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The main objective of an unmanned aerial vehicle (UAV) path planning is to generate a flight path that links a start point to an endpoint in an indoor space avoiding obstacles.  Path planning is essential for many real-life applications such as an autonomous car, surveillance mission, farming robots, unmanned aerial vehicles package delivery, space exploration, and many others. To create an optimal path, we need to adopt a specific criterion to minimize the distance the UAV must travel such as the Euclidean distance. In this paper, we provide our initial idea of creating an optimal path for indoor UAV using both A* and the Late Acceptance Hill Climbing (LAHC) algorithms. We are adopting an indoor search environment with various complexity and utilize the Probabilistic Roadmap algorithm (PRM) as a search space for both algorithms. The basic idea following PRM is to generate random sample points in the space and search these points for an optimal path. The developed results show that the LAHC algorithm outperforms the A* algorithm. 

This paper describes the use of a novel research platform called SenseMaker® to collect and analyze real-time data in the form of participants’ qualitative accounts of COVID-19 along with online learning experiences and participants’ own quantitative assessments of those experiences. Participants were faculty, students, and staff in the College of Engineering at the University of Georgia during Spring 2020. Results from two waves of data collection informed real-time recommendations to College faculty and administration to address COVID-19-related challenges. Results also facilitated faculty development programming to build instructor communities of learning and support in response to the University’s transition to online learning.