Real-Time Navigation for Autonomous Surface Vehicles In Ice-Covered Waters

Vessel transit in ice-covered waters poses unique challenges in safe and efficient motion planning. When the concentration of ice is high, it may not be possible to find collision-free trajectories. Instead, ice can be pushed out of the way if it is small or if contact occurs near the edge of the ice. In this work, we propose a real-time navigation framework that minimizes collisions with ice and distance travelled by the vessel. We exploit a lattice-based planner with a cost that captures the ship interaction with ice. To address the dynamic nature of the environment, we plan motion in a receding horizon manner based on updated vessel and ice state information. Further, we present a novel planning heuristic for evaluating the cost-to-go, which is applicable to navigation in a channel without a fixed goal location. The performance of our planner is evaluated across several levels of ice concentration both in simulated and in real-world experiments.Comment: 7 pages, 8 figure

Transportation Mission-Based Optimization of Heavy Combination Road Vehicles and Distributed Propulsion, Including Predictive Energy and Motion Control

This thesis proposes methodologies to improve heavy vehicle design by reducing the total cost of ownership and by increasing energy efficiency and safety.Environmental issues, consumers expectations and the growing demand for freight transport have created a competitive environment in providing better transportation solutions. In this thesis, it is proposed that freight vehicles can be designed in a more cost- and energy-efficient manner if they are customized for narrow ranges of operational domains and transportation use-cases. For this purpose, optimization-based methods were applied to minimize the total cost of ownership and to deliver customized vehicles with tailored propulsion components that best fit the given transportation missions and operational environment. Optimization-based design of the vehicle components was found to be effective due to the simultaneous consideration of the optimization of the transportation mission infrastructure, including charging stations, loading-unloading, routing and fleet composition and size, especially in case of electrified propulsion. Implementing integrated vehicle hardware-transportation optimization could reduce the total cost of ownership by up to 35% in the case of battery electric heavy vehicles. Furthermore, in this thesis, the impacts of two future technological advancements, i.e., heavy vehicle electrification and automation, on road freight transport were discussed. It was shown that automation helps the adoption of battery electric heavy vehicles in freight transport. Moreover, the optimizations and simulations produced a large quantity of data that can help users to select the best vehicle in terms of the size, propulsion system, and driving system for a given transportation assignment. The results of the optimizations revealed that battery electric and hybrid heavy combination vehicles exhibit the lowest total cost of ownership in certain transportation scenarios. In these vehicles, propulsion can be distributed over different axles of different units, thus the front units may be pushed by the rear units. Therefore, online optimal energy management strategies were proposed in this thesis to optimally control the vehicle motion and propulsion in terms of the minimum energy usage and lateral stability. These involved detailed multitrailer vehicle modeling and the design and solution of nonlinear optimal control problems

From Indicators to Action: Evaluating the Usefulness of Indicators to Move from Regional Climate Change Assessment to Local Adaptation Implementation

As the effects of climate change become increasingly damaging and costly, a public and political consensus is building for planning that will protect private property and public infrastructure. Climate-related planning has primarily focused on mitigation, assessing vulnerability, and building adaptive capacity. Adaptation has not gained substantial ground in the area of implementation. The uncertainty associated with climate change projection and variability has emerged as a dominant barrier to adaptation. However, as knowledge accrues, the global and national science communities have been developing more detailed, fine-scale climate projections. Regional climate assessments are available for the sub-national climate regions in the U.S., and have been created based on the measurement of many components of climate, often referred to as indicators. This thesis evaluates the use of those and other indicators as adaptation decision support tools. Findings suggest that indicators can be effectively integrated into a step-wise, risk-based adaptation planning process to overcome barriers to adaptation, many of which contain concern over climate change uncertainty at their core. The combination of climate science data and information about the local experience of climate change are found to be key to the effective use of indicators in adaptation, as is the direct integration of indicators into the policy-making process. Ideally, these indicators can be used to inform trigger points for phases in a flexible adaptation approach, but more work is needed to develop methods for managing the risks and costs associated with adaptation