Monte Carlo methods. Vol. 1: basics
Monte Carlo methods. Vol. 1: basics
Modeling a dynamic environment using a Bayesian multiple hypothesis approach
Artificial Intelligence
Bayesian Landmark Learning for Mobile Robot Localization
Machine Learning
On the convergence rates of genetic algorithms
Theoretical Computer Science - Special issue on evolutionary computation
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
State Estimation for Nonlinear Systems Using Restricted Genetic Optimization
IEA/AIE '98 Proceedings of the 11th international conference on Industrial and engineering applications of artificial intelligence and expert systems: methodology and tools in knowledge-based systems
Estimating the absolute position of a mobile robot using position probability grids
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Differential evolution solution to the SLAM problem
Robotics and Autonomous Systems
Novel solutions for Global Urban Localization
Robotics and Autonomous Systems
A novel efficient algorithm for mobile robot localization
Robotics and Autonomous Systems
| Hi-index | 0.00 |
A new algorithm based on evolutionary computation concepts is presented in this paper. This algorithm is a non linear evolutive filter known as the Evolutive Localization Filter (ELF) which is able to solve the global localization problem in a robust and efficient way. The proposed algorithm searches stochastically along the state space for the best robot pose estimate. The set of pose solutions (the population) represents the most likely areas according to the perception and motion information up to date. The population evolves by using the log-likelihood of each candidate pose according to the observation and the motion error derived from the comparison between observed and predicted data obtained from the probabilistic perception and motion model. The algorithm has been tested on a mobile robot equipped with a laser range finder to demonstrate the effectiveness, robustness and computational efficiency of the proposed approach.