Autonomous Mobile Robot based on BehaviourBased Robotic using V-REP Simulator–Pioneer P3-DX Robot

Abstract

This article describes the design and implementation of behavior-based robotic (BBR) algorithm on a wheeled mobile robot (WMR) Pioneer P3-DX in a maze exploration mission using V-REP simulator. This robot must trace and search for targets placed randomly on a labyrinth. After successfully meeting the objective, robot runs back to home position using the nearest path. Robot navigation system applies BBR algorithm to reach the target using behavior modules which work simultaneously to obtain the desired robot’s trajectory. The most fundamental behavior which is highly affordable to build on the robot system is a wall-following behavior. To make the robot could follow the wall in a safe, smooth and responsive condition, proportional-integral-derivative (PID) controller is applied. PID controller runs by utilizing the reading of sixteen proximity sensors carried on Pioneer P3-DX robot toward the expected wall distance while the robot is exploring the labyrinth. To ensure the designed system works properly, several tests were conducted, including BBR test and PID controller test. BBR test shows that the system can choose the shortest track when returning to home position. The PID controller test produces robot movement with maximum deviation and settling time for about 0.013 m and 30 seconds, respectively.

Time-Varying Formation Tracking Control for Unmanned Aerial Vehicles with the Leader’s Unknown Input and Obstacle Avoidance: Theories and Applications

“…The formation controller runs at 100 Hz. The gain constants φ = 0.2, K = 3, the potential field function parameter K uav = 0.3, r uav = 0.5 m, K obs = 0.5, r obs = 0.5 m. According to the obstacle avoidance controller (10), UAVs can maintain a safe relative distance, and obstacles in the formation process will also be avoided. Thus, the safety of UAV system can be guaranteed compared with [24], which cannot realize the obstacle avoidance.…”

“…Remark 2. Since uncertainties exist in the actual experiment, the kinematic model of UAV can be described by ẋi = u i (t) + d i (t), where d i stands for the uncertainties of the input of the follower UAV i which has an upper bound δ that satisfies |d i (t)| < δ; then, parameter φ in Equation (10) needs to be adjusted to φ which satisfies φ ≥ δ + u max 0 ; then, the unknown input disturbance of the follower can be suppressed.…”

“…has won widespread attention. Formation control has always been a hot research topic, and various classical control methods have been investigated and applied, such as leader-follower [8], virtual structure [9], and behavior [10] based formation control methods. With the development of formation tracking control, the consensus based technique has caught researchers' eyes.…”

Sistem Kendali Proporsional pada Robot Penghindar Halangan (Avoider) Pioneer P3-DX

“…Kendali proporsional merupakan kendali yang tindakan koreksi yang dilakukan sebanding dengan besar kesalahan yang terjadi [13], dan akan semakin kecil jika semakin dekat dengan target yang dituju. Secara sederhana dapat dikatakan bahwa keluaran (output) kendali proporsional merupakan hasil perkalian antara konstanta proporsional Kp dengan besarnya kesalahan (error) yang terjadi.…”

Desain Robot Holonomic berbasis Roda Mecanum dengan Arm Manipulator

“…Faktor lainnya adalah nilai PWM yang sama, output kecepatan yang dihasilkan oleh motor DC tidak sama sehingga tidak seimbang kecepatan antara satu roda dengan yang lain. Pada pengembangan selanjutnya hal ini bisa diperbaiki dengan melakukan penambahan metode kendali seperti pengendali PID (Proporsional, Integral dan Derivatif) [19], [20].…”