ETRI Knowledge Sharing Platform : Utilization of Mobile Robot at Interaction Systems for At-Home Workouts: Provision of Autonomous Mobility in Indoor Environments

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Conference Paper Utilization of Mobile Robot at Interaction Systems for At-Home Workouts: Provision of Autonomous Mobility in Indoor Environments

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Citation: International Conference on Control, Automation and Systems (ICCAS) 2019, pp.1160-1163

Project Code: 19HS4900, Development of Intelligent Interaction Technology based on Recognition of User's State and Intention for Digital Life, Park Ji Young

Abstract: Many interaction systems for at-home workouts have had much difficulty in mobility due to the fixed location of a vision sensor. In this paper, we present the utilization of a mobile robot that navigates autonomously to improve an interaction system's mobility. The mobile robot is implemented in an open-source robot operating system called ROS to take advantage of open-source packages associated with autonomous navigation. To perform autonomous navigation and provide mobility to the system, it comprises six components: mapping, localization, occupancy grid map, global path planning, local path planning, and ROS communication. The mapping component builds a global map by simultaneous localization and mapping. The localization component estimates the mobile robot's pose within the global map by the adaptive Monte Carlo localization approach. The occupancy grid map component builds a local map for nearby surroundings including dynamic objects. The global path planning component optimizes a route to reach a given target pose. The local path planning component generates a trajectory to reach a local goal while conducting collision avoidance and then produces control commands to follow the trajectory. Last, the ROS communication component connects the mobile robot with the system. To verify the feasibility of the system's mobility, we have tested autonomous navigation capability for the mobile robot at the laboratory level in indoor environments.

KSP Keywords: Adaptive Monte Carlo, Indoor Environment, Interaction system, Mobile robots, Monte Carlo Localization, Occupancy grid map, Open source, Robot operating system(ROS), Vision sensor, autonomous navigation, collision avoidance