This paper proposes a hybridization of two well-known stereo-based obstacle detection techniques for allterrain environments. While one of the techniques is employed for the detection of large obstacles, the other is used for the detection of small ones. This combination of techniques opportunistically exploits their complementary properties to reduce computation and improve detection accuracy. Being particularly computation intensive and prone to generate a high false-positive rate in the face of noisy three-dimensional point clouds, the technique for small obstacle detection is further extended in two directions. The goal of the first extension is to reduce both problems by focusing the detection on those regions of the visual field that detach more from the background and, consequently, are more likely to contain an obstacle. This is attained by means of spatially varying the data density of the input images according to their visual saliency. The second extension refers to the use of a novel voting mechanism, which further improves robustness. Extensive experimental results confirm the ability of the proposed method to robustly detect obstacles up to a range of 20 m on uneven terrain. Moreover, the model runs at 5 Hz on 640 × 480 stereo images. C

Abstract-Modern manufacturing systems require a transformation from mass production towards mass customization. This results in a trend towards more agile production lines. It also demands a reduction of configuration times when building the production line as well as faster reconfiguration when adding new hardware and product variants to the manufacturing line. This paper introduces the concept of a device adapter that allows the device to be seamlessly plugged into the agile production systems. The device adapter wraps the device functionality and offers it as a service, hiding away the low-level process capability (skill) implementation and allowing to formally represent the production steps. Preliminary tests have been performed on an industrial demonstrator that simulates a real manufacturing process.

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This paper presents an aerial-ground field robotic team, designed to collect and transport soil and biota samples in estuarine mudflats. The robotic system has been devised so that its sampling and storage capabilities are suited for radionuclides and heavy metals environmental monitoring. Automating these time-consuming and physically demanding tasks is expected to positively impact both their scope and frequency. The success of an environmental monitoring study heavily depends on the statistical significance and accuracy of the sampling procedures, which most often require frequent human intervention. The bird's-eye view provided by the aerial vehicle aims at supporting remote mission specification and execution monitoring. This paper also proposes a preliminary experimental protocol tailored to exploit the capabilities o↵ered by the robotic system. Preliminary field trials in real estuarine mudflats show the ability of the robotic system to successfully extract and transport soil samples for o✏ine analysis.

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