Distributed Stochastic Optimal Control for Pattern-Oriented Swarms

• URL: http://arxiv.org/abs/2609.12959v2
• Date: Mon, 14 Sep 2026 01:42:48 GMT
• Title: Distributed Stochastic Optimal Control for Pattern-Oriented Swarms
• Abstract summary: pattern-oriented swarms in control, self-rotor, and safe environments offer significant promise for diverse applications.<n>In this paper, we present challenges in optimal distributed control framework to address these challenges within a unified probabilistic architecture.
• Score: 7.5152482124458695
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While offering significant promise for diverse applications, pattern-oriented swarms encounter multifaceted challenges in geometric control, self-organization, and safe navigation through dynamic environments. In this paper, we present a GRF-based stochastic optimal control framework to address these challenges within a unified probabilistic architecture. By extending the GRF into the temporal domain, the proposed framework casts collective coordination as a Bayesian inference task, enabling swarms to accommodate environmental uncertainty, satisfy non-convex constraints, and reconcile heterogeneous dynamics across diverse platforms. We develop an uncertainty- and safety-aware collision avoidance module for navigation in the presence of stochastic obstacle motion. The unscented transform is employed to propagate state uncertainty for both dynamic obstacles and neighboring agents, yielding principled confidence bounds for collision avoidance. In addition, density-guided pattern control is introduced, which encodes geometric patterns as implicit density fields. This representation decouples pattern specification from explicit agent-to-target assignments, thereby facilitating intrinsic self-healing and elastic reconfiguration in a distributed manner. The proposed framework is extensively evaluated through Monte Carlo simulations across diverse scenarios. Its model-agnostic nature is demonstrated on both quadrotor and fixed-wing UAV swarms, highlighting its generalizability across platforms with heterogeneous dynamics. Finally, the efficacy and robustness of the proposed method are validated through indoor experiments with a 15-quadrotor swarm and outdoor deployments involving 4 custom-built autonomous quadrotors. These experiments substantiate the proposed framework's capacity to maintain reliable geometric pattern transitions and safety-aware navigation within real-world environments.

Related papers

• A Forward-Inverse Dynamic Game Framework for Enhanced Multi-Agent Trajectory Planning [10.027004863448854]
  We propose a KL-regularized dynamic game with a state-dependent weight that adaptively balances optimality and behavioral priors.<n>We establish per-it well-posedness of the regularized local game and show that the adaptive weighting function remains Lipschitz continuous under bounded nominal-trajectory updates.
  arXiv  Detail & Related papers  (2026-08-03T03:15:37Z)
• Hierarchical Consistency Learning for Test-time Adaptation in Camouflage Perception [50.278200968044665]
  Camouflaged object detection (COD) aims to localize targets that exhibit minimal perceptual differences from backgrounds through physical attributes.<n>Existing methods, constrained by the static train-then-freeze paradigm, suffer from domain rigidity and dependency annotation.<n>We propose the hierarchical consistency learning framework, which integrates test-time adaptation for dynamic representation recalibration.
  arXiv  Detail & Related papers  (2026-05-25T09:57:46Z)
• Provable Robustness against Backdoor Attacks via the Primal-Dual Perspective on Differential Privacy [51.758416625168]
  Randomized smoothing is a powerful tool for certifying robustness to adversarial perturbations.<n>We propose a framework for certification of complex, composed mechanisms.<n>We provide a principled and general framework for using composite mechanisms to certify robustness under complex threat models.
  arXiv  Detail & Related papers  (2026-05-20T22:17:29Z)
• Surviving the Unseen: Predictive Defense for Novel Multi-Turn Multimodal Attacks [0.0]
  This paper formulates safety verification as a dynamic survival prediction and trajectory dynamics problem.<n>The Triple-tier Anomaly Defense (TRIAD) framework is proposed as a predictive model that maps multimodal and multi-turn conversational flow as a continuous trajectory.
  arXiv  Detail & Related papers  (2026-05-18T18:06:20Z)
• Anticipatory Reinforcement Learning: From Generative Path-Laws to Distributional Value Functions [0.0]
  This paper introduces Anticipatory Reinforcement Learning (ARL), a novel framework designed to bridge the gap between non-Markovian decision processes and classical reinforcement learning architectures.<n>In environments characterised by jump-diffusions and structural breaks, traditional state-based methods often fail to capture the essential path-dependent geometry required for accurate foresight.
  arXiv  Detail & Related papers  (2026-04-06T13:15:44Z)
• Gaussian Joint Embeddings For Self-Supervised Representation Learning [0.0]
  Self-parametric representation learning often relies on deterministic predictive architectures to align context and target views in latent space.<n>We propose a probabilistic alternative based on generative joint modeling.
  arXiv  Detail & Related papers  (2026-03-26T00:54:54Z)
• Distributionally Robust Control with End-to-End Statistically Guaranteed Metric Learning [5.309590159815129]
  We propose a novel end-to-end finite-horizon Wasserstein DRC framework.<n>It integrates the learning of anisotropic Wasserstein metrics with downstream control tasks in a closed-loop manner.<n>We show that the proposed framework achieves superior closed-loop performance and robustness compared with state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-10-11T13:40:49Z)
• Robust Optimization with Diffusion Models for Green Security [49.68562792424776]
  In green security, defenders must forecast adversarial behavior, such as poaching, illegal logging, and illegal fishing, to plan effective patrols.<n>We propose a conditional diffusion model for adversary behavior modeling, leveraging its strong distribution-fitting capabilities.<n>We introduce a mixed strategy of mixed strategies and employ a twisted Sequential Monte Carlo (SMC) sampler for accurate sampling.
  arXiv  Detail & Related papers  (2025-02-19T05:30:46Z)
• 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)
• SAFE-SIM: Safety-Critical Closed-Loop Traffic Simulation with Diffusion-Controllable Adversaries [94.84458417662407]
  We introduce SAFE-SIM, a controllable closed-loop safety-critical simulation framework. Our approach yields two distinct advantages: 1) generating realistic long-tail safety-critical scenarios that closely reflect real-world conditions, and 2) providing controllable adversarial behavior for more comprehensive and interactive evaluations. We validate our framework empirically using the nuScenes and nuPlan datasets across multiple planners, demonstrating improvements in both realism and controllability.
  arXiv  Detail & Related papers  (2023-12-31T04:14:43Z)
• Toward Certified Robustness Against Real-World Distribution Shifts [65.66374339500025]
  We train a generative model to learn perturbations from data and define specifications with respect to the output of the learned model. A unique challenge arising from this setting is that existing verifiers cannot tightly approximate sigmoid activations. We propose a general meta-algorithm for handling sigmoid activations which leverages classical notions of counter-example-guided abstraction refinement.
  arXiv  Detail & Related papers  (2022-06-08T04:09:13Z)

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.