Accurately mapping the course and vegetation along a river is challenging, since overhanging trees block GPS at ground level and occlude the shore line when viewed from higher altitudes. We present a multimodal perception system for the active exploration and mapping of a river from a small rotorcraft. We describe three key components that use computer vision, laser scanning, inertial sensing and intermittant GPS to estimate the motion of the rotorcraft, detect the river without a prior map, and create a 3D map of the riverine environment. Our hardware and software approach is cognizant of the need to perform multi-kilometer missions below tree level with size, weight and power constraints. We present experimental results along a 2 km loop of river using a surrogate perception payload. Overall we can build an S. Scherer ( ) · S. Achar · H. Cover · A. Chambers · S. Nuske · accurate 3D obstacle map and a 2D map of the river course and width from light onboard sensing.

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.

Abstract-Micro aerial vehicles operating outdoors must be able to maneuver through both dense vegetation and across empty fields. Existing approaches do not exploit the nature of such an environment. We have designed an algorithm which plans rapidly through free space and is efficiently guided around obstacles. In this paper we present SPARTAN (Sparse Tangential Network) as an approach to create a sparsely connected graph across a tangential surface around obstacles. We find that SPARTAN can navigate a vehicle autonomously through an outdoor environment producing plans 172 times faster than the state of the art (RRT*). As a result SPARTAN can reliably deliver safe plans, with low latency, using the limited computational resources of a lightweight aerial vehicle.

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.

In this paper, a vectored thrust aerial vehicle(VTAV) that has three ducted fans is considered. Since ducted fans are powerful and effective in providing lift, they are suitable for thrusters of UAVs, but modeling their aerodynamic effects such as ram drag is very difficult. The VTAV has one ducted fan fixed to its body and two ducted fans that can be tilted in order to make rotational moments, which makes the system dynamics even more complicated. This paper focuses on giving a precise dynamical model that includes aerodynamic effects of ducted fans. Then it presents a hovering controller based on the dynamical model developed. Since the horizontal dynamics are under actuated, a switching control approach is introduced to realize a stabilizing controller. Numerical simulations prove the validity of the approach.