Fast End-to-End Generation of Belief Space Paths for Minimum Sensing Navigation
This addresses the problem of efficient motion planning for robotics or autonomous systems, but it is incremental as it builds on existing planners and deep learning architectures.
The paper tackles the high computational cost of sampling-based motion planning in Gaussian belief space by proposing a deep learning approach using a U-Net to predict optimal path candidates from problem descriptions, significantly reducing computation time compared to baseline methods.
We revisit the problem of motion planning in the Gaussian belief space. Motivated by the fact that most existing sampling-based planners suffer from high computational costs due to the high-dimensional nature of the problem, we propose an approach that leverages a deep learning model to predict optimal path candidates directly from the problem description. Our proposed approach consists of three steps. First, we prepare a training dataset comprising a large number of input-output pairs: the input image encodes the problem to be solved (e.g., start states, goal states, and obstacle locations), whereas the output image encodes the solution (i.e., the ground truth of the shortest path). Any existing planner can be used to generate this training dataset. Next, we leverage the U-Net architecture to learn the dependencies between the input and output data. Finally, a trained U-Net model is applied to a new problem encoded as an input image. From the U-Net's output image, which is interpreted as a distribution of paths,an optimal path candidate is reconstructed. The proposed method significantly reduces computation time compared to the sampling-based baseline algorithm.