Robust path tracking control of nonholonomic wheeled mobile robot: Experimental validation

The article addresses a robust control strategy for efficient path tracking of nonholonomic wheeled mobile robot (WMR) based on time delay approach. Depending on the application requirements, nonholonomic WMR system might be subjected to various payloads, which affects the overall system mass, inertia, position of center of mass and other hardware parameters statically or dynamically. Under such circumstances, accurate modeling of nonholonomic robots is difficult and challenging. The proposed controller negotiates uncertainties caused due to payload variations as well as associated disturbances and reduces modeling effort through approximation of the overall uncertainties with a composite function. It has been shown that the controller does not require any bounds on the uncertainties, thus providing unconstrained working paradigm. The controller is proposed for a nonholonomic WMR and its effectiveness is verified through simulation and experimentally while WMR is commanded to track various paths. The superior performance is also noted against adaptive sliding mode control law

Robust control of nonholonomic wheeled mobile robot with past information: Theory and experiment

In this article, a robust hybrid control method is presented for efficient path tracking control of a nonholonomic wheeled mobile robotic system under parametric and nonparametric variations. The present control law is a paradigm shift to control a wheeled mobile robot over a predefined trajectory by fusing the best features of the switching control logic as well as time-delayed control logic. The proposed hybrid control strategy aims at reducing the effort required for modeling the complex wheeled mobile robotic systems by approximating the unknown dynamics using input and feedback information of past time instances. Furthermore, the proposed methodology significantly reduces the approximation error arising from finite time-delay through the switching logic without any prior knowledge of the uncertainty bound. A new stability analysis for the time-delayed control is proposed which establishes an analytical relation between the controller performance and the approximation error. Performance of the proposed hybrid controller is tested with a real-life wheeled mobile robot and improved tracking performance is observed compared to conventional robust control strategies even with the incorporation of dynamic parametric uncertainties

On methods to improve time delay estimation for underwater acoustic source localization

237-244<span style="font-size:9.0pt;font-family: " times="" new="" roman","serif";mso-fareast-font-family:"times="" roman";mso-bidi-font-family:="" mangal;mso-ansi-language:en-gb;mso-fareast-language:en-us;mso-bidi-language:="" hi;mso-bidi-font-weight:bold"="" lang="EN-GB">This paper addresses the problem of acoustic source localization in shallow water environment commonly dominated by impulsive noise and multipath phenomenon. Traditionally acoustic source localization with sensors spaced several wavelengths apart involves Time Delay Estimation (TDE) via Generalized Cross-Correlation Phase Transform (GCC-PHAT)1. However multipath signals and impulsive noise in underwater ambience result in spurious peaks leading to anomalous time delay estimates, and in turn erroneous computation of source location. In the present work we recommend two methods to improve time delay estimation, based on order statistics and via signal detection. Simulation results indicate a significant improvement in time delay estimation as compared to GCC-PHAT in presence of impulsive noise and multipath induced fading.</span

Reliable pose estimation of underwater dock using single camera: a scene invariant approach

It is well known that docking of Autonomous Underwater Vehicle (AUV) provides scope to perform long duration deep-sea exploration. A large amount of literature is available on vision-based docking which exploit mechanical design, colored markers to estimate the pose of a docking station. In this work, we propose a method to estimate the relative pose of a circular-shaped docking station (arranged with LED lights on periphery) up to five degrees of freedom (5-DOF, neglecting roll effect). Generally, extraction of light markers from underwater images is based on fixed/adaptive choice of threshold, followed by mass moment-based computation of individual markers as well as center of the dock. Novelty of our work is the proposed highly effective scene invariant histogram-based adaptive thresholding scheme (HATS) which reliably extracts positions of light sources seen in active marker images. As the perspective projection of a circle features a family of ellipses, we then fit an appropriate ellipse for the markers and subsequently use the ellipse parameters to estimate the pose of a circular docking station with the help of a well-known method in Safaee-Rad et al. (IEEE Trans Robot Autom 8(5):624–640, 1992). We analyze the effectiveness of HATS as well as proposed approach through simulations and experimentation. We also compare performance of targeted curvature-based pose estimation with a non-iterative efficient perspective-n-point (EPnP) method. The paper ends with a few interesting remarks on vantages with ellipse fitting for markers and utility of proposed method in case of non-detection of all the light markers

Real-time underwater image enhancement: An improved approach for imaging with AUV-150

An RGB YCbCr Processing method (RYPro) is proposed for underwater images commonly suffering from low contrast and poor color quality. The degradation in image quality may be attributed to absorption and backscattering of light by suspended underwater particles. Moreover, as the depth increases, different colors are absorbed by the surrounding medium depending on the wavelengths. In particular, blue/green color is dominant in the underwater ambience which is known as color cast. For further processing of the image, enhancement remains an essential preprocessing operation. Color equalization is a widely adopted approach for underwater image enhancement. Traditional methods normally involve blind color equalization for enhancing the image under test. In the present work, processing sequence of the proposed method includes noise removal using linear and non-linear filters followed by adaptive contrast correction in the RGB and YCbCr color planes. Performance of the proposed method is evaluated and compared with three golden methods, namely, Gray World (GW), White Patch (WP), Adobe Photoshop Equalization (APE) and a recently developed method entitled “Unsupervised Color Correction Method (UCM)”. In view of its simplicity and computational ease, the proposed method is recommended for real-time applications. Suitability of the proposed method is validated by real-time implementation during the testing of the Autonomous Underwater Vehicle (AUV-150) developed indigenously by CSIR-CMERI

Automatic target detection of sonar images using multi-modal threshold and connected component theory

267-279<span style="font-size:9.0pt;font-family: " times="" new="" roman","serif";mso-fareast-font-family:"times="" roman";mso-bidi-font-family:="" mangal;mso-ansi-language:en-us;mso-fareast-language:en-us;mso-bidi-language:="" hi"="" lang="EN-US">The aim of this paper is to present a complete progressive development of object detection from underwater acoustic images. Object detection with respect to automatic target detection in underwater autonomous vehicle system is still in a severe problem in context of surveillance and other defense activity. The present work is based on robust method in perspective of segmentation and feature extraction. Underwater acoustic images suffer from typical noise associations and are often of low contrast. In this perspective, a multi-modal thresholding is adopted for automatic segmentation of the images thus obtained and a graph theoretic approach based on connected components is formulated in order to interpret features embedded within the image context. An imaging SONAR is used for carrying out necessary experimental work. The proposed algorithm is executed in comparison with multi-level thresholding and K-means clustering. Effectiveness is established in the context of both running time and quality of processed image as well. The latter aspect is determined by a Figure of Merit (FOM) parameter.</span

Robust Diving and Composite Path Tracking Control of an Autonomous Underwater Vehicle

Autonomous Underwater Vehicles (AUVs) are becoming indispensable for the maritime industry and defense applications. The nonlinear, time-varying, and highly coupled dynamics of AUVs, along with the parametric uncertainties and unmodeled dynamics, make the design of efficient controllers a hard task. This article explores a robust control strategy that aims at providing better tracking accuracy by reducing the switching gain in order to reduce chattering and the control error bandwidth. The performance of the proposed controller is demonstrated through rigorous simulation on an experimentally validated AUV, and superior path tracking performance is noted against sliding mode and time delay control methodologies under various uncertain conditions