Monte Carlo methods. Vol. 1: basics
Monte Carlo methods. Vol. 1: basics
Tracking and data association
A genetic technique for robotic trajectory planning
Telematics and Informatics - Special issue: artificial intelligence and advanced computing technologies for space applications
Robust Monte Carlo localization for mobile robots
Artificial Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Directed Sonar Sensing for Mobile Robot Navigation
Directed Sonar Sensing for Mobile Robot Navigation
Learning Adaptive Parameters with Restricted Genetic Optimization Method
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Connectionist Models of Neurons, Learning Processes and Artificial Intelligence-Part I
Numerical Methods for Unconstrained Optimization and Nonlinear Equations (Classics in Applied Mathematics, 16)
Localization System for Mobile Robots in Indoor Environments
Integrated Computer-Aided Engineering
Evolutionary computing based mobile robot localization
Engineering Applications of Artificial Intelligence
Novel solutions for Global Urban Localization
Robotics and Autonomous Systems
Evolutionary constrained self-localization for autonomous agents
Applied Soft Computing
Cascaded Evolutionary Estimator for Robot Localization
International Journal of Applied Evolutionary Computation
A novel efficient algorithm for mobile robot localization
Robotics and Autonomous Systems
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A mobile robot requires the perception of its local environment for position estimation. Ultrasonic range data provide a robust description of the local environment for navigation. This article presents an ultrasonic sensor localization system for autonomous mobile robot navigation in an indoor semi-structured environment. The proposed algorithm is based upon an iterative non-linear filter, which utilizes matches between observed geometric beacons and an a-priori map of beacon locations, to correct the position and orientation of the vehicle. A non-linear filter based on a genetic algorithm as an emerging optimization method to search for optimal positions is described. The resulting self-localization module has been integrated successfully in a more complex navigation system. Experiments demonstrate the effectiveness of the proposed method in real world applications.