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This article investigates the utility and extension of well-established collision risk modeling approaches for lateral separation standard development for unmanned traffic management and network design. The applicability of standard assumptions and simplifications made in the manned environment to a scaled-down unmanned aerial systems (UAS) environment is first investigated. The results are used to derive an iso-risk surface that shows the tradeoff between separation distance and navigation performance for a given risk level or target level of safety. The model is then extended to consider collision risk from one to n $n$ tracks, and finally to determine the total collision risk and the related iso-risk surfaces for systems of n $n$ parallel tracks with additions to account for the effects of more than two parallel tracks. The extended model is applied to a case study where airspace design for an urban area is conducted via maximization of the number of same-direction parallel tracks while meeting the target level of safety. The results suggest that lateral separation distances less than 100 m are achievable for small UAS, and that the separation standards are mostly affected by the proportion of poorly navigating aircraft., (© 2021 Society for Risk Analysis.)

PURPOSE  : Navigation in visually complex endoscopic environments requires an accurate and robust localisation system. This paper presents the single image deep learning based camera localisation method for orthopedic surgery. METHODS  : The approach combines image information, deep learning techniques and bone-tracking data to estimate camera poses relative to the bone-markers. We have collected one arthroscopic video sequence for four knee flexion angles, per synthetic phantom knee model and a cadaveric knee-joint. RESULTS  : Experimental results are shown for both a synthetic knee model and a cadaveric knee-joint with mean localisation errors of 9.66mm/0.85[Formula: see text] and 9.94mm/1.13[Formula: see text] achieved respectively. We have found no correlation between localisation errors achieved on synthetic and cadaveric images, and hence we predict that arthroscopic image artifacts play a minor role in camera pose estimation compared to constraints introduced by the presented setup. We have discovered that the images acquired for 90°and 0°knee flexion angles are respectively most and least informative for visual localisation. CONCLUSION  : The performed study shows deep learning performs well in visually challenging, feature-poor, knee arthroscopy environments, which suggests such techniques can bring further improvements to localisation in Minimally Invasive Surgery., (© 2021. CARS.)