Purpose: This paper aims to present a concise review on the variant state-of-the-art dynamic task allocation strategies. It presents a thorough discussion about the existing dynamic task allocation strategies mainly with respect to the problem application, constraints, objective functions and uncertainty handling methods.Design/methodology/approach: This paper briefs the introduction of multi-robot dynamic task allocation problem and discloses the challenges that exist in real-world dynamic task allocation problems. Numerous task allocation strategies are discussed in this paper, and it establishes the characteristics features between them in a qualitative manner. This paper also exhibits the existing research gaps and conducive future research directions in dynamic task allocation for multiple mobile robot systems.Findings: This paper concerns the objective functions, robustness, task allocation time, completion time, and task reallocation feature for performance analysis of different task allocation strategies. It prescribes suitable real-world applications for variant task allocation strategies and identifies the challenges to be resolved in multi-robot task allocation strategies.Originality/value: This paper provides a comprehensive review of dynamic task allocation strategies and incites the salient research directions to the researchers in multi-robot dynamic task allocation problems. This paper aims to summarize the latest approaches in the application of exploration problems.

Computer aided ovarian detection and ovarian classification is important in infertility treatment in women. In the proposed methodology, an intelligent automatic detection and ovarian classification with grading based on integration of intensity and texture features using artificial neural network is developed. Three texture features such as autocorrelation, sum average and sum variance obtained from grey-level co-occurrence matrix (GLCM) and intensity obtained using K-means clustering were fed as input to the multilayer feedforward backpropagation network for ovarian detection. Ovarian morphology was used for classification and grading of ovary. This novel technique helps the physician to grade the follicle/cyst. Performance metrics like sensitivity, specificity, accuracy, precision, F-measure, Mathew's correlation coefficient and receiver operating characteristic curve were used to prove the effectiveness of the proposed machine learning based ovarian detection (MLOD). The MLOD classifier yielded an average detection accuracy of 96% which is an increase of 2% as compared to the combined texture and intensity based ovarian classification (TIOC) algorithm.

Automatic detection of human ovarian follicles has been of increasing interest in recent years and is a significant area of women's health. Improper development of ovarian follicles has been an important reason for infertility in women. Currently, detection of ovarian follicle is done through diagnostic imaging technique called ultrasonography. Follicles differ in shape and colour. Further, the camouflaging characteristic of ultrasound images and the presence of speckle noise make the follicle detection a challenging task. In this paper, a novel method for automatic recognition of follicles in ultrasound images is proposed. Discrete wavelet transform based k-means clustering is proposed. Discrete wavelet transform is preferred due to its superior spectral temporal resolution that helps in despeckling the ultrasound images. K-means clustering is used to segment the image into different anatomical structures to yield better segmentation. Structural Similarity (SSIM), False Acceptance Rate (FAR) and False Rejection Rate (FRR) are used to demonstrate the efficiency of the proposed method.

Purpose: The rousing phenomenon of the ageing population is becoming a vital issue and demanding fulminant actions. Population ageing is a resultant of the enhanced health-care system, groovy antibiotics, medications and economic well-being. Old age leads to copious amounts of ailments. Aged people, owing to their reduced mobility and enervating disabilities, tend to rely upon caretakers and/or nursing personnel. With the increasing vogue of nuclear families in the society, the elderly are at the risk of being unveiled to emotional, physical and fiscal insecurities in the years to come. Caring for those seniors will be an enormous undertaking. Design/methodology/approach: There is a dire need for an intelligent assistive system to meet out the requirements of continuous holistic care and monitoring. Assistive robots and systems used for elderly care are studied. The design motivation for the robots, elderly–robot interaction capabilities and technology incorporated in the systems are examined meticulously. Findings: From the survey, it is suggested that the subsystems of an assistive robot revamped for better human–machine interactions will be a potential alternative to the human counterpart. Affirmable advancements in the robot design and interaction methodologies that would increase the holistic care and assistance for aged people are analyzed and listed. Originality/value: This paper reviews the available assistive technologies and suggests a synergistic model that can be adopted for the caring of the elderly.

The most popular method for transporting fluids, and gases is through pipelines. For them to work correctly, regular inspection is necessary. Humans must enter potentially dangerous environments to inspect pipelines. As a result, pipeline robots came into existence. These robots aid in pipeline inspection, protecting numerous people from harm. Despite numerous improvements, pipeline robots still have several limitations. This paper presents the design and motion planning of a wheeled type pipeline inspection robot that can inspect pipelines having an inner diameter between 250 mm to 350 mm. The traditional wheeled robot design has three wheels fixed symmetrically at a 120° angle apart from each other. When maneuvering through a curved pipeline, this robot encounters motion singularity. The proposed robot fixes the wheels at different angles to address this issue, allowing the robot to stay in constant contact with the pipe's surface. Motion analysis is done for the proposed and existing robot design to study their behavior inside the pipeline. The result shows that the proposed robot avoids motion singularity and improves mobility inside pipelines. 3d printing technology aids in the development of the proposed robot. The experimental tests on the developed robot inside a 300 mm-diameter straight and curved pipeline show that the robot avoids motion singularity.

The focus of this work is to investigate the adhesion characteristicsof a permanent magnet arrangement over ferromagnetic surfacesfor wall-climbing robot applications.