@incollection{YeapWongSchmidt2008, author = {Yeap, W.K. and Wong, C.K. and Schmidt, Jochen}, title = {Using a Mobile Robot to Test a Theory of Cognitive Mapping.}, series = {Robotics and Cognitive Approaches to Spatial Mapping, Springer Tracts in Advanced Robotics}, booktitle = {Robotics and Cognitive Approaches to Spatial Mapping, Springer Tracts in Advanced Robotics}, publisher = {Springer}, address = {Berlin}, publisher = {Technische Hochschule Rosenheim}, year = {2008}, abstract = {This paper describes using a mobile robot, equipped with some sonar sensors and an odometer, to test navigation through the use of a cognitive map. The robot explores an office environment, computes a cognitive map, which is a network of ASRs [36, 35], and attempts to find its way home. Ten trials were conducted and the robot found its way home each time. From four random positions in two trials, the robot estimated the home position relative to its current position reasonably accurately. Our robot does not solve the simultaneous localization and mapping problem and the map computed is fuzzy and inaccurate with much of the details missing. In each homeward journey, it computes a new cognitive map of the same part of the environment, as seen from the perspective of the homeward journey. We show how the robot uses distance information from both maps to find its way home.}, language = {en} } @inproceedings{YeapWongSchmidt2006, author = {Yeap, W.K. and Wong, C.K. and Schmidt, Jochen}, title = {Initial Experiments with a Mobile Robot on Cognitive Mapping}, series = {International Symposium on Practical Cognitive Agents and Robots (PCAR 2006), pages 221-230, Perth, Australia}, booktitle = {International Symposium on Practical Cognitive Agents and Robots (PCAR 2006), pages 221-230, Perth, Australia}, year = {2006}, abstract = {This paper shows how a mobile robot equipped with sonar sensors and an odometer is used to test ideas about cognitive mapping. The robot first explores an office environment and computes a "cognitive map" which is a network of ASRs [1]. The robot generates two networks, one for the outward journey and the other for the journey home. It is shown that both networks are different. The two networks, however, are not merged to form a single network. Instead, the robot attempts to use distance information implicit in the shape of each ASR to find its way home. At random positions in the homeward journey, the robot calculates its orientation towards home. The robot's performances for both problems are evaluated and found to be surprisingly accurate.}, language = {en} } @inproceedings{WongYeapSchmidt2007, author = {Wong, C.K. and Yeap, W.K. and Schmidt, Jochen}, title = {Using a Mobile Robot for Cognitive Mapping}, series = {International Joint Conference on Artificial Intelligence (IJCAI), pages 2243-2248, Hyderabad, India, 2007}, booktitle = {International Joint Conference on Artificial Intelligence (IJCAI), pages 2243-2248, Hyderabad, India, 2007}, year = {2007}, abstract = {When animals (including humans) first explore a new environment, what they remember is fragmentary knowledge about the places visited. Yet, they have to use such fragmentary knowledge to find their way home. Humans naturally use more powerful heuristics while lower animals have shown to developa varietyof methodsthat tend to utilize two key pieces of information,namely distance and orientation information. Their methods differ depending on how they sense their environment. Could a mobile robot be used to investigate the nature of such a process, commonly referred to in the psychological literature as cognitive mapping? What might be computed in the initial explorations and how is the resulting "cognitive map" be used to return home? In this paper, we presented a novel approach using a mobile robot to do cognitive mapping. Our robot computes a "cognitive map" and uses distance and orientation information to find its way home. The process developed provides interesting insights into the nature of cognitive mapping and encourages us to use a mobile robot to do cognitive mapping in the future, as opposed to its popular use in robot mapping.}, language = {en} } @article{SchmidtWongYeap2007, author = {Schmidt, Jochen and Wong, C.K. and Yeap, W.K.}, title = {Localisation and Mapping with a Mobile Robot Using Sparse Range Data}, series = {Autonomous Robots and Agents, Studies in Computational Intelligence (SCI),}, journal = {Autonomous Robots and Agents, Studies in Computational Intelligence (SCI),}, publisher = {Springer}, year = {2007}, abstract = {We present an approach for indoor mapping and localisation using sparse range data, acquired by a mobile robot equipped with sonar sensors. The chapter consists of two main parts. First, a split and merge based method for dividing a given metric map into distinct regions is presented, thus creating a topological map in a metric framework. Spatial information extracted from this map is then used for self-localisation on the return home journey. The robot computes local confidence maps for two simple localisation strategies based on distance and relative orientation of regions. These local maps are then fused to produce overall confidence maps.}, language = {de} } @inproceedings{SchmidtWongYeap2007, author = {Schmidt, Jochen and Wong, C.K. and Yeap, W.K.}, title = {Spatial Information Extraction for Cognitive Mapping with a Mobile Robot.}, series = {Conference on Spatial Information Theory: COSIT'07, Melbourne, Australia. Volume 4736 of Lecture Notes in Computer Science}, booktitle = {Conference on Spatial Information Theory: COSIT'07, Melbourne, Australia. Volume 4736 of Lecture Notes in Computer Science}, year = {2007}, abstract = {When animals (including humans) first explore a new environment, what they remember is fragmentary knowledge about the places visited. Yet, they have to use such fragmentary knowledge to find their way home. Humans naturally use more powerful heuristics while lower animals have shown to develop a variety of methods that tend to utilize two key pieces of information, namely distance and orientation information. Their methods differ depending on how they sense their environment. Could a mobile robot be used to investigate the nature of such a process, commonly referred to in the psychological literature as cognitive mapping? What might be computed in the initial explorations and how is the resulting "cognitive map" be used for localization? In this paper, we present an approach using a mobile robot to generate a "cognitive map", the main focus being on experiments conducted in large spaces that the robot cannot apprehend at once due to the very limited range of its sensors. The robot computes a "cognitive map" and uses distance and orientation information for localization.}, language = {en} }