Decomposition-based mission planning for fixed-wing UAVs surveying in wind

This paper presents a new method for planning fixed-wing aerial survey paths that ensures efficient image coverage of a large complex agricultural field in the presence of wind. By decomposing any complex polygonal field into multiple convex polygons, the traditional back-and-forth boustrophedon paths can be used to ensure coverage of these decomposed regions. To decompose a complex field in an efficient and fast manner, a top-down recursive greedy approach is used to traverse the search space in order to minimise flight time of the survey. This optimisation can be computed fast enough for use in the field. As wind can severely affect flight time, it is included in the flight time calculation in a systematic way using a verified cost function that offer greatly reduced survey times in wind. Other improved cost functions have been developed to take into account real world problems, e.g. No Fly Zones, in addition to flight time. A number of real surveys are performed in order to show the flight time in wind model is accurate, to make further comparisons to previous techniques and to show that the proposed method works in real-world conditions providing total image coverage. A number of missions are generated and flown for real complex agricultural fields. In addition to this, the wind field around a survey area is measured from a multi-rotor carrying an ultrasonic wind speed sensor. This shows that the assumption of steady uniform wind holds true for the small areas and time scales of a Unmanned Aerial Vehicle (UAV) aerial survey.</div

Fixed wing UAV survey coverage path planning in wind for improving existing ground control station software

UAV technology is becoming increasingly mature and cheap. This recent cost reduction and performance improvement means that these systems have become reliable and cheap enough to be viable for mass use by farmers and operators for precision agriculture activities; such as disease identification, yield estimation and plant nitrogen monitoring. A critical stage of this is the planning of the flight path which ensures complete image coverage of the region of interest. If the field is a simple convex shape, then generating an optimal coverage path is hugely simplified by using a back and forth Boustrophedon path. However, most fields have complex polygonal shapes, where planning the coverage path manually is nontrivial, as operators may not have the correct skills and experience. This is why in this paper, we outline an algorithm to improve the performance of survey path generation on complex ROI for mission planning software. The tools implemented in this paper take into account environmental factors and aircraft dynamics. By decomposing these complex survey regions into many smaller arrangements of manageable convex polygon survey regions, Boustrophedon paths can be used to cover them. By using a survey model for calculation of flight time in a wind field. it is used to optimise the decomposition in order to lower flight time. The fastest survey path is used to generate waypoints files to be used with a number of popular mission planning software including: DJI PC Ground Control, Mission Planner, QGroundControl

Optimal polygon decomposition for UAV survey coverage path planning in wind

In this paper, a new method for planning coverage paths for fixed-wing Unmanned Aerial Vehicle (UAV) aerial surveys is proposed. Instead of the more generic coverage path planning techniques presented in previous literature, this method specifically concentrates on decreasing flight time of fixed-wing aircraft surveys. This is achieved threefold: by the addition of wind to the survey flight time model, accounting for the fact fixed-wing aircraft are not constrained to flight within the polygon of the region of interest, and an intelligent method for decomposing the region into convex polygons conducive to quick flight times. It is shown that wind can make a huge difference to survey time, and that flying perpendicular can confer a flight time advantage. Small UAVs, which have very slow airspeeds, can very easily be flying in wind, which is 50% of their airspeed. This is why the technique is shown to be so effective, due to the fact that ignoring wind for small, slow, fixed-wing aircraft is a considerable oversight. Comparing this method to previous techniques using a Monte Carlo simulation on randomised polygons shows a significant reduction in flight time

Drone Fleet Deployment Strategy for Large Scale Agriculture and Forestry Surveying

International audienceAgriculture drones offer clear advantages over other monitoring methods including satellite imaging, manned scouting, and manned aircraft. However, for large scale areas, such as large forestry and agriculture mapping problems, the single drone is hard to accomplish its mission of mapping in a relatively short time period of 30 to 45 minutes. In addition, in large forestry mapping, camera, communication, and payload settings may further reduce the maximum endurance of drones in the air. With a single drone, the total required mission time to cover all the area is prolonged, not only producing a high cost for a drone service provider but also having more uncertainty. While with multiple drones, or a fleet of drones, it is possible to identify a globally optimized solution to reduce the total required mission time. In this paper, we mainly discuss the strategy of drone fleet deployment for large scale area surveying. Three key parts are analyzed, including a fleet of drones, cooperative coverage path planning, communication and data processing. The associated state-of-the-art solutions are listed and reviewed. In addition, in this paper, the key operational constraints for large scale agriculture and forestry surveying are analyzed. It should be pointed out that, from a comprehensive point of view, a drone fleet deployment for large scale surveying could attract more attention from the commercial drone industry

Mixed initiative planning and control of UAV teams for persistent surveillance

Tese de mestrado. Mestrado Integrado em Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 201

Flight testing Boustrophedon coverage path planning for fixed wing UAVs in wind

A method was previously developed by this author to optimise the flight path of a fixed wing UAV performing aerial surveys of complex concave agricultural fields. This relies heavily on a flight time in wind prediction model as its cost function. This paper aims to validate this model by comparing flight test results with the model prediction. There are a number of assumptions that this model relies on. The major assumption is that wind is steady and uniform over the small area and time scales involved in a survey. To show that this is reasonable, wind fields measurements will be taken from a multi rotor UAV with an ultrasonic windspeed sensor

Boustrophedon coverage path planning for UAV aerial surveys in wind

© 2017 IEEE. In the quickly developing world of precision agriculture UAV remote sensing, there is a need for a greater understanding of winds effect on fixed wing aerial surveying, as this is missing from current literature. This paper presents a method to define and calculate flight times in a Boustrophedon aerial survey coverage path in wind, for a given convex polygon, at a given sweep angle. It is shown that there exists no easy way to define a sweep angle relative to the wind that minimises flight time. This method is validated by comparing the numerical simulated path and times with a number of surveys run in the high fidelity X-Plane simulator

A benchmarking of commercial small fixed-wing electric UAVs and RGB cameras for photogrammetry monitoring in intertidal multi-regions

Small fixed-wing electric Unmanned Aerial Vehicles (UAVs) are perfect candidates to perform tasks in wide areas, such as photogrammetry, surveillance, monitoring, or search and rescue, among others. They are easy to transport and assemble, have much greater range and autonomy, and reach higher speeds than rotatory-wing UAVs. Aiming to contribute towards their future implementation, the objective of this article is to benchmark commercial, small, fixed-wing, electric UAVs and compatible RGB cameras to find the best combination for photogrammetry and data acquisition of mussel seeds and goose barnacles in a multi-region intertidal zone of the south coast of Galicia (NW of Spain). To compare all the options, a Coverage Path Planning (CPP) algorithm enhanced for fixed-wing UAVs to cover long areas with sharp corners was posed, followed by a Traveling Salesman Problem (TSP) to find the best route between regions. Results show that two options stand out from the rest: the Delair DT26 Open Payload with a PhaseOne iXM-100 camera (shortest path, minimum number of pictures and turns) and the Heliplane LRS 340 PRO with the Sony Alpha 7R IV sensor, finishing the task in the minimum time.Agencia Estatal de Investigación | Ref. PID2021-125060OB-I00Agencia Estatal de Investigación | Ref. TED2021-129756B-C31Ministerio de Universidades | Ref. FPU21/01176Universidade de Vig

Aerial Remote Sensing in Agriculture: A Practical Approach to Area Coverage and Path Planning for Fleets of Mini Aerial Robots

In this paper, a system that allows applying precision agriculture techniques is described. The application is based on the deployment of a team of unmanned aerial vehicles that are able to take georeferenced pictures in order to create a full map by applying mosaicking procedures for postprocessing. The main contribution of this work is practical experimentation with an integrated tool. Contributions in different fields are also reported. Among them is a new one-phase automatic task partitioning manager, which is based on negotiation among the aerial vehicles, considering their state and capabilities. Once the individual tasks are assigned, an optimal path planning algorithm is in charge of determining the best path for each vehicle to follow. Also, a robust flight control based on the use of a control law that improves the maneuverability of the quadrotors has been designed. A set of field tests was performed in order to analyze all the capabilities of the system, from task negotiations to final performance. These experiments also allowed testing control robustness under different weather conditions

Coverage path planning methods focusing on energy efficient and cooperative strategies for unmanned aerial vehicles

The coverage path planning (CPP) algorithms aim to cover the total area of interest with minimum overlapping. The goal of the CPP algorithms is to minimize the total covering path and execution time. Significant research has been done in robotics, particularly for multi-unmanned unmanned aerial vehicles (UAVs) cooperation and energy efficiency in CPP problems. This paper presents a review of the early-stage CPP methods in the robotics field. Furthermore, we discuss multi-UAV CPP strategies and focus on energy-saving CPP algorithms. Likewise, we aim to present a comparison between energy efficient CPP algorithms and directions for future research