Multi-Agent Formation Navigation Using Diffusion-Based Trajectory Generation

• URL: http://arxiv.org/abs/2601.10725v1
• Date: Wed, 24 Dec 2025 04:36:28 GMT
• Title: Multi-Agent Formation Navigation Using Diffusion-Based Trajectory Generation
• Abstract summary: This paper introduces a diffusion-based planner for leader--follower formation control in cluttered environments.<n>The proposed approach produces smooth motions and low tracking errors, with most failures occurring in narrow obstacle-free space.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• AutoPath: Learning Transferable Goal-Conditioned Stochastic Path Prior for Safe Navigation Without Human Demonstrations [103.61864552260316]
  Real-time navigation in dynamic environments requires collision-free and dynamically feasible motion under limited perception.<n>We formulate navigation as learning a transferable goal-aligned path prior that models a reusable distribution over goal-aligned geometry-consistent local paths conditioned on local observations.<n>We develop a structured prior learning framework that parameterizes local paths using a geometry-aware polar action manifold and incorporates risk-sensitive utility shaping with multi-goal distributional rollouts for stable and safety-aware planning.
  arXiv  Detail & Related papers  (2026-07-13T16:00:41Z)
• Safer Trajectory Planning with CBF-guided Diffusion Model for Unmanned Aerial Vehicles [9.178828168133206]
  AeroTrajGen is a novel framework for diffusion-based trajectory generation for unmanned aerial vehicles.<n>It incorporates control barrier function (CBF)-guided sampling during inference, specifically designed for UAVs.<n>We show that CBF-guided sampling reduces collision rates by 94.7% compared to unguided diffusion baselines.
  arXiv  Detail & Related papers  (2026-04-19T16:41:16Z)
• RAD-2: Scaling Reinforcement Learning in a Generator-Discriminator Framework [49.531001563853984]
  RAD-2 is a unified generator-discriminator framework for closed-loop planning.<n>It produces diverse trajectory candidates, while an RL-optimized discriminator reranks these candidates according to their long-term driving quality.<n>It reduces the collision rate by 56% compared with strong diffusion-based planners.
  arXiv  Detail & Related papers  (2026-04-16T17:59:44Z)
• Amortizing Trajectory Diffusion with Keyed Drift Fields [2.5352713493505785]
  Keyed Drifting Policies is a one-step trajectory generator trained with a drift-field objective.<n>It repels generated trajectories toward condition-matched dataset windows.<n>It achieves strong performance with one-step inference and substantially lower planning latency than diffusion sampling.
  arXiv  Detail & Related papers  (2026-03-14T17:56:15Z)
• FlowDrive: moderated flow matching with data balancing for trajectory planning [5.553127690929986]
  FlowDrive is a flow-matching trajectory planner that learns a conditional rectified flow to map noise directly to trajectory distributions.<n>FlowDrive achieves state-of-the-art results among learning-based planners and approaches methods with rule-based refinements.
  arXiv  Detail & Related papers  (2025-09-26T06:49:22Z)
• Directional Motional Control via Engineered Conical Intersections in Trapped Rydberg Ions [0.0]
  We demonstrate coherent control of motional dynamics in trapped Rydberg ions engineered to exhibit a conical intersection between adiabatic potential-energy surfaces.<n>This work demonstrates that engineered conical intersections can serve as a new control resource for directional motional dynamics.
  arXiv  Detail & Related papers  (2025-09-14T16:53:25Z)
• Multi-Agent Path Finding in Continuous Spaces with Projected Diffusion Models [57.45019514036948]
  Multi-Agent Path Finding (MAPF) is a fundamental problem in robotics.<n>This work proposes a novel approach that integrates constrained optimization with diffusion models for MAPF in continuous spaces.
  arXiv  Detail & Related papers  (2024-12-23T21:27:19Z)
• MGF: Mixed Gaussian Flow for Diverse Trajectory Prediction [72.70572835589158]
  We propose constructing a mixed Gaussian prior for a normalizing flow model for trajectory prediction.<n>Our method achieves state-of-the-art performance in the evaluation of both trajectory alignment and diversity on the popular UCY/ETH and SDD datasets.
  arXiv  Detail & Related papers  (2024-02-19T15:48:55Z)
• Diffusion-ES: Gradient-free Planning with Diffusion for Autonomous Driving and Zero-Shot Instruction Following [21.81411085058986]
  Reward-gradient guided denoising generates trajectories that maximize both a differentiable reward function and the likelihood under the data distribution captured by a diffusion model. We propose DiffusionES, a method that combines gradient-free optimization with trajectory denoising. We show that DiffusionES achieves state-of-the-art performance on nuPlan, an established closed-loop planning benchmark for autonomous driving.
  arXiv  Detail & Related papers  (2024-02-09T17:18:33Z)
• Unsupervised Discovery of Interpretable Directions in h-space of Pre-trained Diffusion Models [63.1637853118899]
  We propose the first unsupervised and learning-based method to identify interpretable directions in h-space of pre-trained diffusion models. We employ a shift control module that works on h-space of pre-trained diffusion models to manipulate a sample into a shifted version of itself. By jointly optimizing them, the model will spontaneously discover disentangled and interpretable directions.
  arXiv  Detail & Related papers  (2023-10-15T18:44:30Z)
• Trace and Pace: Controllable Pedestrian Animation via Guided Trajectory Diffusion [83.88829943619656]
  We introduce a method for generating realistic pedestrian trajectories and full-body animations that can be controlled to meet user-defined goals. Our guided diffusion model allows users to constrain trajectories through target waypoints, speed, and specified social groups. We propose utilizing the value function learned during RL training of the animation controller to guide diffusion to produce trajectories better suited for particular scenarios.
  arXiv  Detail & Related papers  (2023-04-04T15:46:42Z)
• Haar Wavelet based Block Autoregressive Flows for Trajectories [129.37479472754083]
  Prediction of trajectories such as that of pedestrians is crucial to the performance of autonomous agents. We introduce a novel Haar wavelet based block autoregressive model leveraging split couplings. We illustrate the advantages of our approach for generating diverse and accurate trajectories on two real-world datasets.
  arXiv  Detail & Related papers  (2020-09-21T13:57:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.