Safe autonomous flight is essential for widespread acceptance of aircraft that must fly close to the ground. We have developed a methodology of collision avoidance that can be used in three dimensions in much the same way as autonomous ground vehicles that navigate over unexplored terrain. Safe navigation is accomplished by a combination of online environmental sensing, path planning and collision avoidance. Here we outline our methodology and report results with an autonomous helicopter that operates at low elevations in uncharted environments some of which are densely populated with obstacles such as buildings, trees and wires. We have recently completed over 700 successful runs in which the helicopter traveled between coarsely specified waypoints separated by hundreds of meters, at speeds up to 10 meters/sec at elevations of 5-11 meters above ground level. The helicopter safely avoids large objects like buildings and trees but also wires as thin as 6 mm. We believe this represents the first time an air vehicle has traveled this fast so close to obstacles. The collision avoidance method learns to avoid obstacles by observing the performance of a human operator.

Abstract-Safe autonomous flight is essential for widespread acceptance of aircraft that must fly close to the ground. We have developed a method of collision avoidance that can be used in three dimensions in much the same way as autonomous ground vehicles that navigate over unexplored terrain. Safe navigation is accomplished by a combination of online environmental sensing, path planning and collision avoidance. Here we report results with an autonomous helicopter that operates at low elevations in uncharted environments some of which are densely populated with obstacles such as buildings, trees and wires. We have recently completed over 1000 successful runs in which the helicopter traveled between coarsely specified waypoints separated by hundreds of meters, at speeds up to 10 meters/sec at elevations of 5-10 meters above ground level. The helicopter safely avoids large objects like buildings and trees but also wires as thin as 6mm. We believe this represents the first time an air vehicle has traveled this fast so close to obstacles. Here we focus on the collision avoidance method that learns to avoid obstacles by observing performance of a human operator.

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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-Rivers with heavy vegetation are hard to map from the air. Here we consider the task of mapping their course and the vegetation along the shores with the specific intent of determining river width and canopy height. A complication in such riverine environments is that only intermittent GPS may be available depending on the thickness of the surrounding canopy. We present a multimodal perception system to be used for the active exploration and mapping of a river from a small rotorcraft flying a few meters above the water. We describe three key components that use computer vision, laser scanning, and inertial sensing to follow the river without the use of a prior map, estimate motion of the rotorcraft, ensure collisionfree operation, and create a three dimensional representation of the riverine environment. While the ability to fly simplifies the navigation problem, it also introduces an additional set of constraints in terms of size, weight and power. Hence, our solutions are cognizant of the need to perform multi-kilometer missions with a small payload. We present experimental results along a 2km loop of river using a surrogate system.