Path planning in uncertain flow fields using ensemble method

Wang, Tong; Maître, Olivier Le; Hoteit, Ibrahim

doi:10.1007/s10236-016-0979-2

Cited by 23 publications

(12 citation statements)

References 57 publications

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“…Uncertainties in the ocean are usually incorporated by Monte Carlo methods where the deterministic plan is computed for each ensemble member, hence, leading to the statistics of optimal paths (e.g. Wang et al 2016). Markov decision process with several variations have also been employed to account for ocean uncertainty (Hollinger et al 2016).…”

Section: A Reachability and Path Panningmentioning

confidence: 99%

A future for intelligent autonomous ocean observing systems

Lermusiaux¹,

Subramani²,

Lin³

et al. 2017

j mar res

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Ocean scientists have dreamed of and recently started to realize an ocean observing revolution with autonomous observing platforms and sensors. Critical questions to be answered by such autonomous systems are where, when, and what to sample for optimal information, and how to optimally reach the sampling locations. Definitions, concepts, and progress towards answering these questions using quantitative predictions and fundamental principles are presented. Results in reachability and path planning, adaptive sampling, machine learning, and teaming machines with scientists are overviewed. The integrated use of differential equations and theory from varied disciplines is emphasized. The results provide an inference engine and knowledge base for expert autonomous observing systems. They are showcased using a set of recent at-sea campaigns and realistic simulations. Real-time experiments with identical autonomous underwater vehicles (AUVs) in the Buzzards Bay and Vineyard Sound region first show that our predicted time-optimal paths were faster than shortest distance paths. Deterministic and probabilistic reachability and path forecasts issued and validated for gliders and floats in the northern Arabian Sea are then presented. Novel Bayesian adaptive sampling for hypothesis testing and optimal learning are finally shown to forecast the observations most informative to estimate the accuracy of model formulations, the values of ecosystem parameters and dynamic fields, and the presence of Lagrangian Coherent Structures.

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Section: A Reachability and Path Panningmentioning

confidence: 99%

A future for intelligent autonomous ocean observing systems

Lermusiaux¹,

Subramani²,

Lin³

et al. 2017

j mar res

View full text Add to dashboard Cite

show abstract

“…If the UAV is flying in between two waypoints w i and w i+1 and is of track to the side by distance e N upwards or downwards from the defined trajectory, the vectorial field attracts the UAV back to the path. More away from the path UAV is stronger vector field is (Wang et al 2016).…”

Section: Principles Of Uav Autonomous Guidancementioning

confidence: 99%

“…Previously calculated flight path error is provided for the path manager block. Flight path error value is used to calculate new UAV heading, which is used to correct and compensate flight path error (Nelson et al 2006;Wang et al 2016). Furthermore, this block keeps the information of which waypoint-switching algorithm is used and needs an input about the planned flight mission with all of the coordinates or waypoints.…”

Section: Autonomous Flight Mission Execution Model Of the Uavmentioning

confidence: 99%

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Autonomous Unmanned Aerial Vehicle Flight Accuracy Evaluation for Three Different Path-Tracking Algorithms

2019

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This paper shows mathematical results of three methods, which can be used for Unmanned Aerial Vehicle (UAV) to make transition from one flight leg to another. In paper, we present general equations, which can be used for generating waypoint-switching methods when for experiment purpose mathematical UAV model is used. UAV is modelled as moving dot, which eliminates all of the aerodynamics factors and we can concentrate only on the navigation problems. Lots of attention is dedicated to show possible flight path error values with representation of modelled flight path trajectories and deviations from the flight mission path. All of the modelled flight missions are done in two-dimensional space and all the results are evaluated by looking at Probability Density Function (PDF) values, as we are mostly interested in the probability of the error.

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“…Another line of work has used model predictive control and optimal control (Morgan et al, 2014;Baumgartner et al, 2009;Zeng et al, 2014), and evolutionary algorithms (Alvarez et al, 2004). Risk-aware path planning methodologies have also been developed by considering the uncertainty in the ocean current predictions (Pereira et al, 2013;Wang et al, 2016). The trajectory planning of AUVs via mathematical programming methods has been less reported.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

Trajectory planning for autonomous underwater vehicles in the presence of obstacles and a nonlinear flow field using mixed integer nonlinear programming

Wang

Lima

Giraldi

et al. 2019

Computers & Operations Research

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This paper addresses the time-optimal trajectory planning for autonomous underwater vehicles. A detailed mixed integer nonlinear programming (MINLP) model is presented, explicitly taking into account vehicle kinematic constraints, obstacle avoidance, and a nonlinear flow field to represent the ocean current. MINLP problems pose great challenges because of the combinatorial complexity and nonconvexities introduced by the nature of the flow field. A novel solution approach in an optimization framework is developed to address associated difficulties. The main benefit of the proposed methodology is the ability to find multiple local minima. The contribution of the paper is fourfold: 1) a novel approach to integrate the flow field into the MINLP model; 2) a diversified initialization strategy using multiple waypoints, different solvers and approximated models, namely, a mixed integer linear programming model and the MINLP model with and without the flow field; 3) an algorithm that forces the solver to seek improved solutions; and 4) a parallel computing approach capitalizing on diversified initialization. The performance of the resulting methodology is illustrated on idealized case studies, and the results are used to gain insight into trajectory planning in the presence of flow fields.

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Path planning in uncertain flow fields using ensemble method

Cited by 23 publications

References 57 publications

A future for intelligent autonomous ocean observing systems

A future for intelligent autonomous ocean observing systems

Autonomous Unmanned Aerial Vehicle Flight Accuracy Evaluation for Three Different Path-Tracking Algorithms

Trajectory planning for autonomous underwater vehicles in the presence of obstacles and a nonlinear flow field using mixed integer nonlinear programming

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