Dilip Kumar Pratihar


Mechanical Engineering




  • Prof-in-Charge, Examinations

Research Areas

D.K. Pratihar has made significant contributions in design and development of intelligent autonomous systems in various fields of Engineering Science, namely robotics, manufacturing science, and others. He has proposed adaptive motion planning schemes for intelligent robots using the principle of soft computing, whose performances have been tested on real robots. In relation to automation in manufacturing processes, he has developed expert systems using soft computing to establish their input-output relationships both in forward and reverse directions.
He is the founder of Soft Computing Lab. in the Department of Mechanical Engineering, and Centre for Excellence in Robotics.
Notable Contributions in Research:
  • Worked extensively on Kinematics, Dynamics, Control and Intelligence Issues of various types of robots, namely manipulators, wheeled robots, six-legged robots, humanoid robots, drones etc.
    Designed and developed both orthotic and prosthetic devices in the field of Rehabilitation Robotics.
    Recently working on design and development of multi-purpose intelligent robot for agricultural applications.
    Worked on various fields of Manufacturing Science, namely Metal Cutting; Metal Forming; Un-conventional Machining; Welding; Casting, and others.
    Has extensively worked on Electron Beam Welding of Similar, Dissimilar, Reactive materials.
    Worked extensively on the fundamentals of Soft Computing.
    Developed Genetic-Fuzzy Systems, Genetic-Neural Systems, Genetic-Neuro-Fuzzy Systems to evolve adaptive motion planners, and adaptive controllers for the intelligent and autonomous robots; expert systems related to medical diagnosis and automated manufacturing processes.
    Proposed adaptive fuzzy clustering tool to yield both compact as well as distinct clusters (data mining).
    Proposed efficient dimensionality reduction technique for visualization (data mining).
    Recently developed an adaptive and efficient nature-inspired optimization tool named Bonobo Optimization (BO) tool; implemented for both single objective and multi-objective optimizations.
    Developed fuzzy reasoning tool to ensure both high accuracy as well as good interpretability.
    Developed intelligent optimization tool, namely Bonobo Optimizer.

  • A genetic-fuzzy approach for mobile robot navigation among moving obstacles D.K. Pratihar, K. Deb, A, Ghosh By International Journal of Approximate Reasoning 20 145-172 (1999)
  • Laser forming of a dome-shaped surface: Experimental investigations, statistical analysis and neural network modeling K. Maji, D.K. Pratihar, A.K. Nath By Optics and Laser in Engineering 53 31-42 (2014)
  • Time-optimal, collision-free navigation of a car-like mobile robot using a neuro-fuzzy approach N.B. Hui, V. Mahendar, D.K. Pratihar By Fuzzy Sets and Systems 157,16 2171-2204 (2006)
  • Fuzzy logic-based screening and prediction of adult psychoses: a novel approach S. Chattopadhyay, D.K. Pratihar, S.C. De Sarkar By IEEE Trans. on Systems,Man and Cybernetics, Part A 39,2 381-387 (2009)
  • A genetic algorithm-based multi-objective shape optimization scheme for cementless femoral implant S. Chanda, S. Gupta, D.K. Pratihar By Trans. on ASME, Journal of Biomechanical Engineering 137 034502-1-12 (2015)
  • Optimal path and gait generations simultaneously of a six-legged robot using a GA-fuzzy approach D.K. Pratihar, K. Deb, A, Ghosh By Robotics and Autonomous Systems 41,1 1-20 (2002)
  • Design of a genetic-fuzzy system to predict surface finish and power requirement in grinding A.K. Nandi, D.K. Pratihar By Fuzzy Sets and Systems 148 487-504 (2004)
  • Optimization of bead geometry in electron beam welding using a genetic algorithm V. Dey, D.K. Pratihar et al. By Journal of Materials Processing Technology 209 1151-1157 (2009)
  • Modeling of TIG welding process using conventional regression analysis and neural network-based approaches P. Dutta, D.K. Pratihar By Journal of Materials Processing Technology 184,1-3 56-68 (2007)
  • Identification of flow regimes using conductivity probe signals and neural networks for counter-current gas-liquid two-phase flow S. Ghosh, D.K. Pratihar, B. Maiti, P.K. Das By Chemical Engineering Science 84 417-436 (2012)
  • Not Available.

Ph. D. Students

Anand Ronald K

Area of Research: Robotics

Anitesh Kumar Singh

Area of Research: laser assisted rapid manufacturing

Anupam Kundu

Area of Research: Electron Beam Welding

Biswesh Ranjan Acharya

Area of Research: Optimisation of machining parameters for micro-electrochemical machining

Bondada Venkatasainath

Area of Research: Robot Vision

Deepak Deshmukh

Area of Research: Robotics

Piyush Kumar Dongre

Area of Research: Digital Image Processing using Soft Computing

Pradeep Nahak

Area of Research: Robotics

Pushpendra Gupta

Area of Research: Many objectives optimization

Ravi Tiwari

Area of Research: Robotics

Santosh Kumar Gupta

Area of Research: Modeling of Laser Processing

Vidyapati Kumar

Area of Research: Optimization, Robotics