Coverage path planning is the operation of finding a path that covers all the points of a specific area. Thanks to the recent advances of hardware technology, Unmanned Aerial Vehicles (UAVs) are starting to be used for photogrammetric sensing of large areas in several application domains, such as agriculture, rescuing, and surveillance. However, most of the research focused on finding the optimal path taking only geometrical constraints into account, without considering the peculiar features of the robot, like available energy, weight, maximum speed, sensor resolution, etc. This paper proposes an energy-aware path planning algorithm that minimizes energy consumption while satisfying a set of other requirements, such as coverage and resolution. The algorithm is based on an energy model derived from real measurements. Finally, the proposed approach is validated through a set of experiments.

Unmanned Aerial Vehicles (UAVs) are starting to be used for photogrammetric sensing of large areas in several application domains, such as agriculture, rescuing, and surveillance. In this context, the problem of finding a path that covers the entire area of interest is known as Coverage Path Planning (CPP). Although this problem has been addressed by several authors from a geometrical point of view, other issues such as energy, speed, acceleration, and image resolution are not often taken into account. To fill this gap, this paper first proposes an energy model derived from real measurements, and then uses this model to implement a coverage path planning algorithm for reducing energy consumption, as well as guaranteeing a desired image resolution. In addition, two safety mechanisms are presented: the first, executed off-line, checks whether the energy stored in the battery is sufficient to perform the planned path; the second, performed online, triggers a safe return-to-launch (RTL) operation when the actual available energy is equal to the energy required by the UAV to go back to the starting point

Background: In recurrent urinary tract infections (UTIs) usual prophylactic antibiotic regimes do not change the long-term risk of recurrence. Our aim was to evaluate the efficacy of D-mannose in the treatment and prophylaxis of recurrent UTIs. Methods: In this randomized cross-over trial female patients were eligible for the study if they had an acute symptomatic UTI and three or more recurrent UTIs during the preceding 12 months. Suitable patients were randomly assigned to antibiotic treatment with trimethoprim/sulfamethoxazole or to a regimen of oral D-mannose 1 g 3 times a day, every 8 hours for 2 weeks, and subsequently 1 g twice a day for 22 weeks. They received the other intervention in the second phase of the study, with no further antibiotic prophylaxis. The primary endpoint was evaluation of the elapsed time to recurrence; secondary endpoints were evaluation of bladder pain (VASp) and urinary urgency (VASu). Results: The results for quantitative variables were expressed as mean values and SD as they were all normally distributed (Shapiro-Wilk test). In total, 60 patients aged between 22 and 54 years (mean 42 years) were included. Mean time to UTI recurrence was 52.7 days with antibiotic treatment, and 200 days with oral D-mannose (p < 0.0001). Conclusions: Mean VASp, VASu score, and average numbers of 24-hour voidings decreased significantly. D-mannose appeared to be a safe and effective treatment for recurrent UTIs in adult women. A significant difference was observed in the proportion of women remaining infection free versus antibiotic treatment.

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