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Anupam Shukla

Bio: Anupam Shukla is an academic researcher from Indian Institute of Information Technology and Management, Gwalior. The author has contributed to research in topics: Artificial neural network & Motion planning. The author has an hindex of 22, co-authored 215 publications receiving 1896 citations. Previous affiliations of Anupam Shukla include Indian Institutes of Information Technology.


Papers
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Journal ArticleDOI
TL;DR: This paper solves the problem of robotic path planning using a combination of A* algorithm and Fuzzy Inference and the resulting FIS was easily able to plan the path of the robot.
Abstract: Robotic Path planning is one of the most studied problems in the field of robotics. The problem has been solved using numerous statistical, soft computing and other approaches. In this paper we solve the problem of robotic path planning using a combination of A* algorithm and Fuzzy Inference. The A* algorithm does the higher level planning by working on a lower detail map. The algorithm finds the shortest path at the same time generating the result in a finite time. The A* algorithm is used on a probability based map. The lower level planning is done by the Fuzzy Inference System (FIS). The FIS works on the detailed graph where the occurrence of obstacles is precisely known. The FIS generates smoother paths catering to the non-holonomic constraints. The results of A* algorithm serve as a guide for FIS planner. The FIS system was initially generated using heuristic rules. Once this model was ready, the fuzzy parameters were optimized using a Genetic Algorithm. Three sample problems were created and the quality of solutions generated by FIS was used as the fitness function of the GA. The GA tried to optimize the distance from the closest obstacle, total path length and the sharpest turn at any time in the journey of the robot. The resulting FIS was easily able to plan the path of the robot. We tested the algorithm on various complex and simple paths. All paths generated were optimal in terms of path length and smoothness. The robot was easily able to escape a variety of obstacles and reach the goal in an optimal manner.

110 citations

Journal ArticleDOI
TL;DR: This work utilizes a newly proposed methodology named ‘grey wolf optimization (GWO)’ to solve the path planning problem of three Dimensional UAV, whose task is to find the feasible trajectory while avoiding collision among obstacles and other UAVs.
Abstract: Robot path planning is essential to identify the most feasible path between a start point and goal point by avoiding any collision in the given environment. This task is an NP-hard problem and can be modeled as an optimization problem. Many researchers have proposed various deterministic and meta-heuristic algorithm to obtain better results for the path planning problem. The path planning for 3D multi-Unmanned Aerial Vehicle (UAV) is very difficult as the UAV has to find a viable path between start point and goal point with minimum complexity. This work utilizes a newly proposed methodology named ‘grey wolf optimization (GWO)’ to solve the path planning problem of three Dimensional UAV, whose task is to find the feasible trajectory while avoiding collision among obstacles and other UAVs. The performance of GWO algorithm is compared with deterministic algorithms such as Dijkstra, A* and D*, and meta-heuristic algorithms such as Intelligent BAT Algorithm (IBA), Biogeography Based Optimization (BBO), Particle Swarm Optimization (PSO), Glowworm Swarm Optimization (GSO), Whale Optimization Algorithm (WOA) and Sine Cosine Algorithm (SCA), so as to find the optimal method. The results show that GWO algorithm outperforms the other deterministic and meta-heuristic algorithms in path planning for 3D multi-UAV.

91 citations

Journal ArticleDOI
TL;DR: A comparative analysis of various nature inspired algorithms to select optimal features/variables required for aiding in the classification of affected patients from the rest shows Binary Bat Algorithm outperformed traditional techniques like Particle Swarm Optimization (PSO), Genetic Algorithm and Modified Cuckoo Search Algorithm with a competitive recognition rate on the dataset of selected features.

70 citations

Journal ArticleDOI
TL;DR: A new algorithm for solving the problem of path planning in a static environment by making use of an algorithm developed earlier by the authors called Multi-Neuron Heuristic Search (MNHS), a modified A^@?

57 citations

Book ChapterDOI
01 Jan 2013
TL;DR: A new nature inspired meta-heuristics algorithm called Egyptian Vulture Optimization Algorithm which primarily favors combinatorial optimization problems which is derived from the nature, behavior and key skills of the Egyptian Vultures for acquiring food for leading their livelihood.
Abstract: In this paper we have introduced for the first time a new nature inspired meta-heuristics algorithm called Egyptian Vulture Optimization Algorithm which primarily favors combinatorial optimization problems. The algorithm is derived from the nature, behavior and key skills of the Egyptian Vultures for acquiring food for leading their livelihood. These spectacular, innovative and adaptive acts make Egyptian Vultures as one of the most intelligent of its kind among birds. The details of the bird’s habit and the mathematical modeling steps of the algorithm are illustrated demonstrating how the meta-heuristics can be applied for global solutions of the combinatorial optimization problems and has been studied on the traditional 0/1 Knapsack Problem (KSP) and tested for several datasets of different dimensions. The results of application of the algorithm on KSP datasets show that the algorithm works well w.r.t optimal value and provide the scope of utilization in similar kind of problems like path planning and other combinatorial optimization problems.

56 citations


Cited by
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01 Jan 2002

9,314 citations

01 Jan 2004
TL;DR: Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance and describes numerous important application areas such as image based rendering and digital libraries.
Abstract: From the Publisher: The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

3,627 citations

Posted Content
TL;DR: This paper proposes gradient descent algorithms for a class of utility functions which encode optimal coverage and sensing policies which are adaptive, distributed, asynchronous, and verifiably correct.
Abstract: This paper presents control and coordination algorithms for groups of vehicles. The focus is on autonomous vehicle networks performing distributed sensing tasks where each vehicle plays the role of a mobile tunable sensor. The paper proposes gradient descent algorithms for a class of utility functions which encode optimal coverage and sensing policies. The resulting closed-loop behavior is adaptive, distributed, asynchronous, and verifiably correct.

2,198 citations

09 Mar 2012
TL;DR: Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems as mentioned in this paper, and they have been widely used in computer vision applications.
Abstract: Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; ckuan@econ.sinica.edu.tw. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.

2,069 citations