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.

Mapping a river's geometry provides valuable information to help understand the topology and health of an environment and deduce other attributes such as which types of surface vessels could traverse the river. While many rivers can be mapped from satellite imagery, smaller rivers that pass through dense vegetation are occluded. We develop a micro air vehicle (MAV) that operates beneath the tree line, detects and maps the river, and plans paths around three-dimensional (3D) obstacles (such as overhanging tree branches) to navigate rivers purely with onboard sensing, with no GPS and no prior map. We present the two enabling algorithms for exploration and for 3D motion planning. We extract high-level goal-points using a novel exploration algorithm that uses multiple layers of information to maximize the length of the river that is explored during a mission. We also present an efficient modification to the SPARTAN (Sparse Tangential Network) algorithm called SPARTANlite, which exploits geodesic properties on smooth manifolds of a tangential surface around obstacles to plan rapidly through free space. Using limited onboard resources, the exploration and planning algorithms together compute trajectories through complex unstructured and unknown terrain, a capability rarely demonstrated by flying vehicles operating over rivers or over ground. We evaluate our approach against commonly employed algorithms and compare guidance decisions made by our system to those made by a human piloting a boat carrying our system over multiple kilometers. We also present fully autonomous flights on riverine environments generating 3D maps over several hundred-meter stretches of tight winding rivers. C 2015 Wiley Periodicals, Inc.

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Abstract-Shipdeck landing is one of the most challenging tasks for a rotorcraft. Current autonomous rotorcraft use shipdeck mounted transponders to measure the relative pose of the vehicle to the landing pad. This tracking system is not only expensive but renders an unequipped ship unlandable. We address the challenge of tracking shipdeck without additional infrastructure on the deck. We present two methods based on video and lidar that are able to track the shipdeck starting at a considerable distance from the ship. This redundant sensor design enables us to have two independent tracking systems. We show the results of the tracking algorithms in 3 different environments, 1. field testing results on actual helicopter flights, 2. in simulation with a moving shipdeck for lidar based tracking and 3. in laboratory using an occluded and moving scaled model of a landing deck for camera based tracking. The complimentary modalities allow shipdeck tracking under varying conditions.

Mapping a rivers course and width provides valuable information to help understand the ecology, topology and health of a particular environment. Such maps can also be useful to determine whether specific surface vessels can traverse the rivers. While rivers can be mapped from satellite imagery, the presence of vegetation, sometimes so thick that the canopy completely occludes the river, complicates the process of mapping. Here we propose the use of a micro air vehicle flying under the canopy to create accurate maps of the environment. We study and present a system that can autonomously explore rivers without any prior information, and demonstrate an algorithm that can guide the vehicle based upon local sensors mounted on board the flying vehicle that can perceive the river, bank and obstacles. Our field experiments demonstrate what we believe is the first autonomous exploration of rivers by an autonomous vehicle. We show the 3D maps produced by our system over runs of 100-450 meters in length and compare guidance decisions made by our system to those made by a human piloting a boat carrying our system over multiple kilometers.