{"ID":2825360,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2601.10725","arxiv_id":"2601.10725","title":"Multi-Agent Formation Navigation Using Diffusion-Based Trajectory Generation","abstract":"This paper introduces a diffusion-based planner for leader--follower formation control in cluttered environments. The diffusion policy is used to generate the trajectory of the midpoint of two leaders as a rigid bar in the plane, thereby defining their desired motion paths in a planar formation. While the followers track the leaders and form desired foramtion geometry using a distance-constrained formation controller based only on the relative positions in followers' local coordinates. The proposed approach produces smooth motions and low tracking errors, with most failures occurring in narrow obstacle-free space, or obstacle configurations that are not in the training data set. Simulation results demonstrate the potential of diffusion models for reliable multi-agent formation planning.","short_abstract":"This paper introduces a diffusion-based planner for leader--follower formation control in cluttered environments. The diffusion policy is used to generate the trajectory of the midpoint of two leaders as a rigid bar in the plane, thereby defining their desired motion paths in a planar formation. While the followers tra...","url_abs":"https://arxiv.org/abs/2601.10725","url_pdf":"https://arxiv.org/pdf/2601.10725v1","authors":"[\"Hieu Do Quang\",\"Chien Truong-Quoc\",\"Quoc Van Tran\"]","published":"2025-12-24T04:36:28Z","proceeding":"cs.RO","tasks":"[\"cs.RO\",\"math.OC\"]","methods":"[\"Diffusion Model\"]","has_code":false}