Title :
Modeling of ultrasonic range sensors for localization of autonomous mobile robots
Author :
Gutierrez-Osuna, Ricardo ; Janet, Jason A. ; Luo, Ren C.
Author_Institution :
Dept. of Electr. & Comput. Eng., North Carolina State Univ., Raleigh, NC, USA
fDate :
8/1/1998 12:00:00 AM
Abstract :
This paper presents a probabilistic model of ultrasonic range sensors using backpropagation neural networks trained on experimental data. The sensor model provides the probability of detecting mapped obstacles in the environment, given their position and orientation relative to the transducer. The detection probability can be used to compute the location of an autonomous vehicle from those obstacles that are more likely to be detected. The neural network model is more accurate than other existing approaches, since it captures the typical multilobal detection pattern of ultrasonic transducers. Since the network size is kept small, implementation of the model on a mobile robot can be efficient for real-time navigation. An example that demonstrates how the credence could be incorporated into the extended Kalman filter (EKF) and the numerical values of the final neural network weights are provided in the appendices
Keywords :
backpropagation; distance measurement; mobile robots; neural nets; probability; ultrasonic measurement; ultrasonic transducers; autonomous mobile robots localisation; backpropagation neural networks; detection probability; extended Kalman filter; mapped obstacles detection probability; multilobal detection pattern; probabilistic model; real-time navigation; sensor model; ultrasonic range sensors modelling; ultrasonic transducers; Acoustic signal detection; Backpropagation; Brain modeling; Dead reckoning; Mobile robots; Neural networks; Remotely operated vehicles; Sonar navigation; Ultrasonic transducers; Vehicle detection;
Journal_Title :
Industrial Electronics, IEEE Transactions on