Scott Gruber

Maximizing target detection under sunlight reflection on water surfaces with an autonomous unmanned aerial vehicle

Reflected sunlight can significantly impact vision-based object detection and tracking algorithms, especially ones based on an aerial platform operating over a marine environment. Unmanned aerial systems above a water surface may be unable to detect objects on the water surface due to sunlight glitter. Although the area affected by sunlight reflection may be limited, rapid course corrections of unmanned aerial vehicles (UAVs)-especially fixed-wing UAVs-is also limited by aerodynamics, making it challenging to determine a reasonable path that avoids sunlight reflection while maximizing chances to capture a target. In this paper, we propose an approach for autonomous UAV path planning that maximizes the accuracy of the estimated target location by minimizing the sunlight reflection influences.

Flexible multi-agent algorithm for distributed decision making

This article presents an algorithm for decentralized control and decision making among a number of unmanned aircraft systems (UASs). The approach is scalable and adaptable to a variety of specific mission tasks. Additionally, the algorithm could easily be adapted for use on land or sea-based systems. Each UAS involved in an automated decision making process uses a selection of both predefined and derived parameters with corresponding predetermined weights to develop a score. These scores, and associated “scorecards,” are then iteratively distributed to all involved agents, ranked, and used to determine task participants. Simulation as well as flight testing shows the algorithm provides appropriate team assignments in a variety of situations while remaining robust to agent dropouts, inconsistent communications, and dynamic situations.

Autonomous Target Detection and Localization Using Cooperative Unmanned Aerial Vehicles

In this chapter we present sensor implementation issues encountered in developing a team of cooperative unmanned aerial vehicles (UAVs) for intelligence, surveillance and reconnaissance missions. UAVs that compose the cooperative team are equipped with heterogeneous sensors and onboard processing capability. The sensor information collected by each UAV is constantly shared among the neighboring UAVs and processed locally using Out-of-Order Sigma-Point Kalman Filtering (O3SPKF) techniques. Results from flight experiments support the effectiveness of the cooperative autonomous UAV technologies.

Fault Tolerant Margins for Unmanned Aerial Vehicle Flight Safety

Consider an unmanned aerial vehicle (UAV) operation from the time it is launched until the time it is put into an autonomous flight control mode. The control input u(t) during this time duration is modeled σu(t) and assumed healthy with σ = 1. In practice, however, the control inputs are less effective with a σ-value in the interval [0, 1) (Fan et al. 2012; Jayakumar and Das 2006; Hu et al. J. Guid. Control. Dyn. 34(3), 927–932, 2011). Complete damaged condition is inferred from σ = 0. Given a stabilizing controller, a range of σ-values in the interval [0,1) for which the closed loop system would remain stable is referred as the fault-tolerant margin (FTM). Flight operations with control effectiveness beyond the FTM are catastrophic. Further, when aerodynamic parameter variations due to an uncertain atmospheric condition in an UAV are present, it is shown that the FTMs are extremely sensitive to these parameter perturbations. In this paper, the FTMs are presented. The effects of control effectiveness factor on the nonlinear UAV when it is in the stable range are investigated. An UAV model in Ashokkumar and York (2016) is considered to illustrate the FTMs as a threshold to guarantee the safe operation.

UAV handbook: Payload design of small uavs

This chapter discusses the payload design issues for small unmanned aerial vehicles (UAVs). Details of several payload design principles to overcome various small UAV constraints imposed by stringent weight, power, and volume are discussed. Throughout the chapter, the efficacy of these principles is demonstrated with the help of the payloads for a fixed wing UAV developed by the S. Gruber ( ) • H. Kwon • C. Hager • R. Sharma • J. Yoder Academy Center for Unmanned Aircraft Systems Research, Department of Electrical and Computer Engineering, United States Air Force Academy, Colorado Springs, CO, USA e-mail: scott.gruber@usafa.edu; hyukseong.kwon@usafa.edu; chad.hager.ctr@usafa.edu; rajnikant.sharma.ctr.in@usafa.edu; josiah.yoder.ctr@usafa.edu D. Pack Department of Electrical and Computer Engineering, University of Texas at San Antonio, San Antonio, TX, USA e-mail: daniel.pack@utsa.edu K.P. Valavanis, G.J. Vachtsevanos (eds.), Handbook of Unmanned Aerial Vehicles, DOI 10.1007/978-90-481-9707-1 84,

Maximizing Water Surface Target Localization Accuracy Under Sunlight Reflection with an Autonomous Unmanned Aerial Vehicle

Reflected sunlight can significantly impact the effectiveness of vision-based object detection and tracking algorithms, especially ones developed for an aerial platform operating over a marine environment. These algorithms often fail to detect water surface objects due to sunlight glitter or rapid course corrections of unmanned aerial vehicles (UAVs) generated by the laws of aerodynamics. In this paper, we propose a UAV path planning method that maximizes the stationary or mobile target detection likelihood during localization and tracking by minimizing the sunlight reflection influences. In order to better reduce sunlight reflection effects, an image-based sunlight reflection reception adjustment is also proposed. We validate our method using both stationary and mobile target tracking tests.

Nonlinear cooperative UAV maneuvers in pitch plane

Flight formations of unmanned aerial vehicles may require coordinated motion in pitch for such tasks as terrain tracking. They maintain a constant altitude over varying terrain elevations and may assist collision avoidance where an altitude change is needed rather than a lateral change. In these maneuvers, controller ability to adjust relative altitude positions (as attractions and repulsions) of the aircraft subject to stability constraints that ends up in a formation shape should be demonstrated. The ascent and descent flight control mode combinations of each unmanned aerial vehicle participating in the formation generally offer the attractions and repulsions. In this paper, centralized and decentralized controller abilities to develop a cooperative formation with flight control modes made of transients and steady states are presented by using two and more than two homogenous unmanned aerial vehicles. A challenge in presenting such a formation by using a centralized controller is its design itself. Generally, eigenstructure properties depict flight control mode variations. That is, variations in closed-loop poles offered by a structurally varying centralized controller compatible to its reconfigurable communication patterns are sufficient to capture the flight control mode options. Hence, a procedure to design such a controller using real parameters is presented.

Proportional–integral–derivative controller family for pole placement:

In this paper, linear time-invariant square systems are considered. A procedure to design infinitely many proportional–integral–derivative controllers, all of them assigning closed-loop poles (or closed-loop eigenvalues), at desired locations fixed in the open left half plane of the complex plane is presented. The formulation accommodates partial pole placement features. The state-space realization of the linear system incorporated with a proportional–integral–derivative controller boils down to the generalized eigenvalue problem. The generalized eigenvalue-eigenvector constraint is transformed into a system of underdetermined linear homogenous set of equations whose unknowns include proportional–integral–derivative parameters. Hence, the proportional–integral–derivative solution sets are infinitely many for the chosen closed-loop eigenvalues in the eigenvalue-eigenvector constraint. The solution set is also useful to reduce the tracking errors and improve the performance. Three examples are illustrated.

Fault tolerant margins of an UAV at uncertain environment

In fault tolerant control, the damaged and healthy states of a sensor output or a control input are captured by using an uncertain parameter. For instance, the control input u(t) resulting from an actuator is modeled au(t) and the healthy and damaged conditions are inferred from a which takes the values σ=1 and σ in [0, 1), respectively [1-3]. Given a stabilizing controller, a range of σ-values in the interval [0,1) for which the closed loop system would remain stable is referred as the fault-tolerant margin. In this paper, several of these margins are presented when aerodynamic parameter variations due to an uncertain atmospheric condition in an unmanned aerial vehicle (UAV) are present. It is shown that the fault tolerant margins are extremely sensitive to these parameter perturbations. Some perturbation locations can significantly reduce the stable range for parameter values as well as for the fault tolerant margins. An UAV model is considered to illustrate the fault tolerant margins and admissible parameter variations. Secondly, the algorithm applied to compute the fault tolerant margins is used to solve the examples of Professor Ross B Barmish et al [29] and hence the exact stability margins for a two parameter system are illustrated. However, when the number of parameters increases, the algorithm becomes computationally intensive.

Nonlinear trajectory transcriptions for aircraft loss of control recovery

Aircraft loss of control may occur when the interpolated gain set is such that it destabilizes the linear aircraft. That is, when linear aircraft is not asymptotically stable, the nonlinear aircraft loss of control occurs. Even in cases where interpolation stabilizes the linear aircraft, loss of control may also occur when the gains are not reconfigured within the stability region associated with the controller. Hence, understanding nonlinear aircraft navigation and control algorithms using such controllers that originate from various linear design methods becomes a challenging problem. It prompts to simulate the nonlinear aircraft trajectories within the distorted stability regions of the linear controllers interpolated when each one of them is designed to regulate an equilibrium (or trim) point. In this article, the gain interpolation parameter that would lead to loss of control is presented. Then, techniques to avert the loss of control using nonlinear trajectory transcriptions are presented. A 3-degree-of-freedom aircraft model of Langelaan is considered for illustrations.