Robot trajectory generation method and system based on diffusion and rule inference

Abstract

The application provides a robot trajectory generation method and system based on diffusion and rule inference, and relates to the technical field of autonomous navigation.The application proposes a novel conditional diffusion trajectory generation model based on architecture induction bias, which is not only dependent on loss function for result constraint, but also divides the complex trajectory generation task into two mutually coordinated technical processes:1, based on local environment graph construction, graph convolution propagation and graph level feature convergence, the inference of the overall passable structure and obstacle topological relationship within the scope of the current local environment graph is realized;2, based on node level scene representation, target feature continuous injection and prediction step by step context update, the fine-grained local maneuvering control of obstacle avoidance, turning and end convergence behavior at each future waypoint is realized, and the high-fidelity generation of the trajectory in the complex unstructured environment is ensured.Meanwhile, a GTGRU is designed, the end drift problem is solved by introducing time attention bias.

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