Search

For surveillance of high-security zones, seismic sensors have received considerable attention in numerous civilian and military applications. Since seismic sensors are highly sensitive to the Earth’s surface vibration, outdoor surveillance is difficult during seasonal variations. We acquire empirical seismic signals of various events during normal and rainy weather over a period of three months and analyze the potential challenges in the classification of intrusion with existing solutions. In this work, we propose to categorize events occurring during rain as a separate class to improve upon classification accuracies for human, vehicle, animal movements, and no-disturbance in normal as well as rainy weather. Next, considering the similarities in speech and seismic signals, we propose Gaussian mixture modeling of Mel-frequency cepstral coefficients (MFCCs) and their first derivatives ( $\Delta $ -MFCCs) of seismic signals followed by maximum likelihood estimation of the corresponding Gaussian mixture model (GMM) of the true event. We conduct extensive experiments with a setup in an outdoor environment and demonstrate that consideration of rain as a separate event greatly enhances the efficacy of the classifier. We provide a comparative performance analysis and show that the proposed classifier with an overall accuracy of 97.7% in normal and rainy weather outperforms existing solutions, popular machine learning (ML), and deep learning (DL) models.

This research introduces a unified parametric output function based control law for autonomous navigation of nonholonomic Wheeled Mobile Robots (WMRs) through various predefined paths, such as straight line, circular, elliptical, square, rectangular, and their combinations, achieved by adjusting parameters. This approach simplifies path-following control paradigm by eliminating the need for generating separate output functions and relevant independent decoupling matrices for each path. Uncertainties of WMR are considered and embedded within the control framework. Analytical proof is provided to ensure the existence of the control law, which is proposed for the new framework using the unified parametric function that represents various paths, for all time. Further, a fuzzy rule-base driven gain parameter scheduling framework is adopted for efficient real-life implementation of the controller, avoiding actuator saturation. After successfully achieving the desired performance maneuver in a simulated environment, experiments are conducted for validation. A Pioneer P3-DX WMR is utilized to implement the control law towards maneuvering through aforementioned paths generated by the unified function, which is relevant in robotic applications. The WMR demonstrates the ability to navigate effectively with higher accuracy along the aforementioned paths at varying velocities, including zero velocity. The zero velocity feature helps to accomplish the required functionality, such as picking up an object, at an intermediate step, thus surpassing traditional trajectory tracking methods. Eventually, the unified function based path following control framework outperforms several existing controllers, especially for paths that are subjected to substantially large curvature at some portions, as illustrated through experimental performances and subsequent quantification.

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.

Unstructured oceanic environment and uncertain operating conditions of autonomous underwater vehicles (AUV) with limited on-board energy resources calls for the design of appropriate controllers to achieve optimal energy consumption while navigatingunderunstructured oceanic environment. With this aspect in mind, a sub-optimal robust control methodology has been presented here based on the appropriate switching surface design pertaining to the sliding mode control and an optimizer based on the solution of Euler-Lagrange equation for minimisation of total energy in relevance to the trajectory tracking of autonomous underwater robots within acceptable range of tracking error. The optimizer searches the local minimum in the nearest neighbourhood of the solution provided by the sliding mode controller and produces a sub-optimal solution.The system has been driven through composite 3-dimensional helical trajectories and reasonably accurate performance with substantial reduction of total control effort is noticed over the existing standalone controllers in spite of various parametric variations.

This research article presents a novel solution of controller design for the autonomous path-following maneuver of a nonholonomic wheeled mobile robotic (WMR) system subjected to static and dynamic obstacles. Currently, autonomously operated nonholonomic vehicles, capable of completing tasks avoiding collisions, are deployed for low-profile household applications to sophisticated space missions. The geometrically constrained path-following controller with embedded intelligence for collision avoidance is designed based on a twofold hybrid control law. The hybrid control law combines the feedback linearization controller (FBC) with the fuzzy logic controller (FLC). The feedback linearization controller helps the mobile robot to remain on its desired path, while the fuzzy logic controller helps the mobile robot to avoid critical obstacles around the reference path. It is analytically proven that the proposed hybrid control law converges the robot asymptotically to the desired path when it is safely away from obstacles. Further, when the robot detects an obstacle in its sensing range, obstacle avoidance control law acts locally and it deviates itself from the desired path to avoid collision assuring stability through a bounded tracking control law. Considering bounded sensor measurement uncertainties during obstacle detection, it is observed that the fuzzy logic-based collision-avoidance algorithm behaves in a robust manner. The hybrid controller is designed and tested in a simulated environment with the real-life parameters of a nonholonomic wheeled mobile robot. The robot is capable of traversing through the geometrically constrained desired paths avoiding obstacles in an improved manner compared to the existing method. Moreover, the robot is also capable of returning back to its original path when obstacles are safely away. The performance of the novel hybrid control strategy is illustrated for several geometrically constrained paths via simulation to show its effectiveness in terms of fulfilling dual objectives of path following and simultaneous collision avoidance in an improved manner. [ABSTRACT FROM AUTHOR]