Autonomous Exploration and Motion Planning for an Unmanned Aerial Vehicle Navigating Rivers

Abstract

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.

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).…”

Stereo vision-based obstacle avoidance for micro air vehicles using an egocylindrical image space representation

“…3,4 Vision-based approaches can be characterized based on how perception, scene representation, and motion planning and control are implemented. Common approaches use either optical flow, monocular or stereo depth perception, or a combination of these.…”

Survey on Computer Vision for UAVs: Current Developments and Trends

“…This algorithm is experimentally evaluated on a hexacopter. In [150] a complete aerial platform setup has been developed for river mapping. The proposed work employs a stereo camera and a laser scanner for the mapping, obstacle detection and state estimation.…”