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.

Fast, autonomous flight in GPS‐denied and cluttered environments

“…Shen, Michael, and Kumar () were the first to demonstrate a full navigation system running onboard the robot without a known map of the environment. Since then, multiple groups have demonstrated similar capabilities (Fang et al., ; Nieuwenhuisen, Droeschel, Beul, & Behnke, ; Nuske et al., ; Schmid, Lutz, Tomić, Mair, & Hirschmüller, ; Valenti, Dryanovski, Jaramillo, Ström, & Xiao, ). While these systems are capable of operating in unknown environments, the navigation speeds are quite low (less than 3 m/s).…”

Autonomous aerial navigation using monocular visual‐inertial fusion

“…With a redundant sensor suite, Ref. mainly focuses on providing a safety path and presents convincing robust navigation experiments in an extreme environment. However, with the different objective, we contemplate about building an agile flight platform with minimum sensing.…”

“…9 In Ref. 10, the authors present a two-layer fusion framework with multiple sensors aiming to develop a robust autonomous navigation system in challenging environments such as a shipboard environment.…”

Flying on point clouds: Online trajectory generation and autonomous navigation for quadrotors in cluttered environments

“…There has been much research on developing autonomous quadrotor systems. Among these works, some focus on the perception functionality (Bry, Richter, Bachrach, & Roy, ; Faessler et al, ; S. Shen, Michael, & Kumar, ) and some present complete system results (Fang et al, ; Lin et al, ; Mohta et al, ). In this study, we propose a motion planning framework for the safe navigation of an autonomous quadrotor.…”