Long-distance migration with minimal energy consumption in a thermal turbulent environment

Xu, Ao; Wu, Hua-Lin; Xi, Heng-Dong

doi:10.1103/physrevfluids.8.023502

Cited by 5 publications

(1 citation statement)

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“…Algorithms inspired from computational fluid dynamics have also been used to explore coordinated control of swarms in flow fields [7], [8], [34]- [36]. Recent developments have made use of model predictive control (MPC) [37] and reinforcement learning (RL) algorithms [38]- [42] to find optimal paths in unsteady flow fields. Optimal control, including MPC, has been previously related to the passive FTLE in the past [43]- [46].…”

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confidence: 99%

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

Krishna,

Brunton,

Song

2023

IEEE Access

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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

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