Arun Kumar Singh

University of Tartu

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K. Madhava Krishna

International Institute of Information Technology, Hyderabad

Houman Masnavi

Toronto Metropolitan University

Bharath Gopalakrishnan

International Institute of Information Technology, Hyderabad

Karl Kruusamäe

University of Tartu

Fatemeh Rastgar

University of Tartu

Vijay Eathakota

International Institute of Information Technology, Hyderabad

Alvo Aabloo

University of Tartu

Mithun Babu

Indian Institute of Technology Hyderabad

Dinesh Manocha

University of Maryland, College Park

Madhava Krishna

Centre for Artificial Intelligence and Robotics

Cooperative Institutions

Indian Institute of Technology Hyderabad

International Institute of Information Technology, Hyderabad

Robotics Research (United States)

Motilal Nehru National Institute of Technology

University of Tartu

Indian Institute of Science Bangalore

Galgotias University

Indian Institute of Technology Delhi

Banaras Hindu University

Graphic Era University

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A STUDY ON CUSTOMERS PERCEPTION TOWARDS PATANJALI PRODUCT WITH SPECIAL REFERENCE TO HARIDWAR

ShodhKosh Journal of Visual and Performing Arts (2024)

Arun Kumar Singh Manish Shrivastava

In the FMCG sector, there are several well-known and renowned FMCG brands. In India, the ultimate purpose of every business endeavour is customer satisfaction. Customers are the most significant economic group impacted by government and private sector economic actions. In the Indian herbal market, there are several well-known and renowned herbal brands. Patanjali is a significant FMCG product competitor in the market. The quality advantages provided by a brand affect the consumer's decision and usage of that brand over time, especially when it comes to eatable and cosmetic products. The primary purpose of this research is to better understand the consumers' personal characteristics, degree of education, and variables influencing their purchase of Patanjali Soap products.

10.29121/shodhkosh.v5.i3.2024.4160

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A simulation framework for evolution on uneven terrains for synchronous drive robot

Advanced Robotics (2013)

Gattupalli Aditya Vijay Eathakota Arun Kumar Singh K. Madhava Krishna

Abstract This paper presents a simulation framework for evolution on uneven terrains for a wheeled mobile robot (WMR) such as a synchronous drive robot. The framework lends itself as a tool capable of solving various problems, such as forward kinematic-based evolution, inverse kinematic-based evolution, path planning and trajectory tracking. This framework becomes particularly useful when we understand that the evolution problem (and hence, the various associated problems based on evolution) is particularly challenging on uneven terrain. Specifically, it is entailed to bring in the contact constraints posed by the interaction of the wheel and the ground as well as the holonomic constraints as the problem is formulated in a Differential Algebraic Equation setting. The problem becomes all the more crucial as vehicles moving on uneven terrain are becoming the order of the day. Nonetheless, there has not been much literature that deals in length the various aspects that go into the framework. This paper elaborates on the various aspects of the framework, presents simulation results on uneven terrain, where the vehicle evolves without slipping, and also presents substantial quantitative analysis in regard to wheel slippage. The main contributions of this paper are the motion planning using forward kinematic framework and a new formulation of inverse kinematics for wheeled robots on uneven terrains.

10.1080/01691864.2013.763848

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Recognition of Characters of New-born Baby's Fingerprinting Using Machine Learning

BENTHAM SCIENCE PUBLISHERS eBooks (2025)

Arun Kumar Singh

The ability to recognize fingerprints properly and quickly has been possible thanks to the development of machine learning (ML) techniques, which have revolutionized the biometric identification field. In this research work, we offer a machine-learning method for character recognition in newborn fingerprints. A collection of newborn fingerprint photos with known demographics (gender, age, and ethnicity) was gathered, and the images were pre-processed to improve contrast and reduce noise. Relevant information was extracted from the photos using feature extraction techniques, and machine learning (ML) algorithms like support vector machines (SVM), decision trees (DT), and neural networks (NN) were trained to identify the distinctive fingerprint traits of a newborn. The results of the study showed that the recommended method, which employs ML algorithms, can correctly recognize the characteristics of a newborn baby's fingerprints. Just a few of the metrics utilized to assess each ML model's performance in a hold-out validation situation were precision, recall, and F1 score. The decision tree achieved an 89% success rate, the neural network achieved 94% success rate, and the SVM algorithm achieved the success rate of 92%. These findings suggest that ML algorithms can quickly and accurately recognize the characters in a newborn baby's fingerprints. The suggested approach has numerous uses in security and healthcare systems where precise identification is essential. Accurate infant identification is essential in the healthcare industry to guarantee proper medical care and avoid medical errors. Access control in security systems can be implemented with fingerprint recognition. This study advances the use of machine learning (ML) to recognize characters in newborn baby fingerprints more accurately and efficiently. The results could have a big impact on security and healthcare systems. The suggested technique, which makes use of machine learning techniques, can quickly and precisely identify the characters in a newborn baby's fingerprints. This study adds to the development of more precise and effective techniques for the recognition of newborn baby fingerprint characters, highlighting the potential uses of machine learning in healthcare and security systems.

10.2174/9789815324099125030030

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Embedded Hardware Appropriate Fast 3D Trajectory Optimization for Fixed Wing Aerial Vehicles by Leveraging Hidden Convex Structures

2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2021)

Vivek K. Adajania Houman Masnavi Fatemeh Rastgar Karl Kruusamäe Arun Kumar Singh

Most commercially available fixed-wing aerial vehicles (FWV) can carry only small, lightweight computing hardware such as Jetson TX2 onboard. Solving non-linear trajectory optimization on these computing resources is computationally challenging even while considering only the kinematic motion model. Most importantly, the computation time increases sharply as the environment becomes more cluttered. In this paper, we take a step towards overcoming this bottleneck and propose a trajectory optimizer that achieves online performance on both conventional laptops/desktops and Jetson TX2 in a typical urban environment setting. Our optimizer builds on the novel insight that the seemingly non-linear trajectory optimization problem for FWV has an implicit multi-convex structure. Our optimizer exploits these computational structures by bringing together diverse concepts from Alternating Minimization, Bregman iteration, and Alternating Direction Method of Multipliers. We show that our optimizer outperforms the state-of-the-art implementation of sequential quadratic programming approach in optimal control solver ACADO in computation time and solution quality measured in terms of control and goal reaching cost.

Trajectory Optimization

Solver

10.1109/iros51168.2021.9636337

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Reactive Path Coordination Based on Time-Scaled Collision Cone

Journal of Guidance Control and Dynamics (2018)

Arun Kumar Singh Quang Cuong Pham

10.2514/1.g003155

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Real Time Physical Ffitness Monitoring App: BeTough

Lecture notes in electrical engineering (2021)

Pushpa Choudhary Akhilesh Kumar Choudhary Arun Kumar Singh Ashish Tripathi

Rest (music)

10.1007/978-981-15-9938-5_27

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A Novel Five Wheeled Rover for All Terrain Navigation

Lecture notes in computer science (2009)

Arun Kumar Singh Arun. H. Patil Anup Kumar Saha

Discontinuity (linguistics)

Suspension

Four-bar linkage

10.1007/978-3-642-10817-4_108

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Diagnosis of Cough and Cancer Using Image Compression and Decompression Techniques

Advances in intelligent systems and computing (2020)

Ashish Tripathi Ratnesh Prasad Srivastava Arun Kumar Singh Pushpa Choudhary Prem Chand Vashist

10.1007/978-981-15-1518-7_30

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PRIEST: Projection Guided Sampling-Based Optimization For Autonomous Navigation

IEEE Robotics and Automation Letters (2024)

Fatemeh Rastgar Houman Masnavi Basant Sharma Alvo Aabloo Jan Swevers

Efficient navigation in unknown and dynamic environments is crucial for expanding the application domain of mobile robots. The core challenge stems from the non-availability of a feasible global path for guiding optimization-based local planners. As a result, existing local planners often get trapped in poor local minima. In this paper, we present a novel optimizer that can explore multiple homotopies to plan high-quality trajectories over long horizons while still being fast enough for real-time applications. We build on the gradient-free paradigm by augmenting the trajectory sampling strategy with a projection optimization that guides the samples toward a feasible region. As a result, our method can recover from the frequently encountered pathological cases wherein all the sampled trajectories lie in the high-cost region. We push the state-of-the-art (SOTA) in the following respects. Over the navigation stack of the Robot Operating System (ROS), we show an improvement of 7-13% in success rate and up to two times in total travel time metric. On the same benchmarks and metrics, our approach achieves up to 44% improvement over model predictive path integral (MPPI) and its recent variants. On simple point-to-point navigation tasks, our optimizer is up to two times more reliable than SOTA gradient-based solvers, as well as sampling-based approaches such as the Cross-Entropy Method (CEM) and VPSTO. Codes: https://github.com/fatemeh-rastgar/PRIEST

10.1109/lra.2024.3357311

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Future Technology

CRC Press eBooks (2023)

Arun Kumar Singh Mahesh Kumar Singh Pushpa Chaoudhary Pushpendra Singh

A modern civilization that offers the most ingenious lifestyle, convenience, and security is very well-liked today. An intelligent house receives and manages data per human needs using sensors, controllers, and microcontrollers. Current systems come in a variety of control devices and configuration settings. The smart society perception is also rigorously related to facets of sustainability, such as reducing the effect of urban operations on the atmosphere, maximizing the management of energy supplies, and designing creative facilities and technologies for people. Smart city development is based on the Internet of Things (IoT) and cyber-physical systems. As a model of smart cities, they have initiated a phase of strong innovation in numerous fields (such as mobility and transport, manufacturing, health, tourism, and education), with gratitude to substantial investments made by stakeholders. This chapter focuses on the roles of IoT and artificial intelligence (AI) in the development of Smart Society 5.0. It also discusses the major challenges of a smart society, such as AI in a smart town, smart water management, smart lighting system, smart traffic control, smart parking space, smart waste management, smart police force, and smart governance to name a few. This will let people working on the infrastructure of an intelligent society see if a newly suggested facility or piece of future IoT technology is practical, affordable, or powerful.

10.1201/9781003438588-15

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