MOAR Planner: Multi-Objective and Adaptive Risk-Aware Path Planning for Infrastructure Inspection with a UAV
2026-06-29 • Robotics
Robotics
AI summaryⓘ
The authors developed a new path planner called MOAR to help drones fly safely and efficiently during inspections, even when conditions like weather or battery life change. Their system can quickly adjust the drone’s path in real time by using special risk calculations and a model that plans speed and movements carefully. They tested their method through simulations and real flights, finding it performs well compared to other popular planners. This work helps drones navigate more reliably in tricky situations.
UAV navigationpath plannerrisk-aware cost functiontrajectory adaptationdynamic risk factorsbattery autonomyreal-time planninggraph searchspeed planningdrone inspection
Authors
Louis Petit, Alexis Lussier Desbiens
Abstract
The problem of autonomous navigation for UAV inspection remains challenging as it requires effectively navigating in close proximity to obstacles, while accounting for dynamic risk factors such as weather conditions, communication reliability, and battery autonomy. This paper introduces the MOAR path planner which addresses the complexities of evolving risks during missions. It offers real-time trajectory adaptation while concurrently optimizing safety, time, and energy. The planner employs a risk-aware cost function that integrates pre-computed cost maps, the new concepts of damage and insertion costs, and an adaptive speed planning framework. With that, the optimal path is searched in a graph using a discrete representation of the state and action spaces. The method is evaluated through simulations and real-world flight tests. The results show the capability to generate real-time trajectories spanning a broad range of evaluation metrics: around 90% of the range occupied by popular algorithms. The proposed framework contributes by enabling UAVs to navigate more autonomously and reliably in critical missions.