ANEP : Adaptive newton ego planner for unmanned aerial vehicle rooftop gutter cleaning

Roof gutters are highly susceptible to the accumulation of debris, such as fallen leaves, which can block drain outlets and pose a significant threat to the structural integrity of a building. Traditional high-altitude cleaning methods are based primarily on manual operations with ropes, ladders, and extended tools. These methods are not only inherently dangerous, but also inefficient, making them inadequate for building maintenance. The use of drones offers a promising solution for high-altitude cleaning. However, drones still face significant technical challenges in ensuring precise arrival at target points after detecting debris while simultaneously navigating multi-point obstacle avoidance during flight. In particular, autonomous roof gutter cleaning requires robust multi-stage trajectory planning in a cluttered environment where state estimation and waypoint accuracy can be degraded by the noises and inaccuracies from sensing and Global Navigation Satellite System (GNSS). To address these challenges, this thesis presents the Adaptive Newton Ego Planner (ANEP), the first trajectory planning framework specifically tailored for autonomous Unmanned Aerial Vehicle (UAV) roof gutter cleaning. The proposed ANEP integrates a novel Adaptive Newton-Raphson based optimizer (ANRBO), which replaces the conventional gradient-based optimizer in existing local planners to enhance exploration and prevent premature convergence during online trajectory refinement. The optimizer leverages Newton-Raphson Search Rules (NRSR) and a Trap Avoidance Operator (TAO), and further proposes an online dynamic adaptation mechanism that enables efficient multi-point exploration and real-time trajectory optimization under noisy localization. Experimental results demonstrate that the proposed method generates smoother trajectories, lower trajectory errors, and a higher mission completion rate across a range of GNSS noise levels, indicating strong robustness in degraded localization conditions. These results suggest that ANEP can serve as an effective and noise-robust trajectory planning component for UAV-based roof gutter maintenance scenarios.