ANEP: Adaptive Newton Ego Planner for Multiwaypoint UAV Trajectory Optimization in Constrained Environments

Abstract

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 primarily rely 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 multipoint obstacle avoidance during flight. In particular, autonomous roof gutter cleaning requires robust multistage trajectory planning in a cluttered environment, where the state estimation and waypoint accuracy can be degraded by the noises and inaccuracies from sensing and Global Navigation Satellite System (GNSS). This article presents an Adaptive Newton Ego Planner (ANEP) framework for multiwaypoint trajectory optimization under constrained environments. The proposed ANEP integrates a novel adaptive Newton–Raphson-based optimizer (ANRBO) to enhance exploration and prevent premature convergence during online trajectory refinement. The optimizer leverages Newton–Raphson search rules (NRSRs) and a trap avoidance operator (TAO), while an online dynamic adaptation mechanism enables the efficient multipoint exploration and real-time trajectory optimization. In this article, we focus on trajectory planning and optimization. Perception, e.g., debris detection, and cleaning actuation were not evaluated in this simulation study. Experimental results demonstrate that the proposed trajectory planning method improves multistage flight missions by generating smoother trajectories, lower trajectory errors, and a higher mission completion rate. These results suggest that ANEP can serve as an effective trajectory planning component for unmanned aerial vehicle (UAV)-based roof gutter maintenance scenarios.