AJ
A. Jain
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Informative path planning (IPP) tasks UAV with locating and accurately measuring high concentrations of specific phenomena in initially unexplored, budget constrained search space. Adaptive-IPP closes the loop between sensing and planning by replanning sensor trajectories as a probabilistic belief about the environment evolves but even state-of-the-art planners face two challenges while replanning their trajectory online: 1.) Replanning during deployment incurs substantial online computation and thus time costs during deployment, which is problematic in a time-budgeted mission and 2.) the resulting path-optimization landscape is non-convex, producing a fundamentally multimodal set of valid solutions. Behaviorally cloning such an expert with a deterministic, MSE-trained policy mode-averages these equally valid trajectories, producing plans that correspond to none of them and fail to learn the underlying monitoring objective in unseen environments.This thesis presents a path-planning framework that instead uses a conditional denoising diffusion model to generate valid 3D waypoint sequences for continuous environments. A novel CMA-ES-based data collection procedure exposes multiple deployable expert trajectories per belief state, giving the diffusion model a genuinely multimodal demonstration set to learn from. We tested our diffusion based model in simulated missions on synthetic Gaussian random fields and real-world agriculture maps based on the National Agricultural Imagery Programme (NAIP) and found that diffusion reduces time-integrated map uncertainty by roughly 34\% relative to the online replanning expert that it was trained on, while replanning approximately 11× faster by appropriately learning the goal of online replanning through the multimodal dataset, and consistently outperforms a deterministic learning baseline trained on the same demonstrations. These results suggest that diffusion-based behavioral cloning preserves valid planning modes and enables real-time, non-myopic informative path planning.
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Informative path planning (IPP) tasks UAV with locating and accurately measuring high concentrations of specific phenomena in initially unexplored, budget constrained search space. Adaptive-IPP closes the loop between sensing and planning by replanning sensor trajectories as a probabilistic belief about the environment evolves but even state-of-the-art planners face two challenges while replanning their trajectory online: 1.) Replanning during deployment incurs substantial online computation and thus time costs during deployment, which is problematic in a time-budgeted mission and 2.) the resulting path-optimization landscape is non-convex, producing a fundamentally multimodal set of valid solutions. Behaviorally cloning such an expert with a deterministic, MSE-trained policy mode-averages these equally valid trajectories, producing plans that correspond to none of them and fail to learn the underlying monitoring objective in unseen environments.This thesis presents a path-planning framework that instead uses a conditional denoising diffusion model to generate valid 3D waypoint sequences for continuous environments. A novel CMA-ES-based data collection procedure exposes multiple deployable expert trajectories per belief state, giving the diffusion model a genuinely multimodal demonstration set to learn from. We tested our diffusion based model in simulated missions on synthetic Gaussian random fields and real-world agriculture maps based on the National Agricultural Imagery Programme (NAIP) and found that diffusion reduces time-integrated map uncertainty by roughly 34\% relative to the online replanning expert that it was trained on, while replanning approximately 11× faster by appropriately learning the goal of online replanning through the multimodal dataset, and consistently outperforms a deterministic learning baseline trained on the same demonstrations. These results suggest that diffusion-based behavioral cloning preserves valid planning modes and enables real-time, non-myopic informative path planning.