Multi-Robot Systems

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The field studies how robot teams can divide work, coordinate motion, and combine information to accomplish tasks that are difficult for one robot alone. Common approaches include multi-agent path finding and task allocation for warehouses and exploration, distributed control and reinforcement learning for swarms, and cooperative localization and SLAM that fuse cameras, ranging, and inertial sensors. Researchers also design communication protocols, simulators, and operator interfaces to support heterogeneous robots while managing limited bandwidth, computation, and human attention. A central challenge is making coordination safe and reliable as teams grow, robots fail, environments change, and methods move from simulation to real hardware.

Latest papers

205 papers30 papers in the last four weeks, up 76% on the four weeks before. 0.2% of all new papers.

Mar 14, 2026

Quotient Geometry and Persistence-Stable Metrics for Swarm Configurations

Can temporal information recover geometry lost by a persistence diagram? For

n\ge3

points on the unit sphere evolving under known homogeneous complete consensus, we prove generic local recovery modulo rotations and permutations from two ordinary zero-dimensional Vietoris--Rips persistence snapshots. The structural result applies to every labeled tree: its edge inner products and their first temporal derivatives have differential rank

2n-3

, exactly the dimension of the regular formation quotient. An explicit pole--equator witness proves this simultaneously for all tree types after taking a finite intersection of open dense sets. Although one snapshot has generic local fibers of dimension

n-2

, two sufficiently close snapshots are injective on a common neighborhood. The optimal inverse Lipschitz constant grows as

Θ(1/|h|)

when the sampling interval tends to zero. A verified left inverse, interval derivative bounds, and explicit persistence-matching margins certify neighborhoods and time intervals at rational reference formations. The results are local and model-specific; reflection ambiguity and centered rank obstructions remain.

Mark M. Bailey

AI, Cyber, Influence and Data Science Department Director, Biological and Computational Intelligence Center National Intelligence University Bethesda, MD, USA

Multi-Robot Systems Swarm Robotics Topological Data Analysis

Mar 14, 2026

Data-Driven Autoregressive Power Prediction for GTernal Robots in the Robotarium

Energy-aware algorithms for multi-robot systems require accurate power consumption models, yet existing approaches rely on kinematic approximations that fail to capture the complex dynamics of real hardware. We present a lightweight autoregressive predictor for the GTernal mobile robot platform deployed in the Georgia Tech Robotarium. Through analysis of 48,000 samples collected across six motion trials, we discover that power consumption exhibits strong temporal autocorrelation (

ρ_1 = 0.95

) that dominates kinematic effects. A 7,041-parameter multi-layer perceptron (MLP) achieves

R^2 = 0.90

on held-out motion patterns by conditioning on recent power history, reaching the theoretical prediction ceiling imposed by measurement noise. Physical validation across seven robots in a collision avoidance scenario yields mean

R^2 = 0.87

, demonstrating zero-shot transfer to unseen robots and behaviors. The predictor runs in 224

μ

s per inference, enabling real-time deployment at 150

\times

the platform's 30 Hz control rate. We release the trained model and dataset to support energy-aware multi-robot algorithm development.

Yassin Abdelmeguid, Ammar Hasan

American University of Sharjah

Autoregressive Modeling Mobile Robotics Multi-Robot Systems

Mar 13, 2026

Hybrid topology control: a dynamic leader-based distributed edge-addition and deletion mechanism

Coordinated operations of multi-robot systems (MRS) require agents to maintain communication connections to accomplish team objectives. However, maintaining the connections imposes costs in terms of restricted robot mobility, resulting in suboptimal team performance. In this work, we consider a realistic MRS framework in which agents are subject to unknown dynamical disturbances and experience communication delays. Most existing works on connectivity maintenance use consensus-based frameworks for graph reconfiguration, where decision-making time scales with the number of nodes and requires multiple rounds of communication, making them ineffective under communication delays. To address this, we propose a novel leader-based decision-making algorithm that uses a central node for efficient real-time reconfiguration, reducing decision-making time to depend on the graph diameter rather than the number of nodes and requiring only one round of information transfer through the network. We propose a novel method for estimating robot locations within the MRS that actively accounts for unknown disturbances and the communication delays. Using these position estimates, the central node selects a set of edges to delete while allowing the formation of new edges, aiming to keep the diameter of the new graph within a threshold. We provide numerous simulation results to showcase the efficacy of the proposed method.

Kunal Garg, Xi Yu

School of Engineering for Matter, Transport and Energy, Arizona State University · School of Manufacturing Systems and Networks, Arizona State University

Multi-Robot Systems Multi-Robot Coordination Multi-Agent Communication

Mar 13, 2026

Conflict Mitigation in Shared Environments using Flow-Aware Multi-Agent Path Finding

Deploying multi-robot systems in environments shared with dynamic and uncontrollable agents presents significant challenges, especially for large robot fleets. In such environments, individual robot operations can be delayed due to unforeseen conflicts with uncontrollable agents. While existing research primarily focuses on preserving the completeness of Multi-Agent Path Finding (MAPF) solutions considering delays, there is limited emphasis on utilizing additional environmental information to enhance solution quality in the presence of other dynamic agents. To this end, we propose Flow-Aware Multi-Agent Path Finding (FA-MAPF), a novel framework that integrates learned motion patterns of uncontrollable agents into centralized MAPF algorithms. Our evaluation, conducted on a diverse set of benchmark maps with simulated uncontrollable agents and on a real-world map with recorded human trajectories, demonstrates the effectiveness of FA-MAPF compared to state-of-the-art baselines. The experimental results show that FA-MAPF can consistently reduce conflicts with uncontrollable agents, up to 55%, without compromising task efficiency.

Lukas Heuer, Yufei Zhu, Luigi Palmieri +4

L. Heuer, Y. Zhu and M. Magnusson - Örebro University · L. Palmieri, A. Rudenko and A. Mannucci - Bosch Research · A. Rudenko - Technical University of Munich +1

Multi-Robot Motion Planning Multi-Agent Path Finding Multi-Robot Systems

Mar 12, 2026

Bridging the Awareness Gap: Socially Mediated State Externalization for Transparent Distributed Home Robots

Distributed multi-robot systems for the home often require robots to operate out of the user's sight, creating a state awareness gap that can diminish trust and perceived transparency and control. This paper investigates whether real-time, socially mediated state externalization can bridge this gap without compromising task performance. We developed a system where a co-located social mediator robot (Pepper) externalizes the hidden execution states of an out-of-sight mobile manipulator (Stretch~3) for voice-driven object retrieval and delivery, where task-level states are synchronized and externalized through verbal updates and visual progress display. In a counterbalanced within-subject study (N=30), we compared a baseline of Autonomous Hidden Execution against Socially Mediated State Externalization. Our results show that externalization significantly increases user task-focused attention (from 15.8% to 84.6%, p<.001) and substantially improves perceived perspicuity, dependability, stimulation, and attractiveness (all p<.001). Furthermore, 83% of participants preferred the externalized condition, and this improvement in user experience was achieved without a statistically significant increase in end-to-end task completion time (p=.271). The results suggest that socially mediated state externalization is an effective architectural mechanism for designing more transparent and trustworthy distributed robot systems, ultimately enhancing user experience without sacrificing performance in distributed home robot deployments.

Wenzheng Zhao, Manideep Duggi, Fengpei Yuan

Faculty of Robotics Engineering Department, Worcester Polytechnic Institute, 100 Institute Rd, Worcester, MA, USA

Human-Robot Interaction Multi-Robot Systems Social Robotics

Mar 12, 2026

Learning Visuomotor Policy for Multi-Robot Laser Tag Game

In this paper, we study multi robot laser tag, a simplified yet practical shooting-game-style task. Classic modular approaches on these tasks face challenges such as limited observability and reliance on depth mapping and inter robot communication. To overcome these issues, we present an end-to-end visuomotor policy that maps images directly to robot actions. We train a high performing teacher policy with multi agent reinforcement learning and distill its knowledge into a vision-based student policy. Technical designs, including a permutation-invariant feature extractor and depth heatmap input, improve performance over standard architectures. Our policy outperforms classic methods by 16.7% in hitting accuracy and 6% in collision avoidance, and is successfully deployed on real robots. Code will be released publicly.

Kai Li, Shiyu Zhao

College of Computer Science and Technology at Zhejiang University, Hangzhou, China. · School of Engineering at Westlake University, Hangzhou, China.

Visuomotor Policy Learning Multi-Robot Systems Vision-Based Robot Control

Mar 11, 2026

Distributed Kalman--Consensus Filtering with Adaptive Uncertainty Weighting for Multi-Object Tracking in Mobile Robot Networks

This paper presents an implementation and evaluation of a Distributed Kalman--Consensus Filter (DKCF) for Multi-Object Tracking (MOT) in mobile robot networks operating under partial observability and heterogeneous localization uncertainty. A key challenge in such systems is the fusion of information from agents with differing localization quality, where frame misalignment can lead to inconsistent estimates, track duplication, and ghost tracks. To address this issue, we build upon the MOTLEE framework and retain its frame-alignment methodology, which uses consistently tracked dynamic objects as transient landmarks to improve relative pose estimates between robots. On top of this framework, we propose an uncertainty-aware adaptive consensus weighting mechanism that dynamically adjusts the influence of neighbor information based on the covariance of the transmitted estimates, thereby reducing the impact of unreliable data during distributed fusion. Local tracking is performed using a Kalman Filter (KF) with a Constant Velocity Model (CVM) and Global Nearest Neighbor (GNN) data association. simulation results demonstrate that adaptive weighting effectively protects local estimates from inconsistent data, yielding a MOTA improvement of 0.09 for agents suffering from localization drift, although system performance remains constrained by communication latency.

Niusha Khosravi, Rodrigo Ventura, Meysam Basiri

Instituto Superior Técnico University of Lisbon Lisbon, Portugal

Multi-Object Tracking Multi-Robot Systems Kalman Filtering Multi-Agent Consensus

Mar 10, 2026

On the Cost of Evolving Task Specialization in Multi-Robot Systems

Task specialization can lead to simpler robot behaviors and higher efficiency in multi-robot systems. Previous works have shown the emergence of task specialization during evolutionary optimization, focusing on feasibility rather than costs. In this study, we take first steps toward a cost-benefit analysis of task specialization in robot swarms using a foraging scenario. We evolve artificial neural networks as generalist behaviors for the entire task and as task-specialist behaviors for subtasks within a limited evaluation budget. We show that generalist behaviors can be successfully optimized while the evolved task-specialist controllers fail to cooperate efficiently, resulting in worse performance than the generalists. Consequently, task specialization does not necessarily improve efficiency when optimization budget is limited.

Paolo Leopardi, Heiko Hamann, Jonas Kuckling +1

Centre for the Advanced Study of Collective Behaviour, University of Konstanz, Konstanz, Germany · Department of Computer and Information Science, University of Konstanz, Konstanz, Germany · Department of Computer Science and Artificial Intelligence, University of Technology Nuremberg, Germany

Multi-Robot Systems Swarm Robotics Evolutionary Robotics

Mar 9, 2026

Fly, Track, Land: Infrastructure-less Magnetic Localization for Heterogeneous UAV-UGV Teaming

Persistent air ground robot teams require nano UAVs that can leave a mobile UGV, perform short scouting or inspection tasks, and reliably return to a compact landing interface. This final docking phase remains difficult: it demands centimeter-scale relative localization on a moving platform, while nano UAVs provide only a few grams of payload and severe onboard sensing, power, and computation constraints. We address this problem with an infrastructure-less magneto inductive anchor-tag localization system for heterogeneous UAV-UGV teaming. Unlike passive magnetic docking aids or approaches exploiting environmental magnetic features, the proposed system actively generates a frequency multiplexed AC magnetic field on the UGV and uses it as an onboard localization reference for closed loop flight. The nano UAV carries only a lightweight passive receive coil and estimates its 3D position directly in the UGV frame, providing a local close range reference for hovering, tracking, and landing without external anchors, visual fiducials, GNSS, or motion capture online. The magnetic estimate is fused with the native onboard sensing stack, enabling operation under nano UAV SWaP constraints. Experiments with a Unitree A1 quadruped and a Crazyflie nano UAV, evaluated against motion capture ground truth, show centimeter level accuracy in static hovering and landing and approximately 8-11 cm RMSE during UGV motion, while the flow only baseline frequently violates the safety bound and fails the task.

Valerio Brunacci, Davide Plozza, Alessio De Angelis +2

Department of Engineering, University of Perugia, Perugia, 06125, Italy · Multi-Robot Systems Group, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, 16627, Czech Republic · Department of Information Technology and Electrical Engineering (D-ITET), ETH Zurich, Zurich, 8092, Switzerland

Robotics Wireless Localization Multi-Robot Systems Cooperative Localization

Mar 7, 2026

A Distributed Gaussian Process Model for Multi-Robot Mapping

We propose DistGP: a multi-robot learning method for collaborative learning of a global function using only local experience and computation. We utilise a sparse Gaussian process (GP) model with a factorisation that mirrors the multi-robot structure of the task, and admits distributed training via Gaussian belief propagation (GBP). Our loopy model outperforms Tree-Structured GPs \cite{bui2014tree} and can be trained online and in settings with dynamic connectivity. We show that such distributed, asynchronous training can reach the same performance as a centralised, batch-trained model, albeit with slower convergence. Last, we compare to DiNNO \cite{yu2022dinno}, a distributed neural network (NN) optimiser, and find DistGP achieves superior accuracy, is more robust to sparse communication and is better able to learn continually.

Seth Nabarro, Mark van der Wilk, Andrew J. Davison

Dyson Robotics Lab, Imperial College London · Department of Computer Science, University of Oxford

Sparse Gaussian Processes Gaussian Belief Propagation Gaussian Processes Multi-Robot Systems Distributed Training Bayesian Inference

Mar 7, 2026

The Talking Robot: Distortion-Robust Acoustic Models for Robot-Robot Communication

We present Artoo, a learned acoustic communication system for robots that replaces hand-designed signal processing with end-to-end co-trained neural networks. Our system pairs a lightweight text-to-speech (TTS) transmitter (1.18M parameters) with a conformer-based automatic speech recognition (ASR) receiver (938K parameters), jointly optimized through a differentiable channel. Unlike human speech, robot-to-robot communication is paralinguistics-free: the system need not preserve timbre, prosody, or naturalness, only maximize decoding accuracy under channel distortion. Through a three-phase co-training curriculum, the TTS transmitter learns to produce distortion-robust acoustic encodings that surpass the baseline under noise, achieving 8.3% CER at 0 dB SNR. The entire system requires only 2.1M parameters (8.4 MB) and runs in under 13 ms end-to-end on a CPU, making it suitable for deployment on resource-constrained robotic platforms.

Hanlong Li, Karishma Kamalahasan, Jiahui Li +2

Institute of Science Tokyo · Georgia Institute of Technology

Robust ASR Automatic Speech Recognition Multi-Robot Systems TTS Synthesis

Mar 5, 2026

LLM-Guided Decentralized Exploration with Self-Organizing Robot Teams

When individual robots have limited sensing capabilities or insufficient fault tolerance, it becomes necessary for multiple robots to form teams during exploration, thereby increasing the collective observation range and reliability. Traditionally, swarm formation has often been managed by a central controller; however, from the perspectives of robustness and flexibility, it is preferable for the swarm to operate autonomously even in the absence of centralized control. In addition, the determination of exploration targets for each team is crucial for efficient exploration in such multi-team exploration scenarios. This study therefore proposes an exploration method that combines (1) an algorithm for self-organization, enabling the autonomous and dynamic formation of multiple teams, and (2) an algorithm that allows each team to autonomously determine its next exploration target (destination). In particular, for (2), this study explores a novel strategy based on large language models (LLMs), while classical frontier-based methods and deep reinforcement learning approaches have been widely studied. The effectiveness of the proposed method was validated through simulations involving tens to hundreds of robots.

Hiroaki Kawashima, Shun Ikejima, Takeshi Takai +2

Graduate School of Information Science, University of Hyogo, Hyogo, Japan · School of Social Information Science, University of Hyogo, Hyogo, Japan · Takenaka Research & Development Institute, Takenaka Corporation, Chiba, Japan +1

Multi-Robot Exploration Autonomous Robot Exploration Multi-Robot Systems Swarm Robotics Large Language Model-Based Robot Planning

Mar 3, 2026

IMR-LLM: Industrial Multi-Robot Task Planning and Program Generation using Large Language Models

In modern industrial production, multiple robots often collaborate to complete complex manufacturing tasks. Large language models (LLMs), with their strong reasoning capabilities, have shown potential in coordinating robots for simple household and manipulation tasks. However, in industrial scenarios, stricter sequential constraints and more complex dependencies within tasks present new challenges for LLMs. To address this, we propose IMR-LLM, a novel LLM-driven Industrial Multi-Robot task planning and program generation framework. Specifically, we utilize LLMs to assist in constructing disjunctive graphs and employ deterministic solving methods to obtain a feasible and efficient high-level task plan. Based on this, we use a process tree to guide LLMs to generate executable low-level programs. Additionally, we create IMR-Bench, a challenging benchmark that encompasses multi-robot industrial tasks across three levels of complexity. Experimental results indicate that our method significantly surpasses existing methods across all evaluation metrics.

Xiangyu Su, Juzhan Xu, Oliver van Kaick +2

Shenzhen University. · SpeedBot Robotics Co., Ltd. · Carleton University. +1

Robot Task Planning Large Language Model-Based Robot Planning Multi-Robot Systems Industrial Robotics LLM-Based Program Synthesis

Feb 25, 2026

Parallel Reference-Centric Continuous-Time Relative Localization with Augmented Clamped Non-Uniform B-Splines

Accurate relative localization is critical for multi-robot cooperation. In robot groups, measurements from different robots arrive asynchronously and with clock time-offsets. Although Continuous-Time (CT) formulations have proved effective for handling asynchronous measurements in single-robot SLAM and calibration, extending CT methods to multi-robot settings faces great challenges in achieving high-accuracy, low-latency, and high-frequency performance. In particular, existing CT methods suffer from the inherent query-time delay of unclamped B-splines and high optimization latency. This paper proposes CT-RIO, a novel Continuous-Time Relative-Inertial Odometry framework. We adopt Clamped Non-Uniform B-splines (C-NUBS) to represent states, eliminating the query-time delay. We further augment C-NUBS with closed-form extension and shrinkage operations that preserve the spline shape, making it suitable for online estimation and enabling flexible knot management. This flexibility leads to the concept of a knot-keyknot strategy, which supports spline extension at high frequency while retaining sparse keyknots for adaptive relative motion modeling. We then formulate a reference-centric sliding-window relative localization problem that operates purely on relative kinematics and inter-robot constraints. To enable low-latency and high-frequency estimation, we decompose the tightly coupled optimization into robot-wise subproblems and solve them in parallel using asynchronous block coordinate descent. Extensive experiments show that CT-RIO converges from time-offsets as large as 264 ms to sub-millisecond within 3 s, and achieves RMSEs of 0.046 m and 1.8 degree. It consistently outperforms evaluated published methods, with improvements of up to 60% under high-speed motion.

Jiadong Lu, Zhehan Li, Tao Han +4

State Key Laboratory of Industrial Control Technology, Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou, 310027, China. · Huzhou Institute of Zhejiang University, Huzhou, 313000, China. · The School of Automation and Intellegent Sensing, Shanghai Jiao Tong University, Shanghai, 200240, China. +1

Relative Pose Estimation Multi-Robot Systems Cooperative Localization B-Spline Trajectory Representation Robot State Estimation

Feb 23, 2026

Hilbert-Augmented Reinforcement Learning for Scalable Multi-Robot Coverage and Exploration

We present a coverage framework that integrates Hilbert space-filling priors into decentralized multi-robot learning and execution. We augment DQN and PPO with Hilbert-based spatial indices to structure exploration and reduce redundancy in sparse-reward environments, and we evaluate scalability in multi-robot grid coverage. We further describe a waypoint interface that converts Hilbert orderings into curvature-bounded, time-parameterized SE(2) trajectories (planar (x, y, θ)), enabling onboard feasibility on resource-constrained robots. Experiments show improvements in coverage efficiency, redundancy, and convergence speed over DQN/PPO baselines. In addition, we validate the approach on a Boston Dynamics Spot legged robot, executing the generated trajectories in indoor environments and observing reliable coverage with low redundancy. These results indicate that geometric priors improve autonomy and scalability for swarm and legged robotics.

Tamil Selvan Gurunathan, Aryya Gangopadhyay

Multi-Robot Exploration Multi-Robot Systems Reinforcement Learning Decentralized MARL Robot Motion Planning

Feb 22, 2026

Distributed and Consistent Multi-Robot Visual-Inertial-Ranging Odometry on Lie Groups

Reliable localization is a fundamental requirement for multi-robot systems operating in GPS-denied environments. Visual-inertial odometry (VIO) provides lightweight and accurate motion estimation but suffers from cumulative drift in the absence of global references. Ultra-wideband (UWB) ranging offers complementary global observations, yet most existing UWB-aided VIO methods are designed for single-robot scenarios and rely on pre-calibrated anchors, which limits their robustness in practice. This paper proposes a distributed collaborative visual-inertial-ranging odometry (DC-VIRO) framework that tightly fuses VIO and UWB measurements across multiple robots. Anchor positions are explicitly included in the system state to address calibration uncertainty, while shared anchor observations are exploited through inter-robot communication to provide additional geometric constraints. By leveraging a right-invariant error formulation on Lie groups, the proposed approach preserves the observability properties of standard VIO, ensuring estimator consistency. Simulation results with multiple robots demonstrate that DC-VIRO significantly improves localization accuracy and robustness, while simultaneously enabling anchor self-calibration in distributed settings.

Ziwei Kang, Yizhi Zhou

School of Control and Computer Engineering, North China Electric Power University, Beijing, VA 102206, China · Department of Electrical and Computer Engineering, George Mason University, Fairfax, VA 22030, USA

Robot State Estimation Robot Localization Visual-Inertial Odometry Cooperative Localization Multi-Robot Systems

Feb 18, 2026

Dual-Quadruped Collaborative Transportation in Narrow Environments via Safe Reinforcement Learning

Collaborative transportation, where multiple robots collaboratively transport a payload, has garnered significant attention in recent years. While ensuring safe and high-performance inter-robot collaboration is critical for effective task execution, it is difficult to pursue in narrow environments where the feasible region is extremely limited. To address this challenge, we propose a novel approach for dual-quadruped collaborative transportation via safe reinforcement learning (RL). Specifically, we model the task as a fully cooperative constrained Markov game, where collision avoidance is formulated as constraints. We introduce a cost-advantage decomposition method that enforces the sum of team constraints to remain below an upper bound, thereby guaranteeing task safety within an RL framework. Furthermore, we propose a constraint allocation method that assigns shared constraints to individual robots to maximize the overall task reward, encouraging autonomous task-assignment among robots, thereby improving collaborative task performance. Simulation and real-time experimental results demonstrate that the proposed approach achieves superior performance and a higher success rate in dual-quadruped collaborative transportation compared to existing methods.

Zhezhi Lei, Zhihai Bi, Wenxin Wang +1

Robotics and Autonomous Systems Thrust, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou 511453, China · Advanced Remanufacturing & Technology Centre (ARTC), Agency for Science, Technology and Research (A*STAR), Singapore 637143 · Cheng Kar-Shun Robotics Institute, The Hong Kong University of Science and Technology, Hong Kong SAR, China

Multi-Robot Cargo Transportation Multi-Robot Systems Cooperative MARL Safe RL Robot Collision Avoidance

Feb 17, 2026

Lifelong Scalable Multi-Agent Realistic Testbed and A Comprehensive Study on Design Choices in Lifelong AGV Fleet Management Systems

We present Lifelong Scalable Multi-Agent Realistic Testbed (LSMART), an open-source simulator to evaluate any Multi-Agent Path Finding (MAPF) algorithm in a Fleet Management System (FMS) with Automated Guided Vehicles (AGVs). MAPF aims to move a group of agents from their corresponding starting locations to their goals. Lifelong MAPF (LMAPF) is a variant of MAPF that continuously assigns new goals for agents to reach. LMAPF applications, such as autonomous warehouses, often require a centralized, lifelong system to coordinate the movement of a fleet of robots, typically AGVs. However, existing works on MAPF and LMAPF often assume simplified kinodynamic models, such as pebble motion, as well as perfect execution and communication for AGVs. Prior work has presented SMART, a software capable of evaluating any MAPF algorithms while considering agent kinodynamics, communication delays, and execution uncertainties. However, SMART is designed for MAPF, not LMAPF. Generalizing SMART to an FMS requires many more design choices. First, an FMS parallelizes planning and execution, raising the question of when to plan. Second, given planners with varying optimality and differing agent-model assumptions, one must decide how to plan. Third, when the planner fails to return valid solutions, the system must determine how to recover. In this paper, we first present LSMART, an open-source simulator that incorporates all these considerations to evaluate any MAPF algorithms in an FMS. We then provide experiment results based on state-of-the-art methods for each design choice, offering guidance on how to effectively design centralized lifelong AGV Fleet Management Systems. LSMART is available at https://smart-mapf.github.io/lifelong-smart.

Jingtian Yan, Yulun Zhang, Zhenting Liu +5

Robotics Institute, Carnegie Mellon University · Symbotic

Multi-Robot Systems Multi-Agent Path Finding Robust MAPF

Feb 17, 2026

A Comparison of Bayesian Prediction Techniques for Mobile Robot Trajectory Tracking

This paper presents a performance comparison of different estimation and prediction techniques applied to the problem of tracking multiple robots. The main performance criteria are the magnitude of the estimation or prediction error, the computational effort and the robustness of each method to non-Gaussian noise. Among the different techniques compared are the well known Kalman filters and their different variants (e.g. extended and unscented), and the more recent techniques relying on Sequential Monte Carlo Sampling methods, such as particle filters and Gaussian Mixture Sigma Point Particle Filter.

Jose Luis Peralta-Cabezas, Miguel Torres-Torriti, Marcelo Guarini-Hermann

Dept. of Electrical Engineering, Pontificia Universidad Católica de Chile, Vicuña Mackenna 4860, Casilla 306-22, Santiago, Chile

Robot Trajectory Tracking Robot State Estimation Bayesian Filtering Particle Filtering Kalman Filtering Multi-Robot Systems

Feb 2, 2026

Bridging the Sim-to-Real Gap with multipanda_ros2: A Real-Time ROS2 Framework for Multimanual Systems

We present

multipanda\_ros2

, a novel open-source ROS2 architecture for multi-robot control of Franka Robotics robots. Leveraging ros2 control, this framework provides native ROS2 interfaces for controlling any number of robots from a single process. Our core contributions address key challenges in real-time torque control, including interaction control and robot-environment modeling. A central focus of this work is sustaining a 1kHz control frequency, a necessity for real-time control and a minimum frequency required by safety standards. Moreover, we introduce a controllet-feature design pattern that enables controller-switching delays of

\le 2

ms, facilitating reproducible benchmarking and complex multi-robot interaction scenarios. To bridge the simulation-to-reality (sim2real) gap, we integrate a high-fidelity MuJoCo simulation with quantitative metrics for both kinematic accuracy and dynamic consistency (torques, forces, and control errors). Furthermore, we demonstrate that real-world inertial parameter identification can significantly improve force and torque accuracy, providing a methodology for iterative physics refinement. Our work extends approaches from soft robotics to rigid dual-arm, contact-rich tasks, showcasing a promising method to reduce the sim2real gap and providing a robust, reproducible platform for advanced robotics research.

Jon Škerlj, Seongjin Bien, Abdeldjallil Naceri +1

Munich Institute of Robotics and Machine Intelligence (MIRMI), Technische Universität München (TUM), Germany · Technische Universität Nürnberg (UTN), Germany · Mohamed Bin Zayed University of Artificial Intelligence, UAE

Robotic Control Robot Dynamics Identification Multi-Robot Systems Robotics Simulation Sim-to-Real Transfer Robotics

Jan 30, 2026

MOSAIC: Modular Scalable Autonomy for Intelligent Coordination of Heterogeneous Robotic Teams

Mobile robots have become indispensable for exploring hostile environments, such as in space or disaster relief scenarios, but often remain limited to teleoperation by a human operator. This restricts the deployment scale and requires near-continuous low-latency communication between the operator and the robot. We present MOSAIC: a scalable autonomy framework for multi-robot scientific exploration using a unified mission abstraction based on Points of Interest (POIs) and multiple layers of autonomy, enabling supervision by a single operator. The framework dynamically allocates exploration and measurement tasks based on each robot's capabilities, leveraging team-level redundancy and specialization to enable continuous operation. We validated the framework in a space-analog field experiment emulating a lunar prospecting scenario, involving a heterogeneous team of five robots and a single operator. Despite the complete failure of one robot during the mission, the team completed 82.3% of assigned tasks at an Autonomy Ratio of 86%, while the operator workload remained at only 78.2%. These results demonstrate that the proposed framework enables robust, scalable multi-robot scientific exploration with limited operator intervention. We further derive practical lessons learned in robot interoperability, networking architecture, team composition, and operator workload management to inform future multi-robot exploration missions.

David Oberacker, Julia Richter, Philip Arm +10

FZI Forschungszentrum für Informatik, Karlsruhe, Germany · Machine Intelligence and Robotics Lab (MaiRo), Karlsruhe Institute for Technology (KIT), Karlsruhe, Germany · Robotic Systems Lab (RSL), ETH Zürich, Zürich, Switzerland +1

Multi-Robot Systems Multi-Robot Exploration Multi-Robot Coordination Human-Robot Collaboration Multi-Robot Task Allocation

Dec 31, 2025

CREPES-X: Hierarchical Bearing-Distance-Inertial Direct Cooperative Relative Pose Estimation System

Relative localization is essential for cooperation in autonomous multi-robot systems. Existing approaches either rely on shared environmental features or inertial assumptions, or they degrade under pairwise non-line-of-sight conditions and outliers in complex environments. Robustly and efficiently fusing inter-robot bearings, distances, and inertial measurements for tens of robots remains challenging. We present CREPES-X (Cooperative RElative Pose Estimation System with multiple eXtended features), a hierarchical relative localization framework that enhances speed, accuracy, and robustness under challenging conditions, without requiring any global information. The hardware packs infrared (IR) LEDs, an IR camera, an ultra-wideband module, and an IMU into a cube no larger than

6\,\text{cm}

on each side. On this hardware, a two-stage hierarchical estimator meets different latency, accuracy, and robustness requirements. The single-frame estimator returns instantaneous relative poses from a closed-form solution with bearing outlier rejection. The multi-frame estimator then refines these poses with IMU pre-integration under robocentric relative kinematics, using loosely- and tightly-coupled optimization. Extensive simulations and real-world experiments validate the effectiveness of CREPES-X, demonstrating robustness of up to

90\%

bearing outliers, resilience in challenging conditions, and RMSE of

7.0\,\text{cm}

and

2.2^\circ

in real-world datasets.

Zhehan Li, Jiadong Lu, Zheng Wang +6

State Key Laboratory of Industrial Control Technology, Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou, China. · Huzhou Institute of Zhejiang University, Huzhou, China. · University of Michigan, Ann Arbor, MI, USA. +1

Relative Pose Estimation Multi-Robot Systems Camera Pose Estimation Cooperative Localization Robot Localization Multimodal Sensor Fusion

Dec 24, 2025

Flocking phase transition and threat responses in bio-inspired autonomous drone swarms

Collective motion inspired by animal groups offers powerful design principles for autonomous aerial swarms. We present a bio-inspired 3D flocking algorithm in which each drone interacts only with a minimal set of influential neighbors, relying solely on local alignment and attraction cues. By systematically tuning these two interaction gains, we map a phase diagram revealing sharp transitions between swarming and schooling, as well as a critical region where susceptibility, polarization fluctuations, and reorganization capacity peak. Outdoor experiments with a swarm of ten drones, combined with simulations using a calibrated flight-dynamics model, show that operating near this transition enhances responsiveness to external disturbances. When confronted with an intruder, the swarm performs rapid collective turns, transient expansions, and reliably recovers high alignment within seconds. These results demonstrate that minimal local-interaction rules are sufficient to generate multiple collective phases and that simple gain modulation offers an efficient mechanism to adjust stability, flexibility, and resilience in drone swarms.

Matthieu Verdoucq, Dari Trendafilov, Clément Sire +3

Ecole Nationale de l’Aviation Civile, Université de Toulouse, France · Centre de Recherches sur la Cognition Animale, Centre de Biologie Intégrative (CBI), Centre National de la Recherche Scientifique (CNRS) & Université de Toulouse – Paul Sabatier, France · Laboratoire de Physique Théorique, CNRS & Université de Toulouse – Paul Sabatier, France

Aerial Robotics Multi-Robot Systems Swarm Robotics Multi-Agent Control Multi-Agent Collaboration Multi-Robot Coordination

Nov 19, 2025

Modelling and Model-Checking a ROS2 Multi-Robot System using Timed Rebeca

Model-based development accelerates prototyping, enables earlier experimentation, and ensures rigorous validation of system design intents. In multi-agent systems with complex asynchronous interactions and concurrency, formal verification, particularly model-checking, offers an automated means of confirming that desired properties hold. Timed Rebeca, an actor-based modelling language supporting reactive, concurrent, and timed behaviors, together with its model-checking tool, provides a powerful framework for this purpose. By leveraging these capabilities, Timed Rebeca can intuitively capture ROS2 node graphs, recurring physical signals, motion primitives, and other time-convertible behaviors. Nevertheless, modelling and verifying multi-robot systems entail significant challenges: abstracting intricate information, bridging the gap between discrete models and continuous system dynamics, and managing large state spaces while preserving fidelity. To address these challenges, we propose discretization strategies tailored to various data types and identify thresholds of abstraction that balance accuracy and tractability. We further introduce optimization techniques to accelerate verification. Our work demonstrates how to systematically design and verify multi-robot systems through Timed Rebeca, efficiently transform continuous dynamics into discrete models for model-checking, and maintain a practical, bidirectional flow between the abstract model and the ROS2 implementation. The accompanying Rebeca and ROS2 codebases, made openly available, serve as a foundational reference for researchers and developers aiming to model and verify advanced autonomous robotic systems.

Hiep Hong Trinh, Marjan Sirjani, Federico Ciccozzi +2

Mälardalen University, Universitetsplan 1, 721 23 Västerås, Sweden · KTH Royal Institute of Technology, Brinellvägen 8, 114 28 Stockholm, Sweden

Multi-Robot Systems Robotics Formal Verification

Oct 2, 2025

ARMADA: Autonomous Online Failure Detection and Human Shared Control Empower Scalable Real-world Deployment and Adaptation

Imitation learning has shown promise in learning from large-scale real-world datasets. However, pretrained policies usually perform poorly without sufficient in-domain data. Besides, human-collected demonstrations entail substantial labour and tend to encompass mixed-quality data and redundant information. As a workaround, human-in-the-loop systems gather domain-specific data for policy post-training, and exploit closed-loop policy feedback to offer informative guidance, but usually require full-time human surveillance during policy rollout. In this work, we devise ARMADA, a multi-robot deployment and adaptation system with human-in-the-loop shared control, featuring an autonomous online failure detection method named FLOAT. Thanks to FLOAT, ARMADA enables paralleled policy rollout and requests human intervention only when necessary, significantly reducing reliance on human supervision. Hence, ARMADA enables efficient acquisition of in-domain data, and leads to more scalable deployment and faster adaptation to new scenarios. We evaluate the performance of ARMADA on four real-world tasks. FLOAT achieves nearly 95% accuracy on average, surpassing prior state-of-the-art failure detection approaches by over 20%. Besides, ARMADA manifests more than 4

\times

increase in success rate and greater than 2

\times

reduction in human intervention rate over multiple rounds of policy rollout and post-training, compared to previous human-in-the-loop learning methods.

Wenye Yu, Jun Lv, Zixi Ying +3

Shanghai Jiao Tong University · Shanghai Innovation Institute · Noematrix Ltd.

Robot Policy Adaptation Robot Imitation Learning Data-Efficient Robot Learning Robot Failure Detection Shared Autonomy Multi-Robot Systems