Original title: Obstacle-Aware Autonomous Coverage and Navigation for Outdoor Robots
Leonardo Gargani, Matteo Frosi, Matteo Matteucci · September 2026
Autonomous robots struggle to cover large outdoor areas. Over long trips, they lose track of their position, hit obstacles, struggle with sharp turns, and run out of battery. Researchers designed a complete software system to fix this. It tracks location and heading accurately by fusing dual-antenna satellite data with a sensor-combining filter. The team also improved coverage planning with three updates tailored to robot steering. Finally, a mission manager handles multi-goal navigation, fixes errors, avoids costly paths, and sends the robot to recharge. In simulations and real tests across diverse outdoor areas, the system worked reliably. It successfully swept 93.1% to 96.1% of the planned coverage area while handling different layouts and obstacles.
A robot is tasked with mowing a large field with scattered rocks. It must cover the entire area without colliding with rocks or running out of power. In this paper, the system plans efficient routes, adjusts its path for obstacles, and knows exactly where it is using advanced GPS, so it can finish the job and recharge autonomously.
This enables persistent autonomy for outdoor robots in tasks like agriculture or inspection, reducing the need for human intervention in long-duration missions.
The abstract doesn't specify limitations, but notes validation in simulation and real-world trials across multiple outdoor areas with varying geometries and obstacle densities.
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Obstacle-Aware Autonomous Coverage and Navigation for Outdoor Robots — Leonardo Gargani, Matteo Frosi, Matteo Matteucci
Abstract licence: CC0
Explanation generated by Synthesis from the abstract — it is not the original paper. This is a preprint, so it hasn't necessarily been peer reviewed.