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Many robots are limited in their operating capabilities, both computational and energy-wise. A strong desire exists to keep computation cost and energy consumption to a minimum when executing tasks like object recognition with a mobile robot. Adaptive action selection is a paradigm, offering great flexibility in trading off the cost of acquiring information against making robust and reliable inference under uncertainty. In this paper, we study active multi-view object recognition and describe an information-theoretic framework that combines and unifies two common techniques: online feature selection for reducing computational costs and view planning for resolving ambiguities and occlusions. Our algorithm adaptively chooses between the two strategies of either selecting only the features that are most informative to the recognition, or moving to a new viewpoint that optimally reduces the expected uncertainty on the identity of the object. This two step process allows us to keep overall computation cost minimal but simultaneously increase recognition accuracy. Extensive empirical studies on a large RGB-D dataset, and with two different feature sets, have validated the effectiveness of the proposed framework. Our experiments show that dynamic feature selection alone reduces the computation time at runtime 2.5–6 times and, when combining it with viewpoint selection, we significantly increase the recognition accuracy on average by 8%–18% absolute compared to systems that do not use these two strategies. By establishing a link between active object recognition and change detection, we were further able to use our framework for the follow-up task of actively detecting object change. Furthermore, we have successfully demonstrated the framework’s applicability to a low-powered quadcopter platform with limited operating time. [ABSTRACT FROM AUTHOR]

In this article we present a novel display that is created using a group of mobile robots. In contrast to traditional displays that are based on a fixed grid of pixels, such as a screen or a projection, this work describes a display in which each pixel is a mobile robot of controllable color. Pixels become mobile entities, and their positioning and motion are used to produce a novel experience. The system input is a single image or an animation created by an artist. The first stage is to generate physical goal configurations and robot colors to optimally represent the input imagery with the available number of robots. The run-time system includes goal assignment, path planning and local reciprocal collision avoidance, to guarantee smooth, fast and oscillation-free motion between images. The algorithms scale to very large robot swarms and extend to a wide range of robot kinematics. Experimental evaluation is done for two different physical swarms of size 14 and 50 differentially driven robots, and for simulations with 1,000 robot pixels. [ABSTRACT FROM PUBLISHER]