Mapbuilding using self-organising networks in “really useful robots”
Proceedings of the first international conference on simulation of adaptive behavior on From animals to animats
Learning metric-topological maps for indoor mobile robot navigation
Artificial Intelligence
Xavier: a robot navigation architecture based on partially observable Markov decision process models
Artificial intelligence and mobile robots
Mobile Robot Localization Using Fuzzy Maps
IJCAI '95 Selected papers from the Workshop on Fuzzy Logic in Artificial Intelligence, Towards Intelligent Systems
Implementation of a Basic Reactive Behavior in Mobile Robotics Through Artificial Neural Networks
IWANN '97 Proceedings of the International Work-Conference on Artificial and Natural Neural Networks: Biological and Artificial Computation: From Neuroscience to Technology
Supervised Reinforcement Learning: Application to a Wall Following Behaviour in a Mobile Robot
IEA/AIE '98 Proceedings of the 11th International Conference on Industrial and Engineering Applications of Artificial In telligence and Expert Systems: Tasks and Methods in Applied Artificial Intelligence
A Refined Method for Occupancy Grid Interpretation
RUR '95 Proceedings of the International Workshop on Reasoning with Uncertainty in Robotics
Occupancy grids: a probabilistic framework for robot perception and navigation
Occupancy grids: a probabilistic framework for robot perception and navigation
Spatial learning for navigation in dynamic environments
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Real-time map building and navigation for autonomous robots inunknown environments
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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In this paper two computationally efficient methods for building a map of the occupancy of a space based on measurements from a ring of ultrasonic sensors are presented. The first is a method based on building a histogram of the occurrence of free and occupied space. The second is based on the calculation of the rate between occupied space measurements with respect to the total. The resulting occupancy maps have been compared with those obtained with other well-known methods, bothcoun t as well as Bayes-rule-based ones, in static environments. Free space, occupied space and unknown labels were also compared subsequent to the application of a simple segmentation algorithm. The results obtained gave rise to statistically significant differences between all the different types on comparing the resulting maps. In the case of comparing occupancy labels, no differences were found between the following pairs of methods: RATE and SUM (p - value = 0.157), ELFES and RATE (p - value = 0.600) and ELFES and SUM (p - value = 0.593).