GPU-Based Path Optimization Algorithm in High-Resolution Cost Map with Kinodynamic Constraints : Using Non-Reversible Parallel Tempering

Abstract: This thesis introduces a GPU-accelerated algorithm for path planning under kinodynamic constraints, focusing on navigation of flying vehicles within a high-resolution cost map. The algorithm operates by creating dynamically feasible initial paths, and a non-reversible parallel tempering Markov chain Monte Carlo scheme to optimize the paths while adhering to the nonholonomic kinodynamical constraints. The algorithm efficiently generates high quality dynamically feasible paths. An analysis demonstrates the algorithm's robustness, stability and scalability. The approach used for this algorithm is versatile, allowing for straightforward adaptation to different dynamic conditions and cost maps. The algorithm's applicability also extends to various path planning problems, signifying the potential advantages of GPU-accelerated algorithms in the domain of path planning.