Optimal complete terrain coverage using an Unmanned Aerial Vehicle

Abstract

Abstract-We present the adaptation of an optimal terrain coverage algorithm for the aerial robotics domain. The general strategy involves computing a trajectory through a known environment with obstacles that ensures complete coverage of the terrain while minimizing path repetition. We introduce a system that applies and extends this generic algorithm to achieve automated terrain coverage using an aerial vehicle. Extensive experimental results in simulation validate the presented system, along with data from over 100 kilometers of successful coverage flights using a fixed-wing aircraft.

Boustrophedon coverage path planning for UAV aerial surveys in wind

“…Small UAV will have a low mass and low airspeeds, which make them very susceptible to the effects of wind. This is clearly shown in [8] where wind added around 6 mins to a 21 min simulated fixed wing survey of an 1x0.6 km area in a number of different sweep angles. Since small UAV's have limited endurance and range, optimising the path in wind is essential.…”

“…If a vehicle is able to maintain a constant ground velocity, then the path with the lowest number of turns will be shortest [ [15], [8]]. During the turn, the aircraft will not be able to collect any data, this is as turns are conducted just outside the ROI, and any photos taken in a turn will not be orthogonal to the ground.…”

“…This is called a Boustrophedon path, which means "way of the ox". This is a simple and effective path when performed within a simple convex polygonal area [ [14], [8]]. All that is required to construct this path is the ROI polygon, the sweep angle of the paths, and cross track spacing determined by the UAVs image footprint, and required lateral overlap.…”

Multi-domain monitoring of marine environments using a heterogeneous robot team

“…2) Software Infrastructure: In order to automate the aerial data collection process, we developed an application that interfaces with the commercial control software for the Unicorn UAV, and in particular incorporates an autonomous navigation controller for conducting complete and repeated aerial coverage [19]. In addition, a front-end interface allows a human operator to monitor the UAV's status, select useful aerial images to send to biologists for inspection, and validate waypoints chosen by the biologists before re-transmitting them to our team of robots.…”

Socially-Driven Collective Path Planning for Robot Missions

“…For instance, our research group is interested in applying this path planner to a heterogeneous multi-robot system for assisting marine biologists in the study of coral reefs. Specifically, our robotic aircraft carries out coverage of the target reef region [1] while streaming live aerial footage to off-site scientists. These scientists then identify locations for further inspection, based on their expert domain knowledge and field experience of coral reefs.…”