Joshua Redding

Vision-based target localization from a fixed-wing miniature air vehicle

This paper presents a method for localizing a ground-based object when imaged from a small fixed-wing unmanned aerial vehicle (UAV). Using the pixel location of the target in an image, with measurements of UAV position and attitude, and camera pose angles, the target is localized in world coordinates. This paper presents a study of possible error sources and localization sensitivities to each source. The localization method has been implemented and experimental results are presented demonstrating the localization of a target to within 11 m of its known location

Automated Battery Swap and Recharge to Enable Persistent UAV Missions

This paper introduces a hardware platform for automated battery changing and charging for multiple UAV agents. The automated station holds a buffer of 8 batteries in a novel dual-drum structure that enables a “hot” battery swap, thus allowing the vehicle to remain powered on throughout the battery changing process. Each drum consists of four battery bays, each of which is connected to a smartcharger for proper battery maintenance and charging. The hot-swap capability in combination with local recharging and a large 8-battery capacity allow this platform to refuel multiple UAVs for long-duration and persistent missions with minimal delays and no vehicle shutdowns. Experimental results from the RAVEN indoor flight test facility are presented that demonstrate the capability and robustness of the battery change/charge station in the context of a multi-agent, persistent mission where surveillance is continuously required over a specified region.

An intelligent Cooperative Control Architecture

This paper presents an extension of existing cooperative control algorithms that have been developed for multi-UAV applications to utilize real-time observations and/or performance metric(s) in conjunction with learning methods to generate a more intelligent planner response. We approach this issue from a cooperative control perspective and embed elements of feedback control and active learning, resulting in an new intelligent Cooperative Control Architecture (iCCA). We describe this architecture, discuss some of the issues that must be addressed, and present illustrative examples of cooperative control problems where iCCA can be applied effectively.

An Adaptive Fault Management (AFM) System for Resilient Flight Control

prone to false alarms. If a false failure information is used in the feedback control law, this can lead to substantial performance deterioration and even instability of the closed-loop system. One possible solution to this problem is Self Diagnostics (SD) - a procedure where probing signals are injected into the system to try to identify failure or damage-related parameters. Such a procedure needs to be implemented with care since it can potentially excite aircraft structural modes. In addition, self diagnostics can induce an oscillatory response of the aircraft states while not still being insufficient to accurately identify the parameters, particularly if their number is large. Hence such an approach is effective only if the number of failure-related parameters is small. Knowledge of the post-fault parameter values is important for: (i) calculation of available control authority immediately following an upset and control reconfiguration, (ii) computation of achievable trim points; and (iii) calculation of new control constraints. In this paper we present and discuss our Fast on-Line Actuator Reconfiguration Enhancement (FLARE) Systemthat is based on decentralized detection and identification of flight control actuator failures, and results in a relatively small number of parameters that need to be identified on-line. FLARE was recently augmented by the self-diagnostics module, resulting in an effective system that accurately estimates the parameters such that the effect of the self diagnostic signals is not felt by the aircraft states. Hence the possibility of exciting the structural modes is minimized. The FLARE system with self diagnostics (FLARE-SD) is described in detail, as well as the areas where such knowledge can be effectively used to increase the pilot’s awareness and improve flight safety. The features of the FLARE-SD system are illustrated through simulations of F/A-18 aircraft dynamics under actuator failures.

Robust Fault-Tolerant Flight Control using a New Failure Parameterization

Most available approaches for adaptive accommodation of failures in flight control actuators result in a large number of parameters that need to be adjusted on-line. In this paper we propose a new failure parameterization that models a large class of failures in terms of a single parameter. The class of failures includes lock-in-place (LIP), float, hard- over and loss of effectiveness (LOE). It is shown that th; new parameterization accurately models this class of failures, and that the resulting model can be used for observer design to estimate the uncertain parameters on-line. The use of the resulting estimates in the adaptive reconfigurable control law at every instant is shown to result in a stable closed-loop system. The estimation and control algorithms are integrated within the thetas-FLARE (thetas-parameterized Fast on-Line Actuator Reconfiguration Enhancement) architecture. Properties of thetas- FLARE and convergence properties of the failure parameter estimates are illustrated through simulations of F/A-18 aircraft dynamics under multiple flight-critical failures.

Agent capability in persistent mission planning using approximate dynamic programming

This paper presents an extension of our previous work on the persistent surveillance problem. An extended problem formulation incorporates real-time changes in agent capabilities as estimated by an onboard health monitoring system in addition to the existing communication constraints, stochastic sensor failure and fuel flow models, and the basic constraints of providing surveillance coverage using a team of autonomous agents. An approximate policy for the persistent surveillance problem is computed using a parallel, distributed implementation of the approximate dynamic programming algorithm known as Bellman Residual Elimination. This paper also presents flight test results which demonstrate that this approximate policy correctly coordinates the team to simultaneously provide reliable surveillance coverage and a communications link for the duration of the mission and appropriately retasks agents to maintain these services in the event of agent capability degradation.

Proactive planning for persistent missions using composite model-reference adaptive control and approximate dynamic programming

This paper extends prior work on the persistent mission problem where real-time changes in agent capability are included in the problem formulation. Here, we couple the mission planner with a low-level adaptive controller in real time to: (1) Provide robustness against actuator degradations and (2) Use parameters internal to the adaptive controller to provide valuable insight into the physical capabilities of the agent. These parameters, in conjunction with sensor health information, form a more complete measure of agent capability, which is used online and in forward planning to enable both reactive and proactive behavior. Flight results are presented for a persistent mission scenario where actuator degradations are induced to demonstrate: (1) The robustness of the composite adaptive controller and its successful integration with the agent level health-monitoring and mission-level planning systems and (2) The reactive and proactive qualities of the planning system in persistently re-tasking agents under actuator and sensor health degradations.

Scalable, MDP-based planning for multiple, cooperating agents

This paper introduces an approximation algorithm for stochastic multi-agent planning based on Markov decision processes (MDPs). Specifically, we focus on a decentralized approach for planning the actions of a team of cooperating agents with uncertainties in fuel consumption and health-related models. The core idea behind the algorithm presented in this paper is to allow each agent to approximate the representation of its teammates. Each agent therefore maintains its own planner that fully enumerates its local states and actions while approximating those of its teammates. In prior work, the authors approximated each teammate individually, which resulted in a large reduction of the planning space, but remained exponential (in n - 1 rather than in n, where n is the number of agents) in computational scalability. This paper extends the approach and presents a new approximation that aggregates all teammates into a single, abstracted entity. Under the persistent search & track mission scenario with 3 agents, we show that while resulting performance is decreased nearly 20% compared with the centralized optimal solution, the problem size becomes linear in n, a very attractive feature when planning online for large multi-agent teams.

An Autonomous Carrier Landing System for Unmannned Aerial Vehicles

One of the important problems related to naval Unmanned Aerial Vehicles (UAV), is the design of an automatic landing system that would enable autonomous landing of a UAV on an aircraft carrier. When landing needs to be accomplished safely in the high sea states and during the carrier turns, the problem becomes highly complex. In this paper we present an innovative autonomous carrier landing system for UAVs, referred to as the Carrier Motion Prediction & Autonomous Landing (CM-PAL) system. The system is based on real-time estimation of magnitudes and frequencies of waves encountered by the carrier, and online prediction of the carrier motion. This prediction generates information regarding the carrier states at touchdown; this information is in turn used to generate corrections in the UAV’s heading and flight path angle commands to achieve minimum dispersion around the desired touchdown point and heading. We also present performance evaluation results of the CM-PAL system on a high-fidelity simulation of typical aircraft carrier dynamics.

Stable Adaptive Reconfigurable Flight Control with Self-Diagnostics

In this paper a new approach to adaptive fault-tolerant control with self-diagnostics is presented. The approach is based on generating high-frequency signals for actuators with suspected failures, and minimizing the effect of those signals on the system state using the remaining healthy control surfaces. It is shown that the adjustment of parameters using a signal composed of the nominal control input, self-diagnostic signal and compensation signal, and the use of the corresponding parameter estimates in the control law results in a stable system in which the asymptotic convergence to zero is guaranteed for both the tracking and parameter errors. The properties of the proposed system are evaluated through simulations of a high- performance aircraft under multiple flight-critical failures.