Abstract
A multiobjective, risk-aware framework is developed for optimal path planning of autonomous underwater vehicles operating in uncertain current. The uncertainty in the current is described in terms of a finite ensemble of flow realizations. A new optimization framework is proposed that accounts for the full variability of the ensemble in a single optimization problem whose solution may not necessarily coincide with any of the optimal deterministic paths corresponding to individual ensemble members. We formulate stochastic problemsthat aim to minimize a risk measure of the travel time or energy consumption, using a flexible methodology that enables the user to seamlessly explore various objectives, ranging from risk neutral to risk averse. We illustrate the application of the proposed approach using two case studies based on synthetic 2-D settings, and one case involving a real-world problem in the Gulf of Aden. The results are analyzed to assess the value of stochastic solution, guide the selection of suitable risk measures, and demonstrate the impact of the risk measures on the resulting path and on the distribution of travel times.
Highlights
A UTONOMOUS underwater vehicles (AUVs) are being employed for various applications, including collecting information on coastal ecosystems or underwater installations, search and rescue operations, inspection of intake and discharge of thermal plants, and management of shipping operations [1]–[3]
The simplification is based on optimizing the path based on individual realizations of the flow, evaluating the resulting deterministic solutions based on the other members of the ensemble, and selecting the optimal path based on the probabilistic criterion or risk measure
We start by introducing the deterministic version of a 2-D trajectory planning problem before; we extend it to a stochastic programming problem to accommodate an uncertain flow field and minimize a risk measure of the travel time or energy consumption
Summary
A UTONOMOUS underwater vehicles (AUVs) are being employed for various applications, including collecting information on coastal ecosystems or underwater installations, search and rescue operations, inspection of intake and discharge of thermal plants, and management of shipping operations [1]–[3]. The simplification is based on optimizing the path based on individual realizations of the flow, evaluating the resulting deterministic solutions based on the other members of the ensemble, and selecting the optimal path based on the probabilistic criterion or risk measure This approach results in methods that are robust, but because they rely on an ensemble of paths determined for individual realizations of the current field, they may not result in an optimal solution. A key feature of the proposed optimization models is that the solution may not necessarily coincide with any of the deterministic paths corresponding to individual ensemble members, which contrasts with the type of solutions obtained in [26]–[28] To this end, we start by introducing a nonlinear deterministic 2-D path planning problem for steady and unsteady ocean current fields.
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