Novel method of estimating surface condition for tiny mobile robot to improve locomotion performance

Tanaka, Katsuaki; Ishii, Hiroyuki; Okamoto, Y.; Kuroiwa, Daisuke; Miura, Yusaku; Endo, Daiki; Mitsuzuka, Junko; Shi, Qing; Okabayashi, Satoshi; Sugahara, Yusuke; Takanishi, Atsuo

doi:10.1109/iros.2015.7354308

Cited by 5 publications

(1 citation statement)

References 13 publications

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“…Disturbance signal may result in changes in the value of the process variable that must be processed to get the highest predictive control signal before entering the plant. Disturbance caused by unevenness of the ground is detected by the proximity sensor to observe changes in surface area [9]. Proximity sensor detected values are calculated to determine [10] how much additional value to be added to the self-balancing robot motor power [11] in order to remain balanced when going past the unevenness area in front of it.…”

Section: Methodsmentioning

confidence: 99%

Disturbance rejection using feed-forward control system on self balancing robot

Prasetio

Kurniawan

2018

MATEC Web Conf.

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This research implements self-balancing robot using 3 algorithms. There are PID Controller, Ensemble Kalman Filter and Feed-Forward Control system. The PID controller function is as a robot equilibrium control system. The Kalman Ensemble algorithm is used to reduce noise measurement of accelerometer and gyroscope sensors. The PID controller and Ensemble Kalman filter were implemented on self-balancing robot in previous research. In this paper, we added the Feed-Forward controller that serves to detect disturbance derived from the unevenness of the ground. Disturbance is detected using 2 proximity sensors. Base on test results that the system can detect disturbance with an average delay of 2.15 seconds at Kff optimal value is 2.92. Feed-Forward effects result in self-balancing robots increasing power so that the pitch of the robot changes to anticipation of disturbance.

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Section: Methodsmentioning

confidence: 99%