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Unmanned aircraft systems (UAS) are being used more and more every day in almost any area to solve challenging real-life problems. Increased autonomy and advancements in low-cost high-computing technologies made these compact autonomous solutions accessible to any party with ease. However, this ease of use brings its own challenges that need to be addressed. In an autonomous flight scenario over a public space, an autonomous operation plan has to consider the public safety and regulations as well as the task specific objectives. In this work, we propose a generic utility function for the path planning of UAS operations that includes the benefits of accomplishing the goals as well as the safety risks incurred along the flight trajectories, with the purpose of making task-level decisions through the optimization of the carefully constructed utility function for a given scenario. As an optimizer, we benefited from a multi-tree variant of the optimal T-RRT * (Multi-T-RRT * path planning algorithm. To illustrate its operation, results of simulation of a UAS scenario are presented. 

This paper introduces a probabilistic risk assessment (PRA) framework for the path planning to quantify the risk of unmanned aircraft systems’ (UAS) operations to the ground over populated areas. The proposed framework is designed to be flexible enough to address multiple concerns and objectives by utilizing the probabilistic risk exposure map (PREM) of the area of operation and UAS failure mode analysis with corresponding impact probability distributions on the ground. PREM is defined to be the risk of exposure of people or property on the ground to the presence of UAS in the air as a function of position, and itis used to model the distribution of risk exposure over the map. In this study, PREM isconstructed for the impact related risk conditions where their distributions are modeled as a mixture of bivariate normal distributions over the discretized map of the area. Along with PREM, UAS failure modes with ground impact distributions are used in the derivation of the risk function to quantify the risk of being hit by the failing UAS platform for bystanders, properties and the traffic on the ground. Then, utilizing the derived risk function as a planning cost function, the path planner algorithm is used to plan a path that minimizes the risk according to the proposed risk assessment framework. As a pathplanner, optimal bidirectional rapidly-exploring random trees (RRT) is selected due to its fast convergence and optimality guarantee. Finally, the results of simulations for different scenarios are compared and discussed in detail. 

The range and endurance of an unmanned aerial system operating nominally in an outdoor environment depends upon the available power and environmental factors like the magnitude and direction of the prevailing wind. This paper focuses on the development of semi-analytical approaches to computing the range and endurance of battery-powered multi-copter unmanned aerial system under varying wind conditions. The analytically derived range is verified against a comprehensive unmanned aerial system simulation which includes experimentally validated elements such as the propulsion system and electric power consumption modules. It is shown that the analytical approach yields the range maps in close agreement with the simulation results.