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Abstract-We present a novel algorithm for path planning that avoids occlusions of a visual target for an "eye-in-hand" sensor on an articulated robot arm. We compute paths using a probabilistic roadmap to avoid collisions between the robot and obstacles, while penalizing trajectories that do not maintain line-of-sight. The system determines the space from which line-of-sight is unimpeded to the target (the visible region) using the method described in [11]. We assign penalties to trajectories within the roadmap proportional to the distance the camera travels while outside the visible region. Using Dijkstra's algorithm, we compute paths of minimal occlusion (maximal visibility) through the roadmap. In our experiments, we compare a shortest-distance path to the minimal-occlusion path and discuss the impact of the improved visibility.

This paper describes an integrated robot system, known as Curious George, that has demonstrated state-of-the-art capabilities to recognize objects in the real world. We describe the capabilities of this system, including: the ability to access web-based training data automatically and in near real-time; the ability to model the visual appearance and 3D shape of a wide variety of object categories; navigation abilities such as exploration, mapping and path following; the ability to decompose the environment based on 3D structure, allowing for attention to be focused on regions of interest; the ability to capture high-quality images of objects in the environment; and finally, the ability to correctly label those objects with high accuracy. The competence of the combined system has been validated by entry into an international competition where Curious George has been among the top performing systems each year. We discuss the implications of such successful object recognition for society, and provide several avenues for potential improvement.