Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2610.29377

Integrating Flow Matching and Model Predictive Control for Safe Robot Navigation

Philipp Holzmann·Maik Pfefferkorn·Joao Carvalho·Ali Younes·An Thai Le·Georgia Chalvatzaki·Jan Peters·Rolf Findeisen
View original source ↗Read source document ↗Export metadata ↓

Abstract

Learning-based path planners based on diffusion and flow matching can generate diverse trajectories from demonstrations but typically lack mechanisms for constraint satisfaction and collision avoidance during deployment. This work proposes a framework that combines flow-matching-based path planning with model predictive path-following control to integrate data-driven path generation with real-time, constraint-aware execution. The flow-matching model captures multimodal path distributions, while the predictive controller adapts motion online to satisfy state and input constraints and avoid obstacles using updated local sensor information. An event-triggered re-planning scheme incorporates predictive control solutions into subsequent path generation, promoting collision-free navigation in environments with previously unseen obstacles. Simulation results for mobile robot navigation demonstrate robust performance across cluttered environments. The framework provides a systematic approach to combining flow matching with predictive control for adaptive, data-driven navigation.

Explore related subjects

cs.ROcs.SYeess.SY

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Philipp Holzmann, Maik Pfefferkorn, Joao Carvalho, Ali Younes, An Thai Le, Georgia Chalvatzaki, Jan Peters, Rolf Findeisen. 2026-10-21. Integrating Flow Matching and Model Predictive Control for Safe Robot Navigation. https://arxiv.org/abs/2610.29377

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

AgriVLN: Vision-and-Language Navigation for Agricultural Robots

Agricultural robots are serving as powerful members across a wide range of tasks, nevertheless, they are still heavily relying on manual operations or railway systems for movement. In contrast, Vision-and-Language Navigation (VLN) enables navigating robots from starting points to target positions following natural language instructions, having shown strong performances across various domains. However, none of the existing benchmarks or methods are specifically designed for agricultural robots. To bridge this gap, we build our A2A benchmark, which consists of 1,560 episodes across 6 classes of agricultural scenes using the step-by-step format for instructions and the continuous environment for actions. We evaluate several existing state-of-the-art VLN methods on A2A, then we observe that they perform well on short instructions while perform poorly on long instructions. To alleviate this phenomenon, we design our STL module, which decomposes a complex instruction into a list of simple subtasks, thereby assisting the decision-maker to focus on the current short subtask rather than the entire long instruction at any time. We integrate STL into the NavGPT backbone to propose our AgriVLN method, achieving a sufficient improvement on SR from 0.33 to 0.47 with a slight sacrifice on NE from 2.76 m to 2.91 m. We further implement the ablation studies and the case study, discussing the effectiveness and limitations of AgriVLN on different subtask quantities and scene classes. Repository: https://github.com/AlexTraveling/AgriVLN.

cs.RO↗
arXiv2026

HEHA: Bounded Suboptimal Planning for Constrained Routing and Its Use in Heterogeneous Multi-Robot Exploration of Unknown Environments

This paper considers the path planning problem for autonomous exploration of an unknown environment using multiple heterogeneous robots such as drones, wheeled, and legged robots, with different capabilities to traverse complex terrains. A key challenge there is to intelligently allocate the robots to the unknown areas to be explored and determine the visiting order of those areas subject to traversability constraints, which leads to a large-scale constrained multiple hamiltonian path problem (mHPP) that needs to be quickly and iteratively solved every time when new space is explored. To address the hallenge, we propose HEHA (Hierarchical Exploration with Heterogeneous Agents) using a recent hierarchical method that decomposes the exploration into global planning and local planning. The major contribution in HEHA is its global planning, where we propose a new routing algorithm RCH (Routing with Constraints and Heterogeneity) that can quickly find bounded sub-optimal solutions for mHPP to minimize the maximum path length (i.e., makespan) among the agents subject to traversability constraints. Additionally, the local planner in HEHA also considers heterogeneity to avoid repeated and duplicated exploration among the robots. The experimental results show, in the presence of heterogeneous traversability, our RCH can solve mHPP with up to 47% smaller makespan than several baselines, and HEHA can reduce the exploration time by up to 30% compared with existing approaches.

cs.RO↗
arXiv2026

Onboard UAV State Estimation for Fast Flights Without External Localization Systems: A2RL Drone Racing Finalist Approach

Fast flights with aggressive maneuvers in cluttered GNSS-denied environments require fast, reliable, and accurate UAV state estimation. In this paper, we present an approach for onboard state estimation of a high-speed UAV using a monocular RGB camera and an IMU. Our approach fuses data from Visual-Inertial Odometry (VIO), an onboard landmark-based camera measurement system, and an IMU to produce an accurate state estimate. Using onboard measurement data, we estimate and compensate for VIO drift through a novel mathematical drift model. State-of-the-art approaches often rely on more complex hardware (e.g., stereo cameras or rangefinders) and use uncorrected drifting VIO velocities, orientation, and angular rates, leading to errors during fast maneuvers. In contrast, our method corrects all VIO states (position, orientation, linear and angular velocity), resulting in accurate state estimation even during rapid and dynamic motion. Our approach was thoroughly validated through 1600 simulations and numerous real-world experiments. Furthermore, we applied the proposed method in the A2RL Drone Racing Challenge 2025, where our team advanced to the final four out of 210 teams and earned a medal.

cs.RO↗