TY - CHAP A1 - Schmidt, Jochen A1 - Wong, C.K. A1 - Yeap, W.K. T1 - Mapping and Localisation with Sparse Range Data T2 - International Conference on Autonomous Robots and Agents (ICARA), pages 497-502, Palmerston North, New Zealand N2 - We present an approach for indoor mapping and localization with a mobile robot using sparse range data, without the need for solving the SLAM problem. The paper 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-localization. The robot computes local confidence maps for two simple localization strategies based on distance and relative orientation of regions. The local confidence maps are then fused using an approach adapted from computer vision to produce overall confidence maps. Experiments on data acquired by mobile robots equipped with sonar sensors are presented. KW - Mobile Robots KW - Mapping KW - Localization Y1 - 2018 ER - TY - CHAP A1 - Schmidt, Jochen A1 - Wong, C.K. A1 - Yeap, W.K. T1 - A Split & Merge Approach to Metric-Topological Map-Building T2 - International Conference on Pattern Recognition (ICPR), volume 3, pages 1069-1072, Hong Kong N2 - We present a novel split and merge based method for dividing a given metric map into distinct regions, thus effectively creating a topological map on top of a metric one. The initial metric map is obtained from range data that are converted to a geometric map consisting of linear approximations of the indoor environment. The splitting is done using an objective function that computes the quality of a region, based on criteria such as the average region width (to distinguish big rooms from corridors) and overall direction (which accounts for sharp bends). A regularization term is used in order to avoid the formation of very small regions, which may originate from missing or unreliable sensor data. Experiments based on data acquired by a mobile robot equipped with sonar sensors are presented, which demonstrate the capabilities of the proposed method. KW - Pattern Recognition KW - Metric-Topological Map-Building Y1 - 2018 ER - TY - CHAP A1 - Wong, C.K. A1 - Yeap, W.K. A1 - Schmidt, Jochen T1 - Computing a Network of ASRs Using a Mobile Robot Equipped with Sonar Sensors T2 - International Conference on Robotics, Automation, and Mechatronics (RAM), pages 57-62, Bangkok, Thailand N2 - This paper presents a novel algorithm for computing absolute space representations (ASRs) in Yeap, W.K. and Jefferies, M. (1988) for mobile robots equipped with sonar sensors and an odometer. The robot is allowed to wander freely (i.e. without following any fixed path) along the corridors in an office environment from a given start point to an end point. It then wanders from the end point back to the start point. The resulting ASRs computed in both directions are shown KW - Robotics KW - Automation and Mechatronics Y1 - 2006 ER - TY - CHAP A1 - Yeap, W.K. A1 - Wong, C.K. A1 - Schmidt, Jochen T1 - Initial Experiments with a Mobile Robot on Cognitive Mapping T2 - International Symposium on Practical Cognitive Agents and Robots (PCAR 2006), pages 221-230, Perth, Australia N2 - 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. KW - Mobile Robot KW - Cognitive Mapping Y1 - 2006 ER - TY - CHAP A1 - Yeap, W.K. A1 - Wong, C.K. A1 - Schmidt, Jochen T1 - Using a Mobile Robot to Test a Theory of Cognitive Mapping. T2 - Robotics and Cognitive Approaches to Spatial Mapping, Springer Tracts in Advanced Robotics N2 - 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. KW - Cognitive Mapping KW - Mobile Robot KW - Simultaneous Localization  Y1 - 2008 PB - Springer CY - Berlin ER - TY - CHAP A1 - Schmidt, Jochen A1 - Wong, C.K. A1 - Yeap, W.K. T1 - Spatial Information Extraction for Cognitive Mapping with a Mobile Robot. T2 - Conference on Spatial Information Theory: COSIT'07, Melbourne, Australia. Volume 4736 of Lecture Notes in Computer Science N2 - 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. KW - Mobile Robot KW - Sonar Sensor KW - Robotic Research Y1 - 2018 ER - TY - JOUR A1 - Schmidt, Jochen A1 - Wong, C.K. A1 - Yeap, W.K. T1 - Localisation and Mapping with a Mobile Robot Using Sparse Range Data JF - Autonomous Robots and Agents, Studies in Computational Intelligence (SCI), N2 - 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. KW - Mobile Robot KW - Sonar Sensor KW - Sensor Data Fusion KW - Robotic Research Y1 - 2007 PB - Springer ER - TY - CHAP A1 - Wong, C.K. A1 - Yeap, W.K. A1 - Schmidt, Jochen T1 - Using a Mobile Robot for Cognitive Mapping T2 - International Joint Conference on Artificial Intelligence (IJCAI), pages 2243-2248, Hyderabad, India, 2007 N2 - 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. KW - Cognitive Mapping KW - Sonar Sensor KW - Robotic Research Y1 - 2007 ER - TY - CHAP A1 - Wong, C.K. A1 - Yeap, W.K. A1 - Schmidt, Jochen T1 - Our Next Generation of Robotics Researchers? Teaching Robotics at Primary School Level. T2 - Readings in Technology and Education: Proceedings of International Conference on Information Communication Technologies in Education N2 - In this paper, we present our experience in designing and teaching of our first robotics course for students at primary school level. The course was carried out over a comparatively short period of time, namely 6 weeks, 2 hours per week. In contrast to many other projects, we use robots that researchers used to conduct their research and discuss problems faced by these researchers. Thus, this is not a behavioural study but a hands-on learning experience for the students. The aim is to highlight the development of autonomous robots and artificial intelligence as well as to promote science and robotics in schools. KW - Robotic Research KW - Teaching Robotics KW - Software Development Y1 - 2009 ER -