
Zermelo's problem: Optimal pointtopoint navigation in 2D turbulent flows using Reinforcement Learning
To find the path that minimizes the time to navigate between two given p...
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Deep Reinforcement Learning for Time Optimal Velocity Control using Prior Knowledge
While autonomous navigation has recently gained great interest in the fi...
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Optimal control of pointtopoint navigation in turbulent timedependent flows using Reinforcement Learning
We present theoretical and numerical results concerning the problem to f...
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Optimal strategies for the control of autonomous vehicles in data assimilation
We propose a method to compute optimal control paths for autonomous vehi...
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FiniteHorizon, EnergyOptimal Trajectories in Unsteady Flows
Intelligent mobile sensors, such as uninhabited aerial or underwater veh...
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RRTCoLearn: towards kinodynamic planning without numerical trajectory optimization
Samplingbased kinodynamic planners, such as Rapidlyexploring Random Tr...
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Reinforcement Learning for Robotic Timeoptimal Path Tracking Using Prior Knowledge
Timeoptimal path tracking, as a significant tool for industrial robots,...
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Learning Efficient Navigation in Vortical Flow Fields
Efficient pointtopoint navigation in the presence of a background flow field is important for robotic applications such as ocean surveying. In such applications, robots may only have knowledge of their immediate surroundings or be faced with timevarying currents, which limits the use of optimal control techniques for planning trajectories. Here, we apply a novel Reinforcement Learning algorithm to discover timeefficient navigation policies to steer a fixedspeed swimmer through an unsteady twodimensional flow field. The algorithm entails inputting environmental cues into a deep neural network that determines the swimmer's actions, and deploying Remember and Forget Experience replay. We find that the resulting swimmers successfully exploit the background flow to reach the target, but that this success depends on the type of sensed environmental cue. Surprisingly, a velocity sensing approach outperformed a biomimetic vorticity sensing approach by nearly twofold in success rate. Equipped with local velocity measurements, the reinforcement learning algorithm achieved near 100 the timeefficiency of paths found by a global optimal control planner.
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