NaviFormer uses a Transformer architecture inside deep reinforcement learning to jointly predict high-level routes and low-level collision-free trajectories for holistic navigation.
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A multi-stage planner sequences waypoints, predicts visibility-maximizing SAR flight segments with a DRL-trained neural network, and connects them via 3D Dubins curve optimization for multi-target surveillance.
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NaviFormer: A Deep Reinforcement Learning Transformer-like Model to Holistically Solve the Navigation Problem
NaviFormer uses a Transformer architecture inside deep reinforcement learning to jointly predict high-level routes and low-level collision-free trajectories for holistic navigation.
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Multi-stage Planning for Multi-target Surveillance using Aircrafts Equipped with Synthetic Aperture Radars Aware of Target Visibility
A multi-stage planner sequences waypoints, predicts visibility-maximizing SAR flight segments with a DRL-trained neural network, and connects them via 3D Dubins curve optimization for multi-target surveillance.