Safe and smooth mobile robot navigation through cluttered environment from the initial position to goal with optimal path is required to achieve intelligent autonomous ground vehicles. There are countless research contributions from researchers aiming at finding solution to autonomous mobile robot path planning problems. This paper presents an overview of nature-inspired, conventional, and hybrid path planning strategies employed by researchers over the years for mobile robot path planning problem. The main strengths and challenges of path planning methods employed by researchers were identified and discussed. Future directions for path planning research is given. The results of this paper can significantly enhance how effective path planning methods could be employed and implemented to achieve real-time intelligent autonomous ground vehicles.

/pdf/an-overview-of-nature-inspired-conventional-and-hybrid-4f91whzd9z.pdf

An Overview of Nature-Inspired, Conventional, and Hybrid Methods of Autonomous Vehicle Path Planning

This paper introduces a new hybrid control architecture for solving the navigation problem of mobile robot in an unknown dynamic environment based on an actual-virtual target switching strategy. This hybrid architecture is a combination of deliberative and reactive architectures which consists of three layers: modeling, planning and reaction. The deliberative architecture produces collision-free with shortest-distance path, while using the reactive architecture generates safe and time minimal navigation path. The proposed approach differs from previous ones in its integration architecture, the control techniques implemented in each module, and interfaces between the deliberative and reactive components. Validity and feasibility of the proposed approach are verified through simulation and real robot experiments.

/pdf/a-hybrid-control-architecture-for-autonomous-mobile-robot-1guu85fufi.pdf

A hybrid control architecture for autonomous mobile robot navigation in unknown dynamic environment

This paper describes a novel reactive obstacle avoidance approach for mobile robot navigation in unknown and dynamic environment. This approach is developed based on the “situated-activity paradigm” and a “divide and conquer” strategy which steers the robot to move among unknown obstacles and towards a target without collision. The proposed approach entitled the Virtual Semi-Circles(VSC). The VSC approach lies in integration of 4 modules: division, evaluation, decision and motion generation. The VSC proposes a comprehensive obstacle avoidance approach for robust and reliable mobile robot navigation in cluttered, dense and complex unknown environments. The simulation result shows the feasibility and effectiveness of the proposed approach.

/pdf/a-reactive-collision-avoidance-approach-for-mobile-robot-in-3ahpyqmty1.pdf

A reactive collision avoidance approach for mobile robot in dynamic environments

This paper introduces a new behavior-based collision avoidance approach for mobile robot navigation in unknown and dynamic environments, which called Nearest Virtual-Target NVT. The NVT approach was developed based on a modeling-planning-reaction configuration. In modeling module, sensory information is integrated to construct a local model of environment which represents obstacles distribution and free obstacle areas in a part of robot's work space. The planning module uses the “actual-virtual target switching strategy” to compute obstacle free paths towards the target. The robot motion generation is handled by the reaction module. The reaction module applies a fuzzy controller to control the robot's rotational and translational velocities. The contribution of this approach is solving navigation difficulties presented in previous approaches for successful motion of the robot towards the target in troublesome scenarios such as narrow passages, very dense, cluttered and dynamic environments. Feasibility and effectiveness of the proposed approach are verified through simulation and real robot experiments. Eventually, advantages and limitations of this approach are discussed.

A behavior-based approach for collision avoidance of mobile robots in unknown and dynamic environments

This paper presents a biologically inspired architecture for rapid real-time control of autonomous or semi-autonomous vehicles based on a neural model of the escape response of the American cockroa...

A reflexive vehicle control architecture based on a neural model of the cockroach escape response

The present paper describes a real-time motion-planning approach which lies in the integration of three techniques: fuzzy logic (FL), virtual force field (VFF), and boundary following (BF). The FL algorithm is used for velocity control based on sonar readings. The path-planning algorithm is based on the VFF and BF methods. The proposed navigation system differs from previous works in terms of using different algorithms for planning robot motion. Other improvements concern functional and computational aspects of the design and integration of the modules. The robot shows robust performance in complex situations and local minimum scenarios. Simulation results show the effectiveness of the developed system in various environments with long walls, U-shaped, maze-like, and other types of clutter.

/pdf/virtual-force-field-algorithm-for-a-behaviour-based-4dkh0r273h.pdf

Virtual force field algorithm for a behaviour-based autonomous robot in unknown environments

Water quality has become a severe concern on a global scale, owing mostly to the rapid increase of the nation's development. According to Malaysia's Natural Resources and Environment Ministry, poor water management is the primary cause of the country's water quality problems. Many river systems are polluted by home and industrial pollutants, according to the findings of research in Malaysia and comparable difficulties in a few other nations. Hence, the following are the research's goals: (1) To look into what is causing the infractions. (2) To undertake the inquiry, develop a thorough hypothesis. (3) To detect dangerous germs by sampling the most usually infected regions. (4) To develop a test for Total Coliform violations in chlorine-treated water at the water treatment plant and in water distribution systems. As a result, the most major barrier to ensuring the safe delivery of treated water to consumers and protecting human health from water-related diseases is the drinking water treatment process. As a result, practically all water treatment systems around the world, including those in the USA, use a chlorine-based procedure to disinfect the water system during treatment. According to studies, the ideal way of disinfecting treated water is both safe and beneficial. Any sort of pandemic or biologically caused disease has no societal implications. Many countries began to suffer in 2009 as a result of e-coli and total coliform contamination in their water systems, leading to ambiguity in disinfection methods. Some water from UNMC's coolers was within the guidelines, while some exceed them. Water coolers at Block E (614 m) and Block B (605 m), for example, measured 12 CFU/100 ml and 11 CFU/100 ml, respectively. Water coolers should be cleaned regularly to ensure that they perform correctly. Further, the microbial population was found to be higher at water storage tanks than that is at the water cooler. This demonstrates how a water cooler fulfils its purpose of filtering and trapping germs to provide clean drinking water. 

/pdf/experimental-study-on-total-coliform-violations-in-the-3exxzzsz.pdf

Experimental study on total coliform violations in the complied NH2CL, O3, and UV treated municipal water supply system

Near infrared spectroscopy (NIRS) is a widely used analytical technique for non-destructive analysis of various materials including food fraud detection. However, the accurate calibration of NIRS data can be challenging due to the complexity of the underlying relationships between the spectral data and the target variables of interest. Ensemble learning, which combines multiple models to make predictions, has been shown to improve the accuracy and robustness of predictive models in various domains. This paper proposes stacking ensemble machine learning (SEML) for calibration of NIRS data with two levels of learning involved. Eight (8) spectroscopy datasets from public repository and previously published works by the authors are used as the case study. The model well generalized the data in the respective regression tasks with   of at least  »0.8 in the test samples and in the respective classification tasks with classification accuracy (CA) of at least »0.8 also. In addition, the proposed SEML can improve, or at least reach par with, the accuracy of individual base learners in both train and test samples for all cases of regression and classification datasets. It shows superior performance in test samples for both regression and classification datasets with respectively  ranging from 0.86 to nearly 1 and CA ranging from 0.89 to 1.
ABSTRAK: Spektroskopi inframerah dekat (NIRS) adalah teknik analitikal yang banyak digunakan bagi analisa pelbagai bahan tanpa merosakkan bahan termasuk ketika mengesan penipuan makanan. Walau bagaimanapun, kalibrasi yang tepat bagi data NIRS adalah sangat mencabar kerana hubungan antara data spektral dan pemboleh ubah sasaran yang ingin dikaji bersifat kompleks. Gabungan pembelajaran (Ensemble learning), iaitu gabungan pelbagai model bagi membuat prediksi, telah terbukti dapat meningkatkan ketepatan dan kecekapan model prediksi dalam pelbagai bentuk. Kajian ini mencadangkan Turutan Gabungan Pembelajaran Mesin (Stacking Ensemble Machine Learning ) (SEML), bagi teknik penentu ukuran data NIRS melibatkan dua tahap pembelajaran. Lapan (8) set data spektroskopi dari repositori awam dan kajian terdahulu oleh pengarang telah digunakan sebagai kes kajian. Model ini menggeneralisasi data dalam tugas regresi  masing-masing sebanyak ?0.8 bagi sampel ujian dan pengelasan tugas masing-masing dengan ketepatan klasifikasi (CA) sekurang-kurangnya ?0.8. Tambahan, SEML yang dicadangkan ini dapat membantu, atau sekurang-kurangnya setanding dengan ketepatan individu dalam pembelajaran berkumpulan dalam kedua-dua sampel latihan dan ujian bagi semua kes set data regresi dan klasifikasi. Ia menunjukkan prestasi terbaik dalam sampel ujian bagi kedua-dua kumpulan set data regresi dan klasifikasi dengan masing-masing  antara 0.86 hingga hampir 1 dan antara julat 0.89 hingga 1 bagi CA.

Spectroscopy data calibration using stacked ensemble machine learning

Real-time face mask detection on smart thermal body screening with Haar cascade classifier

Energy is existing everywhere and always being transferred from one state to another, even with the slightest movement of an object or matter. The human body uses potential energy and transfers it into kinetic energy when walking. Harvesting energy from the human locomotion is clean. The aim of this research is to study the biomechanics of a human body during motion and exploit the kinetic and vibrational energies to generate electricity to power a mechanized prosthetic or a battery for storage and later uses. The system uses a vertical spring mass system that will move vertically up and down. This movement will be the drive force to rotate the rotor of the generator or to move the cylindrical rod inside the LVDT sensor thus generating electricity. The spring mass system moves when it is subjected with external force, breaking its equilibrium state. The external force is caused by the displacement of center of mass and the heel strike of the foot when the person is walking. Among the various ways to harvest energy through human locomotion, the limb movement generates most energy compared to body heat and heartbeat methods. Therefore, this research implements the limb movement and electromagnetic induction generator properties/Linear Variable Differential Transformer (LVDT) properties and integrate into a system.

Backpack Energy Harvesting System

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