Trajectory Manifold Optimization for Fast and Adaptive Kinodynamic Motion Planning

• URL: http://arxiv.org/abs/2410.12193v1
• Date: Wed, 16 Oct 2024 03:29:33 GMT
• Title: Trajectory Manifold Optimization for Fast and Adaptive Kinodynamic Motion Planning
• Authors: Yonghyeon Lee,
• Abstract summary: Fast kinodynamic motion planning is crucial for systems to adapt to dynamically changing environments. We propose a novel neural network model, it Differentiable Motion Manifold Primitives (DMMP), along with a practical training strategy. Experiments with a 7-DoF robot arm tasked with dynamic throwing to arbitrary target positions demonstrate that our method surpasses existing approaches in planning speed, task success, and constraint satisfaction.
• Score: 5.982922468400902
• License:
• Abstract: Fast kinodynamic motion planning is crucial for systems to effectively adapt to dynamically changing environments. Despite some efforts, existing approaches still struggle with rapid planning in high-dimensional, complex problems. Not surprisingly, the primary challenge arises from the high-dimensionality of the search space, specifically the trajectory space. We address this issue with a two-step method: initially, we identify a lower-dimensional trajectory manifold {\it offline}, comprising diverse trajectories specifically relevant to the task at hand while meeting kinodynamic constraints. Subsequently, we search for solutions within this manifold {\it online}, significantly enhancing the planning speed. To encode and generate a manifold of continuous-time, differentiable trajectories, we propose a novel neural network model, {\it Differentiable Motion Manifold Primitives (DMMP)}, along with a practical training strategy. Experiments with a 7-DoF robot arm tasked with dynamic throwing to arbitrary target positions demonstrate that our method surpasses existing approaches in planning speed, task success, and constraint satisfaction.

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