About the Authors

Ittikon Thammachantuek

Contributed equally to this work with: Ittikon Thammachantuek, Mahasak Ketcham

Roles Writing – original draft

s5907011956029@email.kmutnb.ac.th (IT); mahasak.k@itd.kmutnb.ac.th (MK)

Affiliation Department of Information Technology, Faculty of Information Technology and Digital Innovation, King Mongkut’s University of Technology North Bangkok, Bangkok, Thailand

Mahasak Ketcham

Contributed equally to this work with: Ittikon Thammachantuek, Mahasak Ketcham

Roles Writing – original draft, Writing – review & editing

s5907011956029@email.kmutnb.ac.th (IT); mahasak.k@itd.kmutnb.ac.th (MK)

Affiliation Department of Information Technology Management, Faculty of Information Technology and Digital Innovation, King Mongkut’s University of Technology North Bangkok, Bangkok, Thailand

Competing Interests

The proposed algorithm is tested in both a static environment and dynamic environment with different models. The size of the robot is taken into consideration, as well as the size of the radius of the robot and the radius of the obstacles. Based on the test results, it can be said that the MOEPSO algorithm finds optimal paths better than other algorithms in terms of path length, smoothness, and safety. In addition, it takes less processing time than other algorithms. The competing Interests of this article is about AI, IoT, Robot. This does not alter our adherence to PLOS ONE policies on sharing data and materials.