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Dive into the research topics where Bibhuti Bhusan Choudhury is active.

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Featured researches published by Bibhuti Bhusan Choudhury.


conference on automation science and engineering | 2008

Generation of optimized robotic assembly sequence using ant colony optimization

Surajit Sharma; Bibhuti Bhusan Biswal; Parameshwar Dash; Bibhuti Bhusan Choudhury

A robotic assembly sequence is considered to be optimal when it minimizes assembly cost and satisfies the process constraints. The assembly cost relates to assembly operations, assembly motions and assembly direction changes. The present work utilizes an ant colony optimization (ACO) method for generation of optimized robotic assembly sequences. The method reflects the assembly cost to an energy function associated with the assembly sequence. The energy function is iteratively minimized by ACO to generate the desired optimized assembly sequence. A case study is presented to show the effectiveness of the proposed method.


International Journal of Manufacturing Technology and Management | 2010

An Overview and Comparison of Four Sequence Generating Methods for Robotic Assembly

Bibhuti Bhusan Biswal; Bibhuti Bhusan Choudhury; Debadutta Mishra; Parameswar Dash

The work presents an appropriate methodology for generation of assembly sequences. Several existing methods are studied and applied on randomly chosen products, which are then used as building blocks for development of a simplified and appropriate methodology for generation of robotic assembly sequences. The developed methodologies are validated logically. The suitability of these methods with respect to various aspects of robotic assembly is examined and the appropriate one is selected for use. The outcome of the present work is poised to make the robotic assembly system more efficient and more flexible.


international conference on industrial and information systems | 2009

Generation of robotic assembly sequence using ant colony optimization

S. Sharma; R.N. Mohapatra; Bibhuti Bhusan Biswal; Bibhuti Bhusan Choudhury

A robotic assembly sequence is considered to be optimal when it minimizes assembly cost while satisfying the process constraints. The assembly cost relates to assembly operations, assembly motions and assembly direction changes. The work utilizes an ant colony optimization (ACO) for generation of robotic assembly sequences. The method relates the assembly cost to an energy function associated with the assembly sequence. The energy function is iteratively minimized to generate an assembly sequence with a minimum assembly cost is finally generated. Example problems show the effectiveness of the method. This modified method generates feasible, stable and optimal robotic assembly sequence satisfying the assembly constraints with minimum assembly cost.


international conference on emerging trends in engineering and technology | 2008

An Optimized Multirobot Task Allocation

Bibhuti Bhusan Choudhury; Bibhuti Bhusan Biswal

Multirobot systems (MRS) hold the promise of improved performance and increased fault tolerance for large-scale problems. One of the most important aspects in the design of MRS is the allocation of tasks among the robots in a productive and efficient manner. Optimal solutions to multirobot task allocation (MRTA) can be found through an exhaustive search. Since there are ways in which m tasks can be assigned to n robots, an exhaustive search is often not possible. Task allocation methodologies must ensure that not only the global mission is achieved, but also the tasks are well distributed among the robots. This paper presents task allocation methodologies for MRS by considering their capability in terms of time and space. A two-phase solution methodology is used to solve the MRTA problem wherein the task capacity of the robots is determined during the first phase and the task allocation optimization is done during the second phase using linear programming (LP).


International Journal of Computational Vision and Robotics | 2011

A PSO based multi-robot task allocation

Bibhuti Bhusan Choudhury; Bibhuti Bhusan Biswal

Recent research trends and technology developments have been instrumental to the realisation of autonomous multi-robot systems (MRS) performing increasingly complex missions. However the selection of candidate robots from a team of robots for performing the task(s) from a set of desired tasks in order to achieve an economical and feasible process poses a difficult problem. Task distribution methodologies have to make sure that not only the global assignment is achieved, but also the tasks are well assigned among the robots. This paper presents the particle swarm optimisation (PSO) algorithm designed to address this problem. A two-phase solution methodology is used to solve the multi-robot task allocation (MRTA) problem wherein the task capability of the robots is determined during the first segment and the task allocation optimisation is done using PSO during the second segment. The solution to MRTA problem in dynamic environment is proposed using a novel PSO based algorithm and is compared with that using linear programming (LP).


international conference on computer engineering and technology | 2009

Attribute-Based Relative Ranking of Robot for Task Assignment

Bibhuti Bhusan Choudhury; Bibhuti Bhusan Biswal; R. N. Mahapatra

Availability of large number of robot configurations has made the robot workcell designers think over the issue of selecting the most suitable one for a given set of operations. The process of selection of the appropriate kind of robot must consider the various attributes of the robot manipulator in conjunction with the requirement of the various operations for accomplishing the task. The present work is an attempt to develop a systematic procedure for selection of robot based on an integrated model encompassing the manipulator attributes and manipulator requirements. The developed procedure can advantageously be used to standardize the robot selection process with view to perform a set of intended tasks. The work is also aimed at creating an exhaustive list of attributes and classifying them into different distinct categories. The coding scheme for the attributes and the relative ranking of the manipulators are illustrated with example


international conference on advanced computer control | 2009

Attribute-Based Ranking and Selection of Robots for Task Assignment

Bibhuti Bhusan Choudhury; Bibhuti Bhusan Biswal; Rabindra Narayan Mahapatra

The selection of robot for a particular application is a critical issue and it depends on large number of parameters. With changing product design parameters, improving robot technology the problem of robot selection has been of concern to users. This problem has become more difficult in recent years due to increasing complexity, available features, and facilities offered by different robots. A systematic procedure is developed in this work for selection of robot manipulators based on their attributes. This procedure can be used to standardize the robot selection procedure when the user needs to use the robot for a particular application. Subsequently, the selection procedure proceeds to rank the alternatives in the shortlist by employing the attribute based specification method. This is an attempt to create an exhaustive procedure by identifying maximum possible number of attributes. The coding scheme and the ranking procedures are illustrated with example.


Archive | 2016

Gradient Descent with Momentum Based Backpropagation Neural Network for Selection of Industrial Robot

Sasmita Nayak; Bibhuti Bhusan Choudhury; Saroj Kumar Lenka

Fast development of industrial robots and its utilization by the manufacturing industries for many different applications is a critical task for the selection of robots. As a consequence, the selection process of the robot becomes very much complicated for the potential users because they have an extensive set of parameters of the available robots. In this paper, gradient descent momentum optimization algorithm is used with backpropagation neural network prediction technique for the selection of industrial robots. Through this proposed technique maximum, ten parameters are directly considered as an input for the selection process of robot where as up to seven robot parameter data be used in the existing methods. The rank of the preferred industrial robot evaluates from the perfectly the best probable robot that specifies the most genuine benchmark of robot selection for the particular application using the proposed algorithm. Moreover, the performance of the algorithms for the robot selection is analyzed using Mean Square Error (MSE), R-squared error (RSE), and Root Mean Square Error (RMSE).


International Journal of Applied Evolutionary Computation | 2011

Appropriate Evolutionary Algorithm for Scheduling in FMS

Bibhuti Bhusan Choudhury; Bibhuti Bhusan Biswal; Debadutta Mishra; Rabi N. Mahapatra

The diffusion of flexible manufacturing systems (FMS) has not only invigorated production systems, but has also given considerable impetus to relevant analytical fields like scheduling theory and adaptive controls. Depending on the demand of the job there can be variation in batch size. The change in the jobs depends upon the renewal rate. But this does not involve much change in the FMS setup. This paper obtains an optimal schedule of operations to minimize the total processing time in a modular FMS. The FMS setup considered here consists of four numbers of machines to accomplish the desired machining operations. The scheduling deals with optimizing the cost function in terms of machining time. The powers Evolutionary Algorithms, like genetic algorithm (GA) and simulated annealing (SA), can be beneficially utilized for optimization of scheduling FMS. The present work utilizes these powerful approaches and finds out their appropriateness for planning and scheduling of FMS producing variety of parts in batch mode.


international conference on mems, nano, and smart systems | 2009

Optimized Robotic Assembly Sequence Using ACO

Surjit Sharma; Bibhuti Bhusan Biswal; Parameswar Dash; Bibhuti Bhusan Choudhury

A robotic assembly sequence is considered to be optimal when it minimizes assembly cost and satisfies the process constraints. The assembly cost relates to assembly operations, assembly motions and assembly direction changes. The present work utilizes an ant colony optimization (ACO) method for generation of optimized robotic assembly sequences. The method reflects the assembly cost to an energy function associated with the assembly sequence. The energy function is iteratively minimized by ACO to generate the desired optimized assembly sequence. A case study is presented to show the effectiveness of the proposed method.

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Debadutta Mishra

Veer Surendra Sai University of Technology

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Saroj Kumar Lenka

Mody University of Science

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D. Mishra

University College of Engineering

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S. Panda

Veer Surendra Sai University of Technology

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Supriya Sahu

Indira Gandhi Institute of Technology

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