Fazida Hanim Hashim
Universiti Kebangsaan Malaysia

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Strengthening programming skills among engineering students through experiential learning based robotics project Mohd Faisal Ibrahim; Aqilah Baseri Huddin; Fazida Hanim Hashim; Mardina Abdullah; Ashrani Aizzuddin Abd Rahni; Seri Mastura Mustaza; Aini Hussain; Mohd Hairi Mohd Zaman
International Journal of Evaluation and Research in Education (IJERE) Vol 9, No 4: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijere.v9i4.20653

Abstract

This study examined the educational effects in strengthening programming skills among university’s undergraduate engineering students via integration of a robotics project and an experiential learning approach. In this study, a robotics project was conducted to close the gap of students’ difficulty in relating the theoretical concepts of programming and real-world problems. Hence, an experiential learning approach using the Kolb model was proposed to investigate the problem. In this project, students were split into groups whereby they were asked to develop codes for controlling the navigation of a wheeled mobile robot. They were responsible for managing their group’s activities, conducting laboratory tests, producing technical reports and preparing a video presentation. The statistical analysis performed on the students’ summative assessments of a programming course revealed a remarkable improvement in their problem-solving skills and ability to provide programming solutions to a real-world problem.
GA-based Optimisation of a LiDAR Feedback Autonomous Mobile Robot Navigation System Siti Nurhafizah Anual; Mohd Faisal Ibrahim; Nurhana Ibrahim; Aini Hussain; Mohd Marzuki Mustafa; Aqilah Baseri Huddin; Fazida Hanim Hashim
Bulletin of Electrical Engineering and Informatics Vol 7, No 3: September 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (385.649 KB) | DOI: 10.11591/eei.v7i3.1275

Abstract

Autonomous mobile robots require an efficient navigation system in order to navigate from one location to another location fast and safe without hitting static or dynamic obstacles. A light-detection-and-ranging (LiDAR) based autonomous robot navigation is a multi-component navigation system consists of various parameters to be configured. With such structure and sometimes involving conflicting parameters, the process of determining the best configuration for the system is a non-trivial task. This work presents an optimisation method using Genetic algorithm (GA) to configure such navigation system with tuned parameters automatically. The proposed method can optimise parameters of a few components in a navigation system concurrently. The representation of chromosome and fitness function of GA for this specific robotic problem are discussed. The experimental results from simulation and real hardware show that the optimised navigation system outperforms a manually-tuned navigation system of an indoor mobile robot in terms of navigation time.
GA-based Optimisation of a LiDAR Feedback Autonomous Mobile Robot Navigation System Siti Nurhafizah Anual; Mohd Faisal Ibrahim; Nurhana Ibrahim; Aini Hussain; Mohd Marzuki Mustafa; Aqilah Baseri Huddin; Fazida Hanim Hashim
Bulletin of Electrical Engineering and Informatics Vol 7, No 3: September 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (385.649 KB) | DOI: 10.11591/eei.v7i3.1275

Abstract

Autonomous mobile robots require an efficient navigation system in order to navigate from one location to another location fast and safe without hitting static or dynamic obstacles. A light-detection-and-ranging (LiDAR) based autonomous robot navigation is a multi-component navigation system consists of various parameters to be configured. With such structure and sometimes involving conflicting parameters, the process of determining the best configuration for the system is a non-trivial task. This work presents an optimisation method using Genetic algorithm (GA) to configure such navigation system with tuned parameters automatically. The proposed method can optimise parameters of a few components in a navigation system concurrently. The representation of chromosome and fitness function of GA for this specific robotic problem are discussed. The experimental results from simulation and real hardware show that the optimised navigation system outperforms a manually-tuned navigation system of an indoor mobile robot in terms of navigation time.
GA-based Optimisation of a LiDAR Feedback Autonomous Mobile Robot Navigation System Siti Nurhafizah Anual; Mohd Faisal Ibrahim; Nurhana Ibrahim; Aini Hussain; Mohd Marzuki Mustafa; Aqilah Baseri Huddin; Fazida Hanim Hashim
Bulletin of Electrical Engineering and Informatics Vol 7, No 3: September 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (385.649 KB) | DOI: 10.11591/eei.v7i3.1275

Abstract

Autonomous mobile robots require an efficient navigation system in order to navigate from one location to another location fast and safe without hitting static or dynamic obstacles. A light-detection-and-ranging (LiDAR) based autonomous robot navigation is a multi-component navigation system consists of various parameters to be configured. With such structure and sometimes involving conflicting parameters, the process of determining the best configuration for the system is a non-trivial task. This work presents an optimisation method using Genetic algorithm (GA) to configure such navigation system with tuned parameters automatically. The proposed method can optimise parameters of a few components in a navigation system concurrently. The representation of chromosome and fitness function of GA for this specific robotic problem are discussed. The experimental results from simulation and real hardware show that the optimised navigation system outperforms a manually-tuned navigation system of an indoor mobile robot in terms of navigation time.