Accelerometer for mobile robot positioning

Abstract

An evaluation of a low-cost, small sized solid state accelerometer is described in this paper. The sensor is intended for positioning of a mobile robot or platform. Acceleration signal outputted by the sensor is doubly integrated with time which yields the traveled distance. Bias offset drift exhibits in the acceleration signal is accumulative and the accuracy of the distance measurement deteriorates with time due to the integration. Kalman Filter is used to reduce errors caused by random noises. The random bias drift of the accelerometer was found by experiment to be 2.5mg. The accelerometer was moved back and forth three times for a distance of 40cm with an acceleration of 8 d s 2 . The final distance error accumulated was -1.OScm. The bias drift rate due to temperature was O.lOSpg/s when the accelerometer was placed at room temperature. The results show that the accelerometer could be a viable solution as a short duration distance-measuring device for a mobile robot or platform.

CAN Network Based Longitudinal Velocity Measurement Using Accelerometer and GPS Receiver for Automobiles

“…Where, Vref -reference voltage of the ADC which is 3.3 V N -number of bits of resolution of the ADC which is 12 S -sensitivity of the accelerometer which is 0.0204 V/ms -2 Q X (t) -quantized data of X output at time t offset -output voltage of the accelerometer at rest in V The velocity v XA (t) in ms -1 is obtained by integrating the acceleration [17], and is given by,…”

Gait Based Directional Bias Detection of Four-Legged Walking Robots

“…According to previous studies, the position of a robot can be measured by using acceleration sensors (Liu & Pang, 1999). Since the data from acceleration sensors have accumulative error, other position information source like GPS can be useful to compensate the error.…”

Cooperative Localization in GPS-Limited Urban Environments

“…They also experience compounding measurement error as a function of the number of steps taken due to both approximation errors and sensor limitations at each step. Although individual personal navigation systems often feature mechanisms for periodic correction [1,6,7], these techniques require preplanned and preinstalled infrastructure, maps, or even manual intervention to correct positioning error, making rapid deployment difficult or placing undue burden on human operators.…”