QSNN agent in Q-SpiRL framework achieves up to 99% success rate with efficient paths in 20x20 to 40x40 grid worlds with static and dynamic obstacles, outperforming tabular Q-learning, MLP, SNN, and QMLP baselines under unified evaluation.
Deep reinforcement learning based mobile robot navigation: A review,
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A hybrid RL-DWA controller achieves high deformation and near-perfect path completion for deformable microrobots navigating simulated 3D vascular networks from sparse point clouds.
A hybrid navigation system uses offline HJ reachability computations as heuristics and safety constraints within graph search to achieve faster and safer robot movement in complex indoor environments.
citing papers explorer
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Q-SpiRL: Quantum Spiking Reinforcement Learning for Adaptive Robot Navigation
QSNN agent in Q-SpiRL framework achieves up to 99% success rate with efficient paths in 20x20 to 40x40 grid worlds with static and dynamic obstacles, outperforming tabular Q-learning, MLP, SNN, and QMLP baselines under unified evaluation.
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3D RL-DWA: A Hybrid Reinforcement Learning and Dynamic Window Approach for Goal-Directed Local Navigation in Multi-DoF Robots
A hybrid RL-DWA controller achieves high deformation and near-perfect path completion for deformable microrobots navigating simulated 3D vascular networks from sparse point clouds.
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A Hamilton-Jacobi Reachability-Guided Search Framework for Efficient and Safe Indoor Planar Robot Navigation
A hybrid navigation system uses offline HJ reachability computations as heuristics and safety constraints within graph search to achieve faster and safer robot movement in complex indoor environments.