rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Robotic navigation aid for visually impaired people using FLC-ORCA
  name:
    name part: Muhammad Rabani Mohd Romlay
    role:
      role term: creator
    role:
      role term: author
  name:
    name part: Azhar Mohd Ibrahim
    role:
      role term: degree supervisor
  name:
    name part: Siti Fauziah Toha
    role:
      role term: degree supervisor
  name:
    name part: Muhammad Mahbubur Rashid
    role:
      role term: degree supervisor
  name:
    name part: International Islamic University Malaysia
    name part: Kulliyyah of Engineering
  type of resource: text
  genre: theses
  genre: Theses, IIUM local
  origin info:
    place:
      place term: my
    date issued: 2022
    issuance: monographic
  language:
    language term: eng
  language: languageTerm
  physical description:
    form: print
    extent: xxi, 155 leaves : color illustrations ; 30cm.
  abstract: Robotic Navigation Aid (RNA) provides an option for visually impaired people for self-navigation and allow them to travel independently. However, current research does not take into consideration the varying characteristics and different states of obstacle which is present in the surrounding. Current methods of obstacle avoidance have been one-dimensional and limited in terms of representing the real-life surrounding when it comes to simulation and experimental conduct. Navigations are done either within a confined space of clear path, passing through static objects or between other mobile robots itself. Dynamic obstacles are presented within a controlled manner without differentiating class of objects which is intelligent and have the ability to avoid obstacles by itself. While separating static and dynamic objects are clear-cut and obvious, the same could not be said about differentiating moving object and intelligent entity with unpredictable movement. Moreover, the obstacle avoidance methods which attempt to solve the problem of collision within varying state of obstacles tend to assume that other intelligent entity follows the same obstacle avoidance protocol as itself. Furthermore, the presupposition that other intelligent entity bears equal liability of collision avoidance are too far from the circumstances faced in real life. Hence, the introduction of novel methodologies of CE-CBCE & FLC-ORCA attempts to fill in the void within two correlated and fundamental fields of object detection and obstacle avoidance. The proposed CE-CBCE feature extraction method detects human, cyclist and vehicles and separates it from other moving object to be classified as intelligent entity. Sequentially, FLC-ORCA resolves the issue of diversified position, velocity, acceleration, and density of obstacles and depicts the output of responsibility to avoid collision and predicts the obstacle character in the next cycle of scanning. The CE-CBCE feature extraction method recorded 97…
  target audience: specialized
  note: by Muhammad Rabani bin Mohd Romlay
  note: Abstracts in English and Arabic.
  note: "A dissertation submitted in fulfilment of the requirement for the degree of Doctor of Philosophy (Engineering)." --On title page.
  note: Thesis (Ph.D)--International Islamic University Malaysia, 2022.
  note: Includes bibliographical references (leaves 135-155).
  subject:
    geographic code: a-my---
  identifier: http://studentrepo.iium.edu.my/handle/123456789/11497
  record info:
    record content source: UIAM
    record creation date: 230720
    language of cataloging:
      language term: eng