3DMaxConvNet uses a convolutional autoencoder to predict 3D magnetic potential fields from voxel grids, achieving 100% success in 100 closed-loop trials across two urban simulations while planning paths 1.7-200 times faster than A* and RRT*.
Sampling-based Algorithms for Optimal Motion Planning
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Safe Aerial 3D Path Planning for Autonomous UAVs using Magnetic Potential Fields
3DMaxConvNet uses a convolutional autoencoder to predict 3D magnetic potential fields from voxel grids, achieving 100% success in 100 closed-loop trials across two urban simulations while planning paths 1.7-200 times faster than A* and RRT*.