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

Examining Point-Nonpoint Trading Ratios for Acid Mine Drainage Remediation with a Spatial-Temporal Optimization Model

A trading ratio is required for water quality trading that involves nonpoint sources to compensate for the difficulty of determining nonpoint loadings, the stochastic characteristics of nonpoint loadings, and the uncertainty inherent in nonpoint source pollution control strategies. Compensating for risk and uncertainty is one of the primary justifications that a trading ratio greater than one is commonly considered. However, the appropriate specific value of a trading ratio remains unclear because of qualitative differences between point and nonpoint sources. This study addresses a growing concern with the analytical underpinnings of point/nonpoint trading ratios in water quality trading programs. This paper considers a basic spatial-temporal optimal control model assuming that the goal of the decision maker is to maximize ecological services from the watershed over a 10-year planning horizon given a predetermined budget each year to treat acid mine drainage problems. The level of pollution is assumed to be known but declining slightly over time as the acid mine drainage sources evolve. Resources are assumed to be spent on remediation projects that produce long term but declining treatment results. The primary goal of the model is to distribute the available resources over the basin by investing in restoration projects for targeted streams each year that will maximize the ecological return on this investment. The model reflects both the spatial reality of variations in flow, in pollution, in treatment, and in the ecological benefits produced and the intertemporal constraints of limited resources and the inability to move remediation programs once the initial investment is made. The resulting optimal temporal and spatial investment strategies are derived from solutions to a mixed integer programming problem obtained using the GAMS/CPLEX mixed integer programming package. The optimal results are then manipulated to evaluate trading ratios. A hypothetical acidity trading scenario is proposed in which a point source (a new coal mine operation subject to TMDL rules) uses credits generated through remediation projects at other sites from treatment of nonpoint sources within the same basin over the 10-year planning horizon. The trading ratio is the ratio of the expected amount of pollutant removed by treating the nonpoint source divided by the amount of additional pollution allowed from the new point source. Our results indcate that point/nonpoint trading ratios in proposed trading scenarios greater than one can be justified. For example, for a point/nonpoint trade between sources in adjacent stream segments, the appropriate trading ratio is 3.66 (or 3.66 to one). We note that current regulations give a lower bound for point/nonpoint trading ratio of 1:1. The upper bound for point/nonpoint trading ratio depends on technical aspects of the relative costs of treating the point source or treating nonpoint sources and reflects the limit of how much one is willing to pay for credits. A variety of factors determine trading ratios. First, to encourage trades with less uncertainty, trades in which the credit seller and buyer are in close proximity, and in which the credit seller is upstream, lower trading ratios are recommended. Second, trading ratios should be adjusted to favor trades that contribute to strategic restoration goals such as the improvement or maintenance of water quality in a particular basin. Reduced ratios provide incentives to promote the generation of credits in priority locations. Finally, trading ratios for same-pollutant trades should be lower than those for cross-pollutant trades. Three separate trading currencies would be used to account for same-pollutant acid mine drainage trades: pounds of iron, aluminum, and manganese. There would be little uncertainty in the outcome of a trade if the credit generator and buyer were affecting the same pollutant. In contrast, cross-pollutant trades that use a common currency such as ecological indices would be measured based on their ecological effect, which is one step removed from the actual changes in pollutant loads. The higher trading ratio required for cross-pollutant trades reflects this greater uncertainty. All potential trades considered in this study are interspatial trades; trades occur in the same basin; trades could be cross-pollutant trades within acid mine draiange and same-pollutant trades as well; and the credit buyer is the new coal mining operation; credit generators could be government agencies or nonprofit organization; and abandned mine lands and bond forfeiture sites can be sites where credits are generated.point-nonpoint water quality trading, trading ratio, acid mine drainage, spatial-temporal optimization, Environmental Economics and Policy,

Facing urban uncertainty with the Strategic Choice Approach: the introduction of disruptive events

The Strategic Choice Approach (SCA) is a method meant to deal with operational decision in a strategic way and to manage different sources of uncertainty in decision-making processes. The paper describes how SCA can deal with the future in the specific realm of urban planning in current cities, which represents a typical example of Wicked Problem, taking into account the three different levels of uncertainties that the method aims to manage (Uncertainties about the working Environment, UE; Uncertainties about Related decisions, UR; Uncertainties about guiding Values, UV). We argue that these three types of uncertainties are referred to the ‘ordinary’ problems of modern and contemporary cities. The project of an architectural/urban transformation has to do with this kind uncertainties and implications – in overcoming a series of approvals of different institutional order – and, with this purpose, takes the form of a contract. Instead, this categorisation doesn’t conceive some new and uncertain challenges of future cities, around climate change, infrastructural disruption, insecurity, pandemics, at local and global scales, that are currently under debate in the cities. In this study we suggest that this character of uniqueness can imply the exploration of a new category of uncertainty in the SCA scheme, the ‘uncertainty about disruptive events (UD)’, a type of catastrophic or just unknown in their effects. First of all, we define the PSMs (Problem Structuring Methods) as methods of structuring the «wicked problems». Secondly, we examine the SCA as «a strategic choice process through time», taking into account the three different levels of uncertainty that the method intends to manage.Lo Strategic Choice Approach è un metodo ideato per affrontare le decisioni operative in modo strategico e per gestire diverse fonti di incertezza nel processo decisionale. Il paper descrive come lo SCA può occuparsi del futuro nel campo specifico della pianificazione urbana nelle città odierne, tenendo conto dei tre diversi livelli di incertezza che il metodo mira a gestire (Uncertainties about the working Environment, UE; Uncertainties about Related decisions, UR; Uncertainties about guiding Values, UV). Sosteniamo che questi tre tipi di incertezze si riferiscano ai problemi ‘ordinari’ delle città moderne e contemporanee. Il progetto di una trasformazione architettonica/urbana ha a che fare con questo tipo di incertezze e implicazioni - nel dover superare una serie di approvazioni di diversi ordini istituzionali – e, a tale scopo, assume la forma di un contratto. Tuttavia, questa categorizzazione non concepisce alcune nuove e incerte sfide delle città del futuro, riguardo cambiamenti climatici, le infrastrutture, l'insicurezza, le pandemie, a livello locale e globale, attualmente in discussione nelle città. In questo studio suggeriamo che questo carattere di unicità possa implicare l'esplorazione di una nuova categoria di incertezza nello schema di SCA, ‘uncertainty about disruptive events (UD)’, un tipo di eventi catastrofici o semplicemente sconosciuti nei loro effetti. Innanzitutto, definiamo i PSMs (Problem Structuring Methods) quali metodi di strutturare i «wicked problems». In secondo luogo, esaminiamo l’SCA come «un processo di scelta strategica nel tempo», tenendo conto dei tre diversi livelli di incertezza che il metodo intende gestire

The role of learning on industrial simulation design and analysis

The capability of modeling real-world system operations has turned simulation into an indispensable problemsolving methodology for business system design and analysis. Today, simulation supports decisions ranging from sourcing to operations to finance, starting at the strategic level and proceeding towards tactical and operational levels of decision-making. In such a dynamic setting, the practice of simulation goes beyond being a static problem-solving exercise and requires integration with learning. This article discusses the role of learning in simulation design and analysis motivated by the needs of industrial problems and describes how selected tools of statistical learning can be utilized for this purpose

2Planning for Contingencies: A Decision-based Approach

A fundamental assumption made by classical AI planners is that there is no uncertainty in the world: the planner has full knowledge of the conditions under which the plan will be executed and the outcome of every action is fully predictable. These planners cannot therefore construct contingency plans, i.e., plans in which different actions are performed in different circumstances. In this paper we discuss some issues that arise in the representation and construction of contingency plans and describe Cassandra, a partial-order contingency planner. Cassandra uses explicit decision-steps that enable the agent executing the plan to decide which plan branch to follow. The decision-steps in a plan result in subgoals to acquire knowledge, which are planned for in the same way as any other subgoals. Cassandra thus distinguishes the process of gathering information from the process of making decisions. The explicit representation of decisions in Cassandra allows a coherent approach to the problems of contingent planning, and provides a solid base for extensions such as the use of different decision-making procedures.Comment: See http://www.jair.org/ for any accompanying file

Optimal management of bio-based energy supply chains under parametric uncertainty through a data-driven decision-support framework

This paper addresses the optimal management of a multi-objective bio-based energy supply chain network subjected to multiple sources of uncertainty. The complexity to obtain an optimal solution using traditional uncertainty management methods dramatically increases with the number of uncertain factors considered. Such a complexity produces that, if tractable, the problem is solved after a large computational effort. Therefore, in this work a data-driven decision-making framework is proposed to address this issue. Such a framework exploits machine learning techniques to efficiently approximate the optimal management decisions considering a set of uncertain parameters that continuously influence the process behavior as an input. A design of computer experiments technique is used in order to combine these parameters and produce a matrix of representative information. These data are used to optimize the deterministic multi-objective bio-based energy network problem through conventional optimization methods, leading to a detailed (but elementary) map of the optimal management decisions based on the uncertain parameters. Afterwards, the detailed data-driven relations are described/identified using an Ordinary Kriging meta-model. The result exhibits a very high accuracy of the parametric meta-models for predicting the optimal decision variables in comparison with the traditional stochastic approach. Besides, and more importantly, a dramatic reduction of the computational effort required to obtain these optimal values in response to the change of the uncertain parameters is achieved. Thus the use of the proposed data-driven decision tool promotes a time-effective optimal decision making, which represents a step forward to use data-driven strategy in large-scale/complex industrial problems.Peer ReviewedPostprint (published version

A State of the Art of Governance Literature on adaptation to climate change. Towards a research agenda

This report provides a state-of-the-art overview of governance literature on adaptation strategies. What has recent research taught us on adaptation from the perspective of governance and to what research agenda does this lead? This report is structured as followed. Firstly, it will be argued why adaptation is a matter of governance. Secondly, the research methods for the literature study will be outlined. Thirdly, the results of the literature study will portray the findings in terms of the themes and foci with, respectively, environmental studies, spatial planning and development studies, and public administration studies. Finally, a comparative analysis of these findings will lead to a research agenda for future research on governance of adaptatio