Robust Monte Carlo localization for mobile robots
Artificial Intelligence
A Genetic Algorithm for Mobile Robot Localization Using Ultrasonic Sensors
Journal of Intelligent and Robotic Systems
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
Probabilistic self-localization for sensor networks
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Localization methods for a mobile robot in urban environments
IEEE Transactions on Robotics
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Abstract: In this paper, a new model for robotic self-localization using constrained evolutionary computation techniques is proposed. The approach uses a previous stage of location estimation based on Kalman filters in order to redefine the search space for a Genetic Algorithm that finds the most accurate current position of a robot. Genetic Algorithms (GAs) have the advantage of being non-gradient-based optimization methods that have an important role in non-linear systems with high noise to signal ratio. The set of solutions is modified according to natural evolution mechanisms. In addition, GAs are used as a parallel, global search technique, and it evaluates many localization solutions simultaneously, improving the probability of finding the global optimum. Experiments using the approach show the promise of the method to predict the correct position in a robotic soccer field with a error margin better than other state-of-the-art techniques.