Deriving analytical backpropagation equations for EKF covariance gradients enables perception-aware optimal motion planning.
Learning-based NNPP model reduces search time for optimal paths on planetary surfaces.
Efficient path planning for autonomous robots in dynamic environments is achieved by considering environmental flow fields and obstacles, optimizing both time and energy simultaneously.
Introducing a novel PF-DDQN method for multi-AGV path planning, enhancing efficiency and outperforming traditional DDQN algorithms.