Optimal trajectory generation for a glider in time-varying 2D ocean flows B-spline model

Zhang, Weizhong; Inane, T.; Ober‐Blöbaum, Sina; Marsden, J.

doi:10.1109/robot.2008.4543348

Cited by 22 publications

(6 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Other planners employ optimization methods such as nonlinear programming (Inanc, Shadden, and Marsden 2005;Zhang et al 2008), direct (Kruger et al 2007) or iterative nonlinear optimization (Jones and Hollinger 2017), and evolutionary algorithms (Alvarez, Caiti, and Onken 2004;Aghababa 2012) to generate time-and energy optimal paths that minimize a cost function. Here, the computational cost grows exponentially for classic deterministic optimization and a solution is not guaranteed in evolutionary algorithms.…”

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

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.

show abstract

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

“…These models assume that the sensor can generate its own flow relative velocity u(t) = [u x , u y ] ∈ R 2 in addition to the flow-induced velocity. Alternatively, models that incorporate inertial effects can also be used [18], [44], [52]. The function v (x(t), t) is the unsteady flow field within which the agent is moving.…”

Section: A Control Finite Time Lyapunov Exponents (Cftle)mentioning

confidence: 99%

Finite Time Lyapunov Exponent Analysis of Model Predictive Control and Reinforcement Learning

Krishna,

Brunton,

Song

2023

IEEE Access

View full text Add to dashboard Cite

Finite-time Lyapunov exponents (FTLEs) provide a powerful approach to compute timevarying analogs of invariant manifolds in unsteady fluid flow fields. These manifolds are useful to visualize the transport mechanisms of passive tracers advecting with the flow. However, many vehicles and mobile sensors are not passive, but are instead actuated according to some intelligent trajectory planning or control law; for example, model predictive control and reinforcement learning are often used to design energyefficient trajectories in a dynamically changing background flow. In this work, we investigate the use of FTLE on such controlled agents to gain insight into optimal transport routes for navigation in known unsteady flows. We find that these controlled FTLE (cFTLE) coherent structures separate the flow field into different regions with similar costs of transport to the goal location. These separatrices are functions of the planning algorithm's hyper-parameters, such as the optimization time horizon and the cost of actuation. Computing the invariant sets and manifolds of active agent dynamics in dynamic flow fields is useful in the context of robust motion control, hyperparameter tuning, and determining safe and collision-free trajectories for autonomous systems. Moreover, these cFTLE structures provide insight into effective deployment locations for mobile agents with actuation and energy constraints to traverse the ocean or atmosphere.INDEX TERMS optimal control, finite-time Lyapunov exponents, path planning, mobile sensors, dynamical systems, unsteady fluid dynamics, model predictive control, reinforcement learning

show abstract

“…A potential-field-based method in conjunction with the virtual force concept to maneuver AUV in an unknown environment has been illustrated by Ding et al [71] , that resolved the local minima problem in PFA based path following. Zhu et al [72] presented an integrated AUV PP algorithm by incorporating "velocity synthesis (VS)" and "artificial potential field (APF)" methods together. An improvised APF algorithm has been used for obstacle avoidance and an optimized path has been generated using VS method.…”

Section: Potential-field Algorithm (Pfa)mentioning

confidence: 99%

“…•Provides safe and reliable path for slow moving AUVs in the coastline •Computational complexity is high Predictable Metaheuristic algorithms [53][54][55][56][57][58] Energy optimal Poor Low •Searches the solution from a large solution space •Requires effective memory management Predictable MCDA [59] Time optimal Poor Low •Provides time optimized path •Computational complexity is high Unpredictable Graphical method [60][61][62][63][64][65][66] [71,72] Time optimal Poor Low…”

Section: Achieved Lowmentioning

confidence: 99%

A Comprehensive Review of Path Planning Algorithms for Autonomous Underwater Vehicles

Panda

Subudhi

Pati

2020

Int. J. Autom. Comput.

148

View full text Add to dashboard Cite

The underwater path planning problem deals with finding an optimal or sub-optimal route between an origin point and a termination point in marine environments. The underwater environment is still considered as a great challenge for the path planning of autonomous underwater vehicles (AUVs) because of its hostile and dynamic nature. The major constraints for path planning are limited data transmission capability, power and sensing technology available for underwater operations. The sea environment is subjected to a large set of challenging factors classified as atmospheric, coastal and gravitational. Based on whether the impact of these factors can be approximated or not, the underwater environment can be characterized as predictable and unpredictable respectively. The classical path planning algorithms based on artificial intelligence assume that environmental conditions are known apriori to the path planner. But the current path planning algorithms involve continual interaction with the environment considering the environment as dynamic and its effect cannot be predicted. Path planning is necessary for many applications involving AUVs. These are based upon planning safety routes with minimum energy cost and computation overheads. This review is intended to summarize various path planning strategies for AUVs on the basis of characterization of underwater environments as predictable and unpredictable. The algorithms employed in path planning of single AUV and multiple AUVs are reviewed in the light of predictable and unpredictable environments.

show abstract

Optimal trajectory generation for a glider in time-varying 2D ocean flows B-spline model

Cited by 22 publications

References 10 publications

A future for intelligent autonomous ocean observing systems

A future for intelligent autonomous ocean observing systems

Finite Time Lyapunov Exponent Analysis of Model Predictive Control and Reinforcement Learning

A Comprehensive Review of Path Planning Algorithms for Autonomous Underwater Vehicles

Contact Info

Product

Resources

About