TY - GEN
T1 - Near-optimal energy fuzzy parking of mobile robots
AU - Khoukhi, Amar
AU - Demirli, Kudret
PY - 2009
Y1 - 2009
N2 - In this paper, the trajectory-planning problem of a mobile robot is studied with an application to near optimal energy parking. A hybrid data-driven neuro-fuzzy system composed of two steps is developed; first, we introduce a preprocessing step involving offline trajectory parking, and generating reference optimal energy trajectories, while satisfying several constraints related to robot kinematics and dynamics and parking lot limitations. The discrete augmented Lagrangean is implemented to solve the resulting non-linear and non-convex optimal control problem. The outcomes of this pre-processing step allow building a neuro-fuzzy inference system to learn and capture the robot multi-objective dynamic behavior. The second step is a sensor-based neuro-fuzzy navigation scheme. From the learnt optimal energy behavior dataset, a 6-input/2-output ANFIS network is built for online parking. This network considers the three range measurements obtained from three sonar sensors mounted at 3 directions at the front left corner of the robot. In addition, the discrepancy between the current measured distance and the previous measured one, has been implemented to generate a control output consisting of the robot motor torques. First results based on real dimensions of a typical car, demonstrate the effectiveness of the proposed controller in practical car maneuvers.
AB - In this paper, the trajectory-planning problem of a mobile robot is studied with an application to near optimal energy parking. A hybrid data-driven neuro-fuzzy system composed of two steps is developed; first, we introduce a preprocessing step involving offline trajectory parking, and generating reference optimal energy trajectories, while satisfying several constraints related to robot kinematics and dynamics and parking lot limitations. The discrete augmented Lagrangean is implemented to solve the resulting non-linear and non-convex optimal control problem. The outcomes of this pre-processing step allow building a neuro-fuzzy inference system to learn and capture the robot multi-objective dynamic behavior. The second step is a sensor-based neuro-fuzzy navigation scheme. From the learnt optimal energy behavior dataset, a 6-input/2-output ANFIS network is built for online parking. This network considers the three range measurements obtained from three sonar sensors mounted at 3 directions at the front left corner of the robot. In addition, the discrepancy between the current measured distance and the previous measured one, has been implemented to generate a control output consisting of the robot motor torques. First results based on real dimensions of a typical car, demonstrate the effectiveness of the proposed controller in practical car maneuvers.
KW - Augmented lagrangian
KW - Mobile robots
KW - Near-optimal-energy parking
KW - Neuro-fuzzy control
KW - Trajectory planning
UR - http://www.scopus.com/inward/record.url?scp=79953899959&partnerID=8YFLogxK
U2 - 10.1109/IEEEGCC.2009.5734288
DO - 10.1109/IEEEGCC.2009.5734288
M3 - Conference contribution
AN - SCOPUS:79953899959
SN - 9781424438853
T3 - 2009 5th IEEE GCC Conference and Exhibition, GCC 2009
BT - 2009 5th IEEE GCC Conference and Exhibition, GCC 2009
ER -