Daniel Opoku

Electrical and Computer Engineering

North Carolina A&T State University

Email: dopoku AT aggies.ncat.edu




Research Intersts

Automatic Control and Robotic, Machine Learning, Artificial Intelligence, Approximate Reasoning (Fuzzy Inference Systems), Perception and Image Processing



BSc:2007, Kwame Nkrumah University of Science and Technology, Ghana, Electrical and Electronic Engineering

PhD: 2013, North Carolina A&T State University, Electrical Engineering



My Google Scholar webpage: (publications)

  1. D Opoku, A Homaifa, E Tunstel, "The Ar-Star (A r*) Pathfinder.", International Journal of Computer Applications 67, 2014
  2. D Opoku, A Homaifar, EW Tunstel, Towards Incremental Ar-Star , Advance Trends in Soft Computing, 191-202, 2014


Current Research


  1. Ph.D. Studies: A Novel Approach to Intelligent Navigation of a Mobile Robot in a Dynamic and Cluttered Indoor Environment. This research involves the development and implementation of a novel navigation technique for a mobile robot operating in a cluttered and dynamic indoor environment. Three distinct but interrelate parts are considered, namely, localization, mapping and path planning. The localization part is addressed using dead-reckoning (odometry). A least squares numerical approach has been used to calibrate the odometer parameters to minimize the effect of systematic errors on the performance, and an intermittent resetting technique, which employs RFID tags placed at known locations in the indoor environment in conjunction with door-markers, has been developed and implemented to mitigate the errors remaining after the calibration.
  2. A mapping technique that employs a laser measurement sensor as the main exteroceptive sensor has been developed and implemented for building a binary occupancy grid map of the environment. A-r-Star pathfinder, a new path planning algorithm that is capable of high performance both in cluttered and sparse environments, has been developed and implemented. Its properties, challenges, and solutions to those challenges have also been highlighted in this research. An incremental version of the A-r-Star has been developed to handle dynamic environments. Simulation experiments highlighting properties and performance of the individual components have been developed and executed using MATLAB. A prototype world has been built using the Webots™ robotic prototyping and 3-D simulation software. An integrated version of the system comprising the localization, mapping and path planning techniques has been executed in this prototype workspace to produce validation results.
  3. Post-Doctorate/Current: Concept development and feasibility study for the design and implementation of Perception Inference Engine for test and evaluation of Unmanned Autonomous Systems. He researches into the theory and application of type-2 fuzzy sets and their application to the development of inference/control systems.