Honors College Thesis

 

Embedded Reachability for Autonomous Racecar Public Deposited

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https://ir.library.oregonstate.edu/concern/honors_college_theses/4f16c9673

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  • Reachability analysis enables the safety assurance of control systems despite uncertain initial conditions and control inputs, and can be an important component to run on-board an autonomous system. This thesis explores the characteristics of reachability analysis with different algorithmic configurations and runtime parameters running onboard the F1Tenth 1/10th-scale autonomous racing platform. The results demonstrate that it is possible to obtain accurate, real-time reachability computations using only simple kinematics. Parallelizing reachability algorithms on a GPU without algorithmic changes is feasible and reduces runtime. This is all achieved while running the algorithm alongside a fully autonomous racing stack. This implementation uses simple linearized kinematics, yet it is accurate; runs in Python, yet is real-time; shares the hardware with a full autonomous racing stack; and is parallelized to the GPU without algorithmic changes. These features lower the barrier to running reachability significantly. In testing, accurate 10-step reachability as quickly as 150 milliseconds is achieved. It was found that porting portions of reachability onto the GPU can improve the runtime for 10-step reachability by 62% over a CPU-only implementation.
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