Estimating uncertain spatial relationships in robotics
Autonomous robot vehicles
GENITOR II.: a distributed genetic algorithm
Journal of Experimental & Theoretical Artificial Intelligence
A genetic technique for robotic trajectory planning
Telematics and Informatics - Special issue: artificial intelligence and advanced computing technologies for space applications
Fuzzy logic: intelligence, control, and information
Fuzzy logic: intelligence, control, and information
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Mobile Robot Localization and Map Building: A Multisensor Fusion Approach
Mobile Robot Localization and Map Building: A Multisensor Fusion Approach
Globally Consistent Range Scan Alignment for Environment Mapping
Autonomous Robots
Robot Pose Estimation in Unknown Environments by Matching 2D Range Scans
Journal of Intelligent and Robotic Systems
Fastslam: a factored solution to the simultaneous localization and mapping problem with unknown data association
Cooperative localization and multi-robot exploration
Cooperative localization and multi-robot exploration
DP-SLAM: fast, robust simultaneous localization and mapping without predetermined landmarks
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Adaptive genetic operators based on coevolution with fuzzybehaviors
IEEE Transactions on Evolutionary Computation
Sonar based simultaneous localization and mapping using a neuro evolutionary optimization
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Novel solutions for Global Urban Localization
Robotics and Autonomous Systems
CIRA'09 Proceedings of the 8th IEEE international conference on Computational intelligence in robotics and automation
Evidence reasoning machine based on DSmT for mobile robot mapping in unknown dynamic environment
ROBIO'09 Proceedings of the 2009 international conference on Robotics and biomimetics
Fuzzy uncertainty modeling for grid based localization of mobile robots
International Journal of Approximate Reasoning
Integrated PSO and line based representation approach for SLAM
Proceedings of the 2011 ACM Symposium on Applied Computing
Mobile robot localization through identifying spatial relations from detected corners
IWINAC'11 Proceedings of the 4th international conference on Interplay between natural and artificial computation: new challenges on bioinspired applications - Volume Part II
Mobile robot map building from time-of-flight camera
Expert Systems with Applications: An International Journal
Generation of an adaptive simulation driven by product trajectories
Journal of Intelligent Manufacturing
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This paper presents a novel method of integrating fuzzy logic (FL) and genetic algorithm (GA) to solve the simultaneous localization and mapping (SLAM) problem of mobile robots. The core of the proposed SLAM algorithm is based on an island model GA (IGA) which searches for the most probable map(s) such that the associated pose(s) provides the robot with the best localization information. Prior knowledge about the problem domain is transferred to GA in order to speed up the convergence. Fuzzy logic is employed to serve this purpose and allows the IGA to conduct the search starting from a potential region of the pose space. The underlying fuzzy mapping rules infer the uncertainty in the robot's location after executing a motion command and generate a sample-based prediction of its current position. This sample set is used as the initial population for the proposed IGA. Thus the GA-based search starts with adequate knowledge on the problem domain. The correspondence problem in SLAM is solved by exploiting the property of natural selection, which supports better performing individuals to survive in the competition. The proposed algorithm follows essentially no assumption about the environment and has the capacity to resolve the loop closure problem without maintaining explicit loop closure heuristics. The algorithm processes sensor data incrementally and therefore, has the capability of real time map generation. Experimental results in different indoor environments are presented to validate robustness of the algorithm.