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In the Department of Defense, unmanned aerial vehicle (UAV) mission planning is typically in the form of a set of pre-defined waypoints and tasks, and results in optimized plans being implemented prior to the beginning of the mission. These include the order of waypoints, assignment of tasks, and assignment of trajectories. One emerging area that has been recently identified in the literature involves frameworks, simulations, and supporting algorithms for dynamic mission planning, which entail re-planning mid-mission based on new information. These frameworks require algorithmic support for flight path and flight time approximations, which can be computationally complex in nature. This article seeks to identify the leading academic algorithms that could support dynamic mission planning and recommendations for future research for how they could be adopted and used in current applications. A survey of emerging UAV mission planning algorithms and academic UAV flight path algorithms is presented, beginning with a taxonomy of the problem space. Next, areas of future research related to current applications are presented.

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This paper presents tests conducted on routes determined from a Dijkstra-based shortest path problem and a Variance-Constrained Shortest Path problem under varying conditions of traffic and weather in a simulated 'smart environment'. Utilizing envisioned future advanced transportation systems' real-time information on traffic parameters allows data fusion techniques to provide situation awareness to its users. Taking advantage of this real-time data, the routing methodologies and data capture techniques studied in this paper provides Emergency Medical Services with better routes when responding to a vehicular crash. Comparing the performance of both routing methodologies in terms of both their ability to provide better routes as well as computation times demonstrates two alternatives for aiding in future emergency response.