Exploring the use of robots for museum settings and for learning heritage languages and cultures at the Chinese Heritage Centre

In order to build a stable and reliable system for the Amazon Robotics Challenge we went through a detailed study of the performance and system requirements based on the rules and our past experience of the challenge. The challenge was to build a robot that integrates grasping, vision, motion planning, among others, to be able to pick items from a shelf to specific order boxes. This paper presents the development process including component selection, module designs, and deployment. The resulting robot system has dual 6 degrees of freedom industrial arms mounted on fixed bases, which in turn are mounted on a calibrated table. The robot works with a custom-designed top-open extendable shelf. The vision system uses multiple stereo cameras mounted on a fixed calibrated frame. Feature-based comparison and machine-learning based matching are used to identify and determine item pose. The gripper system uses suction cup and the grasping strategy is pick from the top. Error recovery strategies were also implemented to ensure robust performance. During the competition, the robot was able to pick all target items with the shortest amount of time.

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This paper presents the design and implementation of a multimodal person-following system for a mobile telepresence robot. A color histogram matching and position matching algorithm was developed for a person-recognition function using Kinect sensors. Robot motion was controlled by adjusting its velocity according to the humans position in relation to the robot. The robot was able to follow the targeted person in various person-following modes, such as the back-following mode, the side-by-side accompaniment mode as well as the front-guiding mode. An obstacle avoidance function was also implemented using the virtual potential field algorithm.