Thesis Defence: ANEP

Tiancheng Liu, supervised by Dr. Zheng Liu, will defend their thesis titled “ANEP: Adaptive Newton Ego Planner for UAV Rooftop Gutter Cleaning” in partial fulfillment of the requirements for the degree of Master of Applied Science in Electrical Engineering.

An abstract for Tiancheng Liu’s thesis is included below.

Defences are open to all members of the campus community as well as the general public. Registration is not required for in-person defences.

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 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). This thesis presents an Adaptive Newton Ego Planner (ANEP) framework for multi-waypoint 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 (NRSR) and a Trap Avoidance Operator (TAO), while an online dynamic adaptation mechanism enables efficient multi-point exploration and real-time trajectory optimization. In this thesis, 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 multi-stage 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 UAV-based roof gutter maintenance scenarios.