Path Planning for an Unmanned Aerial Vehicle in Enemy Territory
William Payne Reed, Nathan Lawyer, Nick Workman, Elijah Wince, Ben Figueras, Mrinal Kumar · 2023
When foot soldiers or aircraft cross over into enemy territory, risk is always involved. Enemy radar and anti-air weaponry can cause significant problems such as risk to human life and strategic disadvantage. Reducing these problems by lessening risk and improving mapping capabilities is a huge advantage. A drone capable of path planning and navigating through multiple resource constraints is the core of this proposal. The main objective of this project is to improve a provided path planning algorithm and improve it to account for any environment. The goals of the project are to be able to randomly generate static obstacles in the path planning code, and account for multiple resource constraints. Additionally, the improved path planning code will be able to reach its goal pose 10% faster than the original code using heat flux as a resource constraint. A secondary future objective of this project is to accurately model the resource constraints based off a radar. Currently the path planning code uses heat flux as its resource constraint. As a result, the path planning code will be more responsive to any environment that you provide in the code and will accurately simulate a reconnaissance-style mission. The drone constructed for this project will be capable of complete autonomy. Using code made in MATLAB, the drone will find the most optimal path given a prior set of obstacles labeled out on the proposed map. The most optimal path is the path that takes the least amount of time to traverse without exceeding the loading limit. This will provide the best possible intel for foot soldiers moving through enemy territory. Computational and experimental tests will be conducted to assess the hypothesis. Static obstacles and resource constraints will be changed and implemented in the path-planning code to test the loading limit, path taken, and test time. Experimentally the provided code will be compared with the new optimized code to test the drone performance and draw conclusions about the performance of the new-optimized code. The data collected will be essential for assessing the project hypothesis as well as verifying that the overall objectives of the project have been met.