No abstract

Abstract:The autonomy is the most crucial criteria in mobile robots. This operation aims to offer the ability of finding the position and build a map of the environment to the robot. Many methods have been proposed to solve this problem. In this study, an implementation of SLAM approach for unknown indoor environment exploring by mobile robot is proposed. In fact, the proposed approach touch on the unknown indoor environments exploring with static obstacles, based on robot mobile abilities (extereoceptive and proprioceptive sensors). In one hand, the measurements given by the proprioceptive sensor (odometry) are used for the auto localization system. In the other hand, the map building based on extereoceptive sensor scanning and robot position. Therefore, the approach maintains two maps: (1) (OM) map grid describe the occupancy of environment; (2) (TM) map grid memorizes the robot former positions. Furthermore, the use of the proposed maps afford an efficient description and exploitation of the environment resources over time. Finally, the results in simulation and real robots experiments using random exploration (for test), demonstrate the fusibility of the proposed approach.

No abstract

No abstract

No abstract

No abstract

Throughout this study, we employed unsupervised machine learning clustering algorithms, namely K-Means [1] and hierarchical agglomerative clustering (HAC) [2], to explore human locomotion and wayfinding using a VR Magic Carpet (VMC) [3], a table test version known as the Corsi Block Tapping task (CBT) [4]. This variation was carried out in the context of a virtual reality experimental setup. The participants were required to memorize a sequence of target positions projected on the rug and walk to each target figuring in the displayed sequence. the participant’s trajectory was collected and analyzed from a kinematic perspective. An earlier study [5] identified three different categories, but the classification remained ambiguous, implying that they include both kinds of individuals (normal and patients with cognitive spatial impairments). On this basis, we utilized K-Means and HAC to distinguish the navigation behavior of patients from normal individuals, emphasizing the most important discrepancies and then delving deeper to gain more insights.

No abstract