Robust Autonomous Flight in Constrained and Visually Degraded Shipboard Environments

Abstract

This paper addresses the problem of autonomous navigation of a micro aerial vehicle (MAV) for inspection and damage assessment inside a constrained shipboard environment, which might be perilous or inaccessible for humans, especially in emergency scenarios. The environment is GPS-denied and visually degraded, containing narrow passageways, doorways, and small objects protruding from the wall. This causes existing two-dimensional LIDAR, vision, or mechanical bumper-based autonomous navigation solutions to fail. To realize autonomous navigation in such challenging environments, we first propose a robust state estimation method that fuses estimates from a real-time odometry estimation algorithm and a particle filtering localization algorithm with other sensor information in a two-layer fusion framework. Then, an online motion-planning algorithm that combines trajectory optimization with a receding horizon control framework is proposed for fast obstacle avoidance. All the computations are done in real time on the onboard computer. We validate the system by running experiments under different environmental conditions in both laboratory and practical shipboard environments. The field experiment results of over 10 runs show that our vehicle can robustly navigate 20-m-long and only 1-m-wide corridors and go through a very narrow doorway (66-cm width, only 4-cm clearance on each side) autonomously even when it is completely dark or full of light smoke. These experiments show that despite the challenges associated with flying robustly in challenging shipboard environments, it is possible to use a MAV to autonomously fly into a confined shipboard environment to rapidly gather situational information to guide firefighting and rescue efforts.