Reducing the Noise Impact of Unmanned Aerial Vehicles by Flight Control System Augmentation

The aim of this thesis is to explore methods to reduce the noise impact of unmanned aerial vehicles operating within acoustically sensitive environments by flight control system augmentation. Two methods are investigated and include: (i) reduction of sound generated by vehicle speed control while flying along a nominal path and (ii) reduction of acoustic exposure by vehicle path control while flying at a nominal speed. Both methods require incorporation of an acoustic model into the flight control system as an additional control objective and an acoustic metric to characterize primary noise sources dependent on vehicle state. An acoustic model was developed based on Gutin’s work to estimate propeller noise, both to estimate source noise and observer noise using two separate acoustic metrics. These methods can potentially mitigate the noise impact of unmanned aerial systems operating near residential communities. The baseline flight control system of a representative aircraft was augmented with a control law to reduce propeller noise using feedback control of the commanded flight speed until an acoustic target was met, based on the propeller noise model. This control approach focuses on modifying flight speed only, with no perturbation to the trajectory. Multiple flight simulations were performed and the results showed that integrating an acoustic metric into the flight control system of an unmanned aerial system is possible and useful. A second method to mitigate the effects of noise on an observer was also pursued to optimize a trajectory in order to avoid an acoustically sensitive region during the path planning process. After the propeller noise model was incorporated into the vehicle system, simulations showed that it is possible to reduce the noise impact on an observer through an optimization of the trajectory with no perturbation to the flight speed

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

Dynamic Motion Planning for Aerial Surveillance on a Fixed-Wing UAV

We present an efficient path planning algorithm for an Unmanned Aerial Vehicle surveying a cluttered urban landscape. A special emphasis is on maximizing area surveyed while adhering to constraints of the UAV and partially known and updating environment. A Voronoi bias is introduced in the probabilistic roadmap building phase to identify certain critical milestones for maximal surveillance of the search space. A kinematically feasible but coarse tour connecting these milestones is generated by the global path planner. A local path planner then generates smooth motion primitives between consecutive nodes of the global path based on UAV as a Dubins vehicle and taking into account any impending obstacles. A Markov Decision Process (MDP) models the control policy for the UAV and determines the optimal action to be undertaken for evading the obstacles in the vicinity with minimal deviation from current path. The efficacy of the proposed algorithm is evaluated in an updating simulation environment with dynamic and static obstacles.Comment: Accepted at International Conference on Unmanned Aircraft Systems 201

A Survey of path following control strategies for UAVs focused on quadrotors

The trajectory control problem, defined as making a vehicle follow a pre-established path in space, can be solved by means of trajectory tracking or path following. In the trajectory tracking problem a timed reference position is tracked. The path following approach removes any time dependence of the problem, resulting in many advantages on the control performance and design. An exhaustive review of path following algorithms applied to quadrotor vehicles has been carried out, the most relevant are studied in this paper. Then, four of these algorithms have been implemented and compared in a quadrotor simulation platform: Backstepping and Feedback Linearisation control-oriented algorithms and NLGL and Carrot-Chasing geometric algorithms.Peer ReviewedPostprint (author's final draft

Energy-Optimal Path Planning for Solar-Powered Aircraft in Level Flight

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/76295/1/AIAA-2007-6655-400.pd

Aerial Vehicles

This book contains 35 chapters written by experts in developing techniques for making aerial vehicles more intelligent, more reliable, more flexible in use, and safer in operation.It will also serve as an inspiration for further improvement of the design and application of aeral vehicles. The advanced techniques and research described here may also be applicable to other high-tech areas such as robotics, avionics, vetronics, and space