Alireza Bab-Hadiashar

A comparison of reactive robot chemotaxis algorithms

This paper describes the implementation and evaluation of four reactive robot chemotaxis algorithms. If they are applicable, reactive algorithms can provide fast, simple and cost-effective solutions for robot control applications. As part of this evaluation a robot was developed that has sufficient resources to enable it to implement each of the chemotaxis algorithms. The robot has bilateral chemical sensors, an airflow sensor and tactile whiskers to detect obstacles. Chemotaxis algorithms observed in the bacterium E. coli, the silkworm moth Bombyx mori, and the dung beetle Geotrupes stercorarius were tested as well as a gradient-based algorithm. There are many potential applications for chemical sensing robots particularly in situations where animals such as sniffer dogs are currently used. These applications include locating victims of avalanches or earthquakes and detecting landmines. Robotic systems offer a number of benefits compared to the use of animals, including rapid deployment, low maintenance costs and operation for extended periods. Details of the algorithms are given together with typical results obtained using the algorithms in both simulated and practical experiments. The design of the chemical sensing robot and the relative merits and demerits of the different chemotaxis algorithms are also discussed.

Robust Optic Flow Computation

This paper formulates the optic flow problem as a set of over-determined simultaneous linear equations. It then introduces and studies two new robust optic flow methods. The first technique is based on using the Least Median of Squares (LMedS) to detect the outliers. Then, the inlier group is solved using the least square technique. The second method employs a new robust statistical method named the Least Median of Squares Orthogonal Distances (LMSOD) to identify the outliers and then uses total least squares to solve the optic flow problem. The performance of both methods are studied by experiments on synthetic and real image sequences. These methods outperform other published methods both in accuracy and robustness.

Calibration of Resolver Sensors in Electromechanical Braking Systems: A Modified Recursive Weighted Least-Squares Approach

Resolver sensors are utilized as absolute position transducers to control the position and speed of actuators in many industrial applications. The accuracy and convergence of the position and speed measurements provided by resolvers in electromechanical braking system (EMB) designs directly contribute to the braking performance and vehicle safety. In practice, the dc drifts, amplitudes, and phase shift of the resolver signals vary with aging and temperature, and adaptive techniques are required for the calibration of these parameters of resolvers. Existing classical adaptive techniques such as recursive least squares are unable to track the parameters during resting (low-speed actuation or stationary) periods and also a transient period after them. This paper proposes a new approach for real-time tracking of resolver parameters specially developed for actuator-control applications with varying speed and long resting periods. We formulate the algebraic relationship between the resolver parameters and the parameters of resolver characteristic ellipse, which is the ellipse formed by plotting the resolver signals versus each other. Having known the characteristic ellipse parameters, the resolver parameters are calculated using the formulated algebraic relation. Then, a new recursive and adaptive estimator is proposed to track the parameters of characteristic ellipse. The low computational complexity of the proposed method makes it desirable for real-time applications like the EMBs, where limited computational power and memory are available. Experimental results show that the proposed technique is able to track the resolver parameters and the accurate actuator position with a small error in real-time, while other adaptive estimators are unable to track the resolver parameters during and after resting periods

Robust segmentation of visual data using ranked unbiased scale estimate

A method of data segmentation, based upon robust least K-th order statistical model fitting (LKS), is proposed and applied to image motion and range data segmentation. The estimation method differs from other approaches using versions of LKS in a number of important ways. Firstly, the value of K is not determined by a complex optimization routine. Secondly, having chosen a fit, the estimation of scale of the noise is not based upon the K-th order statistic of the residuals. Other aspects of the full segmentation scheme include the use of segment contiguity to: (a) reduce the number of random sample fits used in the LKS stage, and (b) to “fill-in” holes caused by isolated miss-classified data.

Range image segmentation using surface selection criterion

In this paper, we address the problem of recovering the true underlying model of a surface while performing the segmentation. First, and in order to solve the model selection problem, we introduce a novel criterion, which is based on minimising strain energy of fitted surfaces. We then evaluate its performance and compare it with many other existing model selection techniques. Using this criterion, we then present a robust range data segmentation algorithm capable of segmenting complex objects with planar and curved surfaces. The presented algorithm simultaneously identifies the type (order and geometric shape) of each surface and separates all the points that are part of that surface. This paper includes the segmentation results of a large collection of range images obtained from objects with planar and curved surfaces. The resulting segmentation algorithm successfully segments various possible types of curved objects. More importantly, the new technique is capable of detecting the association between separated parts of a surface, which has the same Cartesian equation while segmenting a scene. This aspect is very useful in some industrial applications of range data analysis.

Missing data compensation for safety-critical components in a drive-by-wire system

In this article, a new multistep ahead predictive filtering scheme is introduced. The proposed technique is essential for proper operation of safety-critical components in a drive-by-wire car. Because limited computational time and memory are available in drive-by-wire systems, the main advantage of our scheme is that it only requires one set of finite impulse response (FIR) filter weights to be tuned while it can be used for different numbers of steps ahead predictions. To verify and compare the proposed filter, our model and four competing methods were applied to predict up to four missing samples of displacement sensor data from a brake pedal in a brake-by-wire system. Experimental results show that prediction performance of our proposed FIR filter is higher than, or at least comparable to, other filters with the same memory requirements and the computational overhead of data missing handling by our proposed method is considerably lower than other comparable methods. Hence, the proposed filter has a superior performance in missing data compensation for drive-by-wire systems.

Optic flow calculation using robust statistics

A method for calculating optic flow, using robust statistics, is developed. The method generally out-performs all competing methods in terms of accuracy. One of the key features in the success of this method, is that we use least median of squares, which is known to be robust to outliers. The computational cost is kept very low by using an approximate solution to the least median of squares only in a first stage that detects outliers. The essential ingredients of our method should be applicable in a wide range of other computer vision problems.

Real-Time Clamp Force Measurement in Electromechanical Brake Calipers

In brake-by-wire systems, central controllers require accurate information about the clamp force between the brake pad and the disc as a function of pad displacement, which is usually denoted as the characteristic curve of the caliper. Due to aging, temperature, and other environmental variations, caliper characteristic curves vary with time. Therefore, automatic caliper calibration in real-time is vital for high-performance braking action and vehicle safety. Due to memory and processing-power limitations, the calibration technique should be memory efficient and of low computational complexity. In a typical electromechanical-braking-system design, clamp force measurement variations with actuator displacement is hysteretic. This paper introduces a simple and memory-efficient real-time calibration technique in which a clamp-force model is fitted to the data samples around each hysteresis cycle. The model includes a Maxwell-slip model for the hysteresis caused by friction. Experimental results from the data recorded in various temperatures show that the proposed technique results in clamp force measurements with less than 0.7% error over the range of clamp-force variations. It is also shown that, by using these measurements, the characteristic curve can be accurately calibrated in real-time.

Clamp-Force Estimation for a Brake-by-Wire System: A Sensor-Fusion Approach

The elimination of a clamp-force sensor from brake-by-wire system designs is strongly demanded due to implementation difficulties and cost issues. In this paper, a new method is presented to estimate the clamp force based on other sensory information. This estimator fuses the outputs of two models to optimize the root-mean-square error (RMSE) of estimation. Experimental results show that the estimator can accurately track the true clamp force for high-speed cases as demanded by the antilock braking system controls. A training strategy has been used to ensure that the estimator can successfully adapt to parameter variations associated with wear. This paper is concluded with a discussion on the reliability of the developed clamp-force estimator.

Consistency of robust estimators in multi-structural visual data segmentation

A theoretical framework is presented to study the consistency of robust estimators used in vision problems involving extraction of fine details. A strong correlation between asymptotic performance of a robust estimator and the asymptotic bias of its scale estimate is mathematically demonstrated where the structures are assumed to be linear corrupted by Gaussian noise. A new measure for the inconsistency of scale estimators is defined and formulated by deriving the functional forms of four recent high-breakdown robust estimators. For each estimator, the inconsistency measures are numerically evaluated for a range of mutual distances between structures and inlier ratios, and the minimum mutual distance between the structures, for which each estimator returns a non-bridging fit, is calculated.