IJRCS International Journal of Robotics and Control Systems Vol. 3, No. 2, 2023, pp. 161-170 ISSN 2775-2658 http://pubs2.ascee.org/index.php/ijrcs http://dx.doi.org/10.31763/ijrcs.v3i2.944 ijrcs@ascee.org Artificial Potential Field Path Planning Algorithm in Differential Drive Mobile Robot Platform for Dynamic Environment Maulana Muhammad Jogo Samodro a,1 , Riky Dwi Puriyanto a,2* , Wahyu Caesarendra b,c,3 a Department of Electrical Engineering, Universitas Ahmad Dahlan, Indonesia b Faculty of Integrated Technologies, Universiti Brunei Darusalam, Jalan Tungku Link, BE1410 Brunei Darussalam c Faculty of Mechanical Engineering, Opole University of Technology, 76 Proszkowska St., 45-758 Opole, Poland 1 jogo.samodro@gmail.com; 2 rikydp@ee.uad.ac.id; 3 wahyu.caesarendra@ubd.edu.bn * Corresponding Author 1. Introduction The development of technology has progressed from time to time. The Industrial Revolution 4.0 is a significant achievement that has been made in the industrial world. Robotics was first developed by Computer Aided Manufacturing International (CAM-1). A robot is a device capable of performing functions performed by humans or equipment capable of working with intelligence similar to that of humans. The mobile robot is one field in the world of robotics that can move according to existing ARTICLE INFO ABSTRACT Article history Received February 12, 2023 Revised March 19, 2023 Accepted March 20, 2023 Mobile robots need path-planning abilities to achieve a collision-free trajectory. Obstacles between the robot and the goal position must be passed without crashing into them. The Artificial Potential Field (APF) algorithm is a method for robot path planning that usually used to control the robot for avoiding obstacles in front of the robot. The APF algorithm consists of an attractive potential field and a repulsive potential field. The attractive potential fields work based on the predetermined goals that generated to attract the robot to achieve the goal position. Apart of it, the obstacle generates a repulsive potential field to push the robot away from the obstacle. The robot's localization in producing the robot's position is generated by the differential drive kinematic equations of the mobile robot based on encoder and gyroscope data. In addition, the mapping of the robot's work environment is embedded in the robot's memory. According to the experiment's results, the mobile robot's differential drive can pass through existing obstacles. In this research, four test environments represent different obstacles in each environment. The track length is 1.5 meters. The robot's tolerance to the goal is 0.1 m, so when the robot is in the 1.41 m position, the robot's speed is 0 rpm. The safe distance between the robot and the obstacle is 0.2 m, so the robot will find a route to get away from the obstacle when the robot reaches that safe distance. The speed of the resulting robot decreases as the distance between the robot and the destination gets closer according to the differential drive kinematics equation of the mobile robot. Keywords Mobile robot; Artificial Potential Field; Kinematics model; Path planning; Dynamic environment This is an open-access article under the CC–BY-SA license.