Interactive Planning and Sensing for Aircraft in Uncertain Environments with Spatiotemporally Evolving Threats

Autonomous aerial, terrestrial, and marine vehicles provide a platform for several applications including cargo transport, information gathering, surveillance, reconnaissance, and search-and-rescue. To enable such applications, two main technical problems are commonly addressed.On the one hand, the motion-planning problem addresses optimal motion to a destination: an application example is the delivery of a package in the shortest time with least fuel. Solutions to this problem often assume that all relevant information about the environment is available, possibly with some uncertainty. On the other hand, the information gathering problem addresses the maximization of some metric of information about the environment: application examples include such as surveillance and environmental monitoring. Solutions to the motion-planning problem in vehicular autonomy assume that information about the environment is available from three sources: (1) the vehicle’s own onboard sensors, (2) stationary sensor installations (e.g. ground radar stations), and (3) other information gathering vehicles, i.e., mobile sensors, especially with the recent emphasis on collaborative teams of autonomous vehicles with heterogeneous capabilities. Each source typically processes the raw sensor data via estimation algorithms. These estimates are then available to a decision making system such as a motion- planning algorithm. The motion-planner may use some or all of the estimates provided. There is an underlying assumption of “separation� between the motion-planning algorithm and the information about environment. This separation is common in linear feedback control systems, where estimation algorithms are designed independent of control laws, and control laws are designed with the assumption that the estimated state is the true state. In the case of motion-planning, there is no reason to believe that such a separation between the motion-planning algorithm and the sources of estimated environment information will lead to optimal motion plans, even if the motion planner and the estimators are themselves optimal. The goal of this dissertation is to investigate whether the removal of this separation, via interactive motion-planning and sensing, can significantly improve the optimality of motion- planning. The major contribution of this work is interactive planning and sensing. We consider the problem of planning the path of a vehicle, which we refer to as the actor, to traverse a threat field with minimum threat exposure. The threat field is an unknown, time- variant, and strictly positive scalar field defined on a compact 2D spatial domain – the actor’s workspace. The threat field is estimated by a network of mobile sensors that can measure the threat field pointwise. All measurements are noisy. The objective is to determine a path for the actor to reach a desired goal with minimum risk, which is a measure sensitive not only to the threat exposure itself, but also to the uncertainty therein. A novelty of this problem setup is that the actor can communicate with the sensor network and request that the sensors position themselves in a procedure we call sensor reconfiguration such that the actor’s risk is minimized. This work continues with a foundation in motion planning in time-varying fields where waiting is a control input. Waiting is examined in the context of finding an optimal path with considerations for the cost of exposure to a threat field, the cost of movement, and the cost of waiting. For example, an application where waiting may be beneficial in motion-planning is the delivery of a package where adverse weather may pose a risk to the safety of a UAV and its cargo. In such scenarios, an optimal plan may include “waiting until the storm passes.� Results on computational efficiency and optimality of considering waiting in path- planning algorithms are presented. In addition, the relationship of waiting in a time- varying field represented with varying levels of resolution, or multiresolution is studied. Interactive planning and sensing is further developed for the case of time-varying environments. This proposed extension allows for the evaluation of different mission windows, finite sensor network reconfiguration durations, finite planning durations, and varying number of available sensors. Finally, the proposed method considers the effect of waiting in the path planner under the interactive planning and sensing for time-varying fields framework. Future work considers various extensions of the proposed interactive planning and sensing framework including: generalizing the environment using Gaussian processes, sensor reconfiguration costs, multiresolution implementations, nonlinear parameters, decentralized sensor networks and an application to aerial payload delivery by parafoil

Resource Management for Distributed Estimation via Sparsity-Promoting Regularization

Recent advances in wireless communications and electronics have enabled the development of low-cost, low-power, multifunctional sensor nodes that are small in size and communicate untethered in a sensor network. These sensor nodes can sense, measure, and gather information from the environment and, based on some local processing, they transmit the sensed data to a fusion center that is responsible for making the global inference. Sensor networks are often tasked to perform parameter estimation; example applications include battlefield surveillance, medical monitoring, and navigation. However, under limited resources, such as limited communication bandwidth and sensor battery power, it is important to design an energy-efficient estimation architecture. The goal of this thesis is to provide a fundamental understanding and characterization of the optimal tradeoffs between estimation accuracy and resource usage in sensor networks. In the thesis, two basic issues of resource management are studied, sensor selection/scheduling and sensor collaboration for distributed estimation, where the former refers to finding the best subset of sensors to activate for data acquisition in order to minimize the estimation error subject to a constraint on the number of activations, and the latter refers to seeking the optimal inter-sensor communication topology and energy allocation scheme for distributed estimation systems. Most research on resource management so far has been based on several key assumptions, a) independence of observation, b) strict resource constraints, and c) absence of inter-sensor communication, which lend analytical tractability to the problem but are often found lacking in practice. This thesis introduces novel techniques to relax these assumptions and provide new insights into addressing resource management problems. The thesis analyzes how noise correlation affects solutions of sensor selection problems, and proposes both a convex relaxation approach and a greedy algorithm to find these solutions. Compared to the existing sensor selection approaches that are limited to the case of uncorrelated noise or weakly correlated noise, the methodology proposed in this thesis is valid for any arbitrary noise correlation regime. Moreover, this thesis shows a correspondence between active sensors and the nonzero columns of an estimator gain matrix. Based on this association, a sparsity-promoting optimization framework is established, where the desire to reduce the number of selected sensors is characterized by a sparsity-promoting penalty term in the objective function. Instead of placing a hard constraint on sensor activations, the promotion of sparsity leads to trade-offs between estimation performance and the number of selected sensors. To account for the individual power constraint of each sensor, a novel sparsity-promoting penalty function is presented to avoid scenarios in which the same sensors are successively selected. For solving the proposed optimization problem, we employ the alternating direction method of multipliers (ADMM), which allows the optimization problem to be decomposed into subproblems that can be solved analytically to obtain exact solutions. The problem of sensor collaboration arises when inter-sensor communication is incorporated in sensor networks, where sensors are allowed to update their measurements by taking a linear combination of the measurements of those they interact with prior to transmission to a fusion center. In this thesis, a sparsity-aware optimization framework is presented for the joint design of optimal sensor collaboration and selection schemes, where the cost of sensor collaboration is associated with the number of nonzero entries of a collaboration matrix, and the cost of sensor selection is characterized by the number of nonzero rows of the collaboration matrix. It is shown that a) the presence of sensor collaboration smooths out the observation noise, thereby improving the quality of the signal and eventual estimation performance, and b) there exists a trade-off between sensor selection and sensor collaboration. This thesis further addresses the problem of sensor collaboration for the estimation of time-varying parameters in dynamic networks that involve, for example, time-varying observation gains and channel gains. Impact of parameter correlation and temporal dynamics of sensor networks on estimation performance is illustrated from both theoretical and practical points of view. Last but not least, optimal energy allocation and storage control polices are designed in sensor networks with energy-harvesting nodes. We show that the resulting optimization problem can be solved as a special nonconvex problem, where the only source of nonconvexity can be isolated to a constraint that contains the difference of convex functions. This specific problem structure enables the use of a convex-concave procedure to obtain a near-optimal solution

Cooperative and non-cooperative sense-and-avoid in the CNS+A context: a unified methodology

A unified approach to cooperative and noncooperative Sense-and-Avoid (SAA) is presented that addresses the technical and regulatory challenges of Unmanned Aircraft Systems (UAS) integration into nonsegregated airspace. In this paper, state-of-the-art sensor/system technologies for cooperative and noncooperative SAA are reviewed and a reference system architecture is presented. Automated selection of sensors/systems including passive and active Forward Looking Sensors (FLS), Traffic Collision Avoidance System (TCAS) and Automatic Dependent Surveillance - Broadcast (ADS-B) system is performed based on Boolean Decision Logics (BDL) to support trusted autonomous operations during all flight phases. The BDL adoption allows for a dynamic reconfiguration of the SAA architecture, based on the current error estimates of navigation and tracking sensors/systems. The significance of this approach is discussed in the Communication, Navigation and Surveillance/Air Traffic Management and Avionics (CNS+A) context, with a focus on avionics and ATM certification requirements. Additionally, the mathematical models employed in the SAA Unified Method (SUM) to compute the overall uncertainty volume in the airspace surrounding an intruder/obstacle are described. In the presented methodology, navigation and tracking errors affecting the host UAS platform and intruder sensor measurements are translated to unified range and bearing uncertainty descriptors. Simulation case studies are presented to evaluate the performance of the unified approach on a representative UAS host platform and a number of intruder platforms. The results confirm the validity of the proposed unified methodology providing a pathway for certification of SAA systems that typically employ a suite of non-cooperative sensors and/or cooperative systems

AAS/GSFC 13th International Symposium on Space Flight Dynamics

This conference proceedings preprint includes papers and abstracts presented at the 13th International Symposium on Space Flight Dynamics. Cosponsored by American Astronautical Society and the Guidance, Navigation and Control Center of the Goddard Space Flight Center, this symposium featured technical papers on a wide range of issues related to orbit-attitude prediction, determination, and control; attitude sensor calibration; attitude dynamics; and mission design

Applications of information theory in filtering and sensor management

“A classical sensor tasking methodology is analyzed in the context of generating sensor schedules for monitoring resident space objects (RSOs). This approach, namely maximizing the expected Kullback-Leibler divergence in a measurement update, is evaluated from a probabilistic perspective to determine the accuracy of the conventional approach. In this investigation, a newdivergence-based approach is proposed to circumvent themyopic nature of the measure, forecasting the potential information contribution to a time of interest and leveraging the system dynamics and measurement model to do so. The forecasted objective exploits properties of a batch measurement update to frequently exhibit faster optimization times when compared to an accumulation of the conventional myopic employment. The forecasting approach additionally affords the ability to emphasize tracking performance at the point in time to which the information is mapped. The forecasted divergence is lifted into the multitarget domain and combined with a collision entropy objective. The addition of the collision consideration assists the tasking policy in avoiding scenarios in which determining the origin of a measurement is difficult, ameliorating issues when executing the sensor schedule. The properties of the divergencebased and collision entropy-based objectives are explored to determine appropriate optimization schemes that can enable their use in real-time application. It is demonstrated through a single-target tasking simulation that the forecasted measure successfully outperforms traditional approaches with regard to tracking performance at the forecasted time. This simulation is followed by a multitarget tasking scenario in which different optimization strategies are analyzed, illustrating the feasibility of the proposed tasking policy and evaluating the solution from both schedule quality and runtime perspectives”--Abstract, page iii

Flying Qualities Built-In-Test For Unmanned Aerial Systems

This paper presents a flying qualities built-in-test for UAS application with the scope limited to the longitudinal axis. A doublet input waveform excites the AV and both α and q are used by EUDKF to estimate the A and B matrices which are short period approximations of the system. ζ, ω, GM, PM, observability, and controllability are calculated to determine flying qualities with the results displayed to the AVO in a color-coded, easy to interpret display. While SID algorithms have been flying in vehicles with adaptive control schemes, vehicles with other schemes (such as classical feedback) lack this built-in self assessment tool. In addition, adaptive control SID results are not analyzed and displayed but instead used internally. This work intends to extend this self-assessment option to all UASs regardless of control scheme as a “plug-and-play” add-on by building a reliable and robust tool that requires little tuning

Optimized state feedback regulation of 3DOF helicopter system via extremum seeking

In this paper, an optimized state feedback regulation of a 3 degree of freedom (DOF) helicopter is designed via extremum seeking (ES) technique. Multi-parameter ES is applied to optimize the tracking performance via tuning State Vector Feedback with Integration of the Control Error (SVFBICE). Discrete multivariable version of ES is developed to minimize a cost function that measures the performance of the controller. The cost function is a function of the error between the actual and desired axis positions. The controller parameters are updated online as the optimization takes place. This method significantly decreases the time in obtaining optimal controller parameters. Simulations were conducted for the online optimization under both fixed and varying operating conditions. The results demonstrate the usefulness of using ES for preserving the maximum attainable performance