On the representation and estimation of spatial uncertainly
International Journal of Robotics Research
Simultaneous Localization and Map-Building Using Active Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
A PSO-aided neuro-fuzzy classifier employing linguistic hedge concepts
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert algorithm based on adaptive particle swarm optimization for power flow analysis
Expert Systems with Applications: An International Journal
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Optimal Power Scheduling for Correlated Data Fusion in Wireless Sensor Networks via Constrained PSO
IEEE Transactions on Wireless Communications
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Fuzzy Systems
Fuzzy embedded mobile robot systems design through the evolutionary PSO learning algorithm
WSEAS TRANSACTIONS on SYSTEMS
Mobile robot map building from time-of-flight camera
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
The present paper shows how a recently proposed modified Particle Swarm Optimization (PSO) algorithm, called Geese PSO algorithm, can be utilized to tune a fuzzy supervisor for an adaptive Extended Kalman filter (EKF) based approach to solve simultaneous localization and mapping (SLAM) problems for mobile robots or vehicles. This type of fuzzy based adaptive EKF approach for SLAM problems has recently been shown to be an effective approach to improve performance in those situations where correct a priori knowledge of process and/or sensor/measurement uncertainty statistics i.e. Q and/or R respectively, is not available. The newly proposed system in this work is demonstrated to provide better estimation and map-building performance in comparison with those fuzzy supervisors for the adaptive EKF algorithm, where the free parameters of the fuzzy systems are tuned using basic PSO based algorithm. The utility of the proposed approach is aptly demonstrated by employing it for several benchmark environment situations with various numbers of waypoints and landmarks, where the Geese PSO algorithm could tune the fuzzy supervisor better than the basic PSO based algorithm.