature-selection algorithms based on information theory and reduced the dimension of the feature space to 41. Kernel entropy component analysis was introduced into the e-nose system as a feature extraction method and the dimension of feature space was reduced to 12. Classification of Chinese liquors was performed by using back propagation artificial neural network (BP-ANN), linear discrimination analysis (LDA), and a multi-linear classifier. The classification rate of the multi-linear classifier was 97.22%, which was higher than LDA and BP-ANN. Finally the classification of Chinese liquors according to their raw materials and geographical origins was performed using the proposed multi-linear classifier and classification rate was 98.75% and 100%, respectively.

Collective Odor Source Estimation and Search in Time-Variant Airflow Environments Using Mobile Robots

This paper addresses the collective odor source localization (OSL) problem in a time-varying airflow environment using mobile robots. A novel OSL methodology which combines odor-source probability estimation and multiple robots’ search is proposed. The estimation phase consists of two steps: firstly, the separate probability-distribution map of odor source is estimated via Bayesian rules and fuzzy inference based on a single robot’s detection events; secondly, the separate maps estimated by different robots at different times are fused into a combined map by way of distance based superposition. The multi-robot search behaviors are coordinated via a particle swarm optimization algorithm, where the estimated odor-source probability distribution is used to express the fitness functions. In the process of OSL, the estimation phase provides the prior knowledge for the searching while the searching verifies the estimation results, and both phases are implemented iteratively. The results of simulations for large-scale advection–diffusion plume environments and experiments using real robots in an indoor airflow environment validate the feasibility and robustness of the proposed OSL method.

Real-Time Noncrosstalk Sonar System by Short Optimized Pulse-Position Modulation Sequences

Ultrasonic crosstalk is one of the main causes of false distance measurements that reduce the work efficiency of sonar sensors in mobile robots. To enhance the real-time performance of sonar systems, short digital pulse-position modulation (PPM) sequences are used to trigger ultrasonic transducers. Due to their properties of sharp autocorrelation and flat cross correlation, chaotic and pseudorandom number series are used to modulate pulse positions. A genetic algorithm is adopted to optimize the range of duration between pulses. Real experiments using Polaroid 600 series instrument-grade electrostatic transducers validate the suitability of the proposed method.

A P300 Model for Cerebot – A Mind-Controlled Humanoid Robot

In this paper, we present a P300 model for control of Cerebot – a mind-controlled humanoid robot, including a procedure of acquiring P300 signals, topographical distribution analysis of P300 signals, and a classification approach to identifying subjects’ mental activities regarding robot-walking behavior.

Improvement in the accuracy of estimating the time-of-flight in an ultrasonic ranging system using multiple square-root unscented Kalman filters

A novel monolithic integrated active mixer/LNA utilizing advanced pseudomorphic GaAs FETs has been developed. RF frequency coverage is 900 MHz to 2400 MHz. The Mixer/LNA operates on a single 3 Volt supply at 7 mA. The mixer provides 4 dB conversion gain with a -10 dBm LO drive. The LNA provides a 1.6 dB noise figure and 14 dB gain. The die is packaged in the SSOP-16 plastic package to keep costs low. This is the only GaAs PHEMT active mixer/LNA known to provide such performance from such a small DC requirement.<<ETX>>The square-root unscented Kalman filter (SRUKF) is applied to identify the shape parameters of an ultrasonic echo envelope. The SRUKF has better stability than the normal unscented Kalman filter (UKF) because the square-root of the error covariance matrix used in the SRUKF guarantees positive semidefiniteness. Considering the effect of the initial state on the convergence speed of filters, the multi-SRUKF is used to estimate the time-of-flight (TOF). Each SRUKF has a different initial state. The result estimated in a limited time with minimum mean square error is finally adopted. Simulation experiments for various couples of shape parameters and signal-to-noise ratios validate the improvement in the TOF accuracy. Real experiments using the echo signals of a SensComp 600 ultrasonic transducer show that the relative means and standard deviations of the TOF error obtained using the multi-SRUKF method are less than 0.2% and 0.15%, respectively.

Single Odor Source Declaration by Using Multiple Robots

The single odor source declaration in indoor environments by using multi‐robot system is addressed. A three‐step odor source declaration method is put forward, which include robots convergence, odor concentration persistence judgment and odor mass throughput calculation. Initial experimental results in both artificial and natural indoor airflow environments by using three small mobile robots validate the feasibility of the proposed single odor source declaration method.

An OpenViBE-based brainwave control system for Cerebot

In this paper, we develop a brainwave-based control system for Cerebot, consisting of a humanoid robot and a Cerebus™ Data Acquisition System up to 128 channels. Under the OpenViBE programming environment, the control system integrates OpenGL, OpenCV, WEBOTS, Choregraph, Central software, and user-developed programs in C++ and Matlab. The proposed system is easy to be expanded or upgraded. Firstly, we describe the system structures for off-line analysis of acquired neural signals and for on-line control of a humanoid robot via brainwaves. Secondly, we discuss how to use the toolboxes provided with the OpenViBE environment to design three types of brainwave-based models: SSVEPs, P300s, and mu/beta rhythms. Finally, we use the Cerebot platform to investigate the three models by controlling four robot-walking behaviors: turning right, turning left, walking forward, and walking backward.

Mobile robot gas source localization via top-down visual attention mechanism and shape analysis

A novel mobile robot based gas source localization method in which the top-down visual attention mechanism (TDVAM) is combined with shape analysis is proposed. At each location, three different images which cover the scene in front of the robot are captured via changing the horizontal angle of an onboard pan/tilt camera. In each image, three salient regions are computed using TDVAM model, and maximal one plausible gas source from three salient regions is identified by using the shape analysis. The positions of recognized plausible gas sources are determined with a laser range scanner. The robot is navigated to the plausible gas sources one by one and gas concentration information is used to judge if the plausible sources are real ones. Experimental results in a complex indoor airflow environment in which valves are used to simulate gas sources demonstrate the feasibility and high efficiency of the proposed gas source localization approach.

A New Method Combining KECA-LDA With ELM for Classification of Chinese Liquors Using Electronic Nose

We proposed a hybrid algorithm by combining kernel entropy component analysis (KECA) with linear discriminant analysis (LDA), namely, KECA-LDA for feature reduction in electronic-nose systems. It combined the advantages of KECA and LDA. Then, the data extracted by KECA-LDA were inputted to extreme learning machine (ELM) for classification. In order to examine the performance of the proposed method, eight types of strong-flavor Chinese liquors were tested using an electronic nose (e-nose) system designed by ourselves, and the results after cross validation showed that features extracted by KECA-LDA were more beneficial to classification than KECA, and the performance of ELM was better than that of backpropagation neural network. The highest classification rate by KECA-LDA-ELM was 100%. In conclusion, an e-nose combined with KECA-LDA and ELM is a feasible method to classify Chinese liquors.