User-driven design of robot costume for child-robot interactions among children with cognitive impairment

The involvement of arts and psychology elements in robotics research for children with cognitive impairment is still limited. However, the combination of robots, arts, psychology and education in the development of robots could significantly contribute to the improvement of social interaction skills among children with cognitive impairment. In this article, we would like to share our work on building and innovating the costume of LUCA's robot, which incorporating the positive psychological perspectives and arts values for children with cognitive impairment. Our goals are (1) to educate arts students in secondary arts school on the importance of social robot appearance for children with cognitive impairment, and (2) to select the best costume for future child-robot interaction study with children with cognitive impairments

Toward a Multilevel Cognitive Probabilistic Representation of Space

This paper addresses the problem of perception and representation of space for a mobile agent. A probabilistic hierarchical framework is suggested as a solution to this problem. The method proposed is a combination of probabilistic belief with Object Graph Models(OGM). The world is viewed from a topological optic, in terms of objects and relationships between them. The hierarchical representation that we propose permits an efficient and reliable modeling of the information that the mobile agent would perceive from its environment. The integration of both navigational and interactional capabilities through efficient representation is also addressed. Experiments on a set of images taken from the real world that validate the approach are reported. This framework draws on the general understanding of human cognition and perception and contributes towards the overall efforts to build cognitive robot companions

Topological Global Localization and Mapping with Fingerprint and Uncertainty

Navigation in unknown or partially unknown environments remains one of the biggest challenges in today\'s mobile robotics. Environmental modeling, perception, localization and mapping are all needed for a successful approach. The contribution of this paper resides in the extension of the fingerprint concept (circular list of features around the robot) with uncertainty modeling, in order to improve localization and allow for automatic map building. The uncertainty is defined as the probability of a feature of being present in the environment when the robot perceives it. The whole approach is presented in details and viewed in a topological optic. Experimental results of the perception and localization capabilities with a mobile robot equipped with two 180° laser range finders and an omni-directional camera are reported

Bayesian Programming for Topological Global Localization with Fingerprints

This paper presents a localization algorithm for indoor environments. The environmental model is topological and the approach describes how a multimodal perception increases the reliability for the topological localization problem for mobile robots, by using the Bayesian Programming formalism. For the topological framework the fingerprint concept is used. This type of representation permits a reliable and distinctive environment modeling. Experimental results of a mobile robot equipped with a multi sensor system composed of two 180° laser range finders and an omni-directional camera are reported

Multi-resolution SLAM for Real World Navigation

In this paper a hierarchical multi-resolution approach allowing for high precision and distinctiveness is presented. The method combines topological and metric paradigm. The metric approach, based on the Kalman Filter, uses a new concept to avoid the problem of the drift in odometry. For the topological framework the fingerprint sequence approach is used. During the construction of the topological map, a communication between the two paradigms is established. The fingerprint used for topological navigation enables also the re-initialization of the metric localization. The experimentation section will validate the multi-resolution-representation maps approach and presents different steps of the method