Optimal sampling paths for autonomous vehicles in uncertain ocean flows

Despite an extensive history of oceanic observation, researchers have only begun to build a complete picture of oceanic currents. Sparsity of instrumentation has created the need to maximize the information extracted from every source of data in building this picture. Within the last few decades, autonomous vehicles, or AVs, have been employed as tools to aid in this research initiative. Unmanned and self-propelled, AVs are capable of spending weeks, if not months, exploring and monitoring the oceans. However, the quality of data acquired by these vehicles is highly dependent on the paths along which they collect their observational data. The focus of this research is to find optimal sampling paths for autonomous vehicles, with the goal of building the most accurate estimate of a velocity field in the shortest time possible. The two main numerical tools employed in this work are the level set method for time-optimal path planning, and the Kalman filter for state estimation and uncertainty quantification. Specifically, the uncertainty associated with the velocity field is defined as the trace of the covariance matrix corresponding to the Kalman filter equations. The novelty in this work is the covariance tracking algorithm, which evolves this covariance matrix along the time-optimal trajectories defined by the level set method, and determines the path expected to minimize the uncertainty corresponding to the flow field by the end of deployment. While finding optimal sampling paths using this method is straightforward for the single-vehicle problem, it becomes increasingly difficult as the number of AVs grows. As such, an iterative procedure is presented here for multi-vehicle problems, which in simple cases can be proven to find controls that collectively minimizes the expected uncertainty, assuming that such a minimum exists. This work demonstrates the utility of combining methods from optimal control theory and estimation theory for learning uncertain fields using fleets of autonomous vehicles. Additionally, it shows that by optimizing over long-duration, continuous trajectories, superior results can be obtained when compared to ad hoc approaches such as a gradient-following control. This is demonstrated for both single-vehicle and multi-vehicle problems, and for static and evolving flow models

Human Motion Trajectory Prediction: A Survey

With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand and anticipate human behavior becomes increasingly important. Specifically, predicting future positions of dynamic agents and planning considering such predictions are key tasks for self-driving vehicles, service robots and advanced surveillance systems. This paper provides a survey of human motion trajectory prediction. We review, analyze and structure a large selection of work from different communities and propose a taxonomy that categorizes existing methods based on the motion modeling approach and level of contextual information used. We provide an overview of the existing datasets and performance metrics. We discuss limitations of the state of the art and outline directions for further research.Comment: Submitted to the International Journal of Robotics Research (IJRR), 37 page

A General Spatio-Temporal Clustering-Based Non-local Formulation for Multiscale Modeling of Compartmentalized Reservoirs

Representing the reservoir as a network of discrete compartments with neighbor and non-neighbor connections is a fast, yet accurate method for analyzing oil and gas reservoirs. Automatic and rapid detection of coarse-scale compartments with distinct static and dynamic properties is an integral part of such high-level reservoir analysis. In this work, we present a hybrid framework specific to reservoir analysis for an automatic detection of clusters in space using spatial and temporal field data, coupled with a physics-based multiscale modeling approach. In this work a novel hybrid approach is presented in which we couple a physics-based non-local modeling framework with data-driven clustering techniques to provide a fast and accurate multiscale modeling of compartmentalized reservoirs. This research also adds to the literature by presenting a comprehensive work on spatio-temporal clustering for reservoir studies applications that well considers the clustering complexities, the intrinsic sparse and noisy nature of the data, and the interpretability of the outcome. Keywords: Artificial Intelligence; Machine Learning; Spatio-Temporal Clustering; Physics-Based Data-Driven Formulation; Multiscale Modelin

Optimal trajectory generation in ocean flows

In this paper it is shown that Lagrangian Coherent Structures (LCS) are useful in determining near optimal trajectories for autonomous underwater gliders in a dynamic ocean environment. This opens the opportunity for optimal path planning of autonomous underwater vehicles by studying the global flow geometry via dynamical systems methods. Optimal glider paths were computed for a 2-dimensional kinematic model of an end-point glider problem. Numerical solutions to the optimal control problem were obtained using Nonlinear Trajectory Generation (NTG) software. The resulting solution is compared to corresponding results on LCS obtained using the Direct Lyapunov Exponent method. The velocity data used for these computations was obtained from measurements taken in August, 2000, by HF-Radar stations located around Monterey Bay, CA

Last planner and critical chain in construction management: comparative analysis

This paper endeavours to compare the Last Planner System of production control and the Critical Chain production management method. This comparison is carried out in the context of construction management. The original prescription and the evolution of the practice are examined regarding both approaches, and the similarities and differences are noted. Based on these considerations, gaps in the two approaches are identified and the potential of a synthesis of them is explored

A Rotating-Grid Upwind Fast Sweeping Scheme for a Class of Hamilton-Jacobi Equations

We present a fast sweeping method for a class of Hamilton-Jacobi equations that arise from time-independent problems in optimal control theory. The basic method in two dimensions uses a four point stencil and is extremely simple to implement. We test our basic method against Eikonal equations in different norms, and then suggest a general method for rotating the grid and using additional approximations to the derivatives in different directions in order to more accurately capture characteristic flow. We display the utility of our method by applying it to relevant problems from engineering