Swarm Robotics

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16 papers in the last four weeks, against 1 the four weeks before. 0.2% of all new papers.

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Latest papers 62

Oct 5, 2026cs.RO

Towards Decentralized Formation of Minimum-Length Communication Networks Using Robot Swarms

Multi-robot missions in infrastructure-denied environments frequently rely on reliable communication links between spatially separated locations. We propose a fully decentralized framework for constructing and dynamically maintaining communication networks without centralized topology planning or global positioning infrastructure. Driven strictly by local interactions, robots reconfigure local network topologies and adjust their physical positions to minimize overall network length while adhering to communication constraints. Formal analysis shows that our local reconfiguration operations guarantee continuous network connectivity, strictly decrease network length with every branch transfer, and bound worst-case performance to the shortest starlike tree. Embodied simulations and physical multi-robot experiments confirm that our approach forms networks near the length of centrally computed Euclidean Steiner trees. Additionally, the system dynamically adapts to moving targets and optimizes deployment by utilizing only necessary connectors, preserving excess robots for auxiliary tasks. This work enables autonomous swarms to self-organize adaptive ad hoc communication infrastructure in communication-denied environments, which could support applications varying from subterranean exploration to planetary missions.

Genki Miyauchi, Mohamed S. Talamali, Julian Rau +1

Bristol Robotics Laboratory, University of Bristol, Bristol, UK · School of Electrical and Electronic Engineering, University of Sheffield, Sheffield, UK · Department of Computer Science, Technical University of Darmstadt, Darmstadt, Germany

Swarm Robotics Combinatorial Optimization Multi-Robot Coordination Decentralized Optimization

Oct 5, 2026cs.RO

Visual Swarm Navigation via Deep Reinforcement Learning and Evolutionary Hybrid Design

Swarm robotics presents a robust and cost-effective paradigm for advanced automation in complex, dynamic environments, such as those encountered in search and rescue or environmental monitoring. A fundamental challenge for this field is the data-driven design of decentralized controllers capable of generating emergent collective behaviors. This paper proposes a novel, AI-driven hybrid methodology for the automatic synthesis of swarm robotic controllers for autonomous visual navigation. This approach synergistically combines multi-agent reinforcement learning with neuro-evolutionary strategies, specifically leveraging implementations of the cross-entropy method and the covariance matrix adaptation evolution strategy to optimize a pre-trained individual navigation policy. The underlying deep architecture is engineered for low-cost, resource-constrained platforms, utilizing a compact neural network that relies exclusively on monocular camera imagery. This vision-based design emphasizes computational and energy efficiency, a critical requirement for practical swarm deployments. Experiments, performed in a high-fidelity physics simulator, demonstrate that the resulting controllers enable robust and scalable collective exploration of diverse indoor environments. The controller trained using our cross-entropy method achieves superior exploration coverage, visiting 36.20% more regions compared to the covariance matrix adaptation evolution strategy. Critically, our best vision-based policy achieves exploration performance statistically comparable to traditional methods relying on more expensive distance sensors, while delivering a significant 31.40% average reduction in energy consumption. These findings validate an effective and economically viable autonomous control system, establishing a path for deploying highly efficient collective intelligence in real-world engineering applications.

Álvaro Díez, Fidel Aznar

Department of Computer Science and Artificial Intelligence, University of Alicante

Swarm Robotics Multi-Robot Exploration Decentralized MARL Vision-Based Robot Control Neuroevolution

Oct 4, 2026cs.DC

Min-Max Uniform Circle Formation by Asynchronous Mobile Robots

Given a set of point robots

\mathcal{R}

in the Euclidean plane and a target circle

\mathbf C

enclosing all robot positions, the \textsc{Min-Max Uniform Circle Formation (MMUCF)} problem requires the robots to move to distinct positions on

\mathbf C

such that the final configuration forms a regular

n

-gon while minimizing the maximum distance traveled by any robot. Uniform circle formation is a fundamental coordination task in swarm robotics with applications in perimeter monitoring, surveillance, boundary coverage, and pattern formation. The literature does not address the optimization of the maximum individual displacement during the formation process. In this work, we study the min--max versions of the circle formation and uniform circle formation problems, where the goal is to minimize the maximum distance traveled by any robot. We consider these problems under the

\mathcal{ASYNC}

model, where robots are autonomous, anonymous, identical, homogeneous, oblivious, and silent, and operate under the \textit{Look--Compute--Move} model with non-rigid motion. We first give necessary conditions for a deterministic solution and then present deterministic, distributed, and collision-free algorithms that form a circle and a uniform circle in finite time while minimizing the maximum movement. The algorithms ensure that robots reach distinct positions on the circle and, in the uniform case, equally spaced positions on

\mathbf C

under the considered model.

Animesh Maiti, Prakhar Shukla, Subhash Bhagat

Department of Mathematics Indian Institute of Technology Jodhpur Jodhpur, Rajasthan, India

Multi-Robot Formation Control Multi-Robot Systems Swarm Robotics

Oct 1, 2026cs.MA

Managing Context and Communication in Distributed Agentic UAV Swarms

Unmanned aerial vehicle (UAV) swarms increasingly rely on language-model agents to provide adaptive mission-level reasoning in uncertain environments. Fully distributed control, in which each UAV hosts an independent Small Language Model (SLM), removes reliance on a centralized coordinator but introduces an information-management problem: long-running interaction histories can degrade the reasoning context, while indiscriminate information dissemination increases communication and inference overhead. We address these challenges with a distributed UAV-agent architecture that enables continuous local SLM control through an event-driven reason-act-observe lifecycle. Runtime knowledge is represented as structured atomic notes and organized into core, local, and peer-specific memory. A deterministic interest-aware gossip engine selectively disseminates these notes according to recipient-specific semantic novelty and recency. We evaluate the architecture using ten UAVs in a simulated search-and-rescue mission. Our approach completes all experimental runs, whereas unrestricted flooding messages completes only 70-85%, and delegating forwarding decisions to the SLM prevents mission completion in every run. Compared with unrestricted flooding, our approach approximately halves inference-token consumption, reduces transmitted data, and achieves lower survivor-count error.

Andrea Iannoli, Ivan Zyrianoff, Angelo Trotta +2

Department of Computer Science and Engineering University of Bologna Bologna, Italy · Technology Innovation Institute (TII) United Arab Emirates

Multi-Agent LLM Systems Swarm Robotics Multi-Agent Communication Memory-Augmented Agents

Sep 24, 2026cs.RO

Temperament Engineering: Designing Strategic Behavioural Diversity in Robot Swarms

No two robots are truly identical: calibration, battery state, sensor drift and wear give every swarm a distribution of behaviour rather than a single point, usually treated as an imperfection to be minimised. In animal collectives the reverse holds: consistent individual differences in behaviour ('temperament') are shaped by natural selection and often decisive for group performance. This perspective proposes 'temperament engineering', a bio-inspired framework that treats the swarm's distribution of temperaments, rather than the individual controller, as the design object. It borrows five evolutionarily validated axes of animal temperament (shyness-boldness, exploration-avoidance, activity, aggressiveness and sociability) as a design vocabulary, rendering each as a continuous control parameter

τ\in [0,1]

above the controller, realisable as a module threshold, a policy-conditioning vector in multi-agent reinforcement learning, or a constraint on a foundation-model planner. A three-phase workflow maps mission success criteria onto relevant axes, plans the shape of the

τ

distribution, and tunes reaction norms governing how temperament responds to environmental cues. The payoff is greatest under decentralisation: where a central planner can reassign behaviour online, a temperament distribution is a planner output, but in a swarm without global knowledge it must be an offline, anticipatory design input. Behavioural and platform heterogeneity are thereby co-design variables, and I sketch tentative robot-native axes (self-model plasticity, forcefulness, initiative and expressiveness) arising from features robots have and animals do not. Engineered heterogeneity has been shown to outperform homogeneous swarms in tasks such as aggregation and exploration; establishing when, and how much, heterogeneity repays its cost is the work the field can now take forward.

Edmund R. Hunt

School of Engineering Mathematics & Technology, University of Bristol, Ada Lovelace Building, Tankard’s Cl, Bristol, BS8 1TW, United Kingdom.

Swarm Robotics Multi-Robot Coordination

Sep 23, 2026cs.MA

Connectivity Preservation and Graph Stretching in Range-Only Swarm Dispersion

We study connectivity-preserving finite-jump dispersion of anonymous, identical, and oblivious agents under an idealized range-only sensing model. Each agent measures only the distances to its visible neighbors, without bearings, identifiers, communication, memory, or a shared coordinate system. We derive the largest isotropic displacement certifiable as safe from these measurements alone. The resulting rule requires only the distance to the farthest visible neighbor: each agent selects a random direction and moves by half of its remaining visibility margin. The rule preserves every existing visibility edge under synchronous finite motion and therefore preserves connectivity. For two agents, we prove positive conditional drift in squared distance, almost-sure convergence to the visibility boundary, and finite expected time to reach any fixed neighborhood of that boundary. A one-million-run Monte Carlo experiment agrees with the exact first-round moments and estimates approximately 9.5 rounds to reach distance 0.97V from coincident initial positions; an independent Bellman-equation computation gives the same estimate. For general swarms, 1,000 runs across five initial-topology classes reproduce the deterministic safety guarantee at implementation level and reveal a consistent topology-dependent ordering of attainable diameter under the tested protocol. These results provide a theoretical foundation for connectivity-preserving multi-robot dispersion under minimal sensing, while isolating the guarantees achievable from anonymous range measurements alone.

Ariel Barel

Faculty of Computer Science, Technion Israel Institute of Technology, Haifa, Israel.

Sep 21, 2026cs.MA

Perception-Aware Communication Middleware for Distributed Visual Perception in UAV Swarms

Unmanned Aerial Vehicle (UAV) swarms increasingly support safety-critical applications that rely on distributed visual perception. Meeting the low-latency requirements of these applications can require perception models to execute within the swarm on inference-capable UAVs, creating a need for efficient UAV-to-UAV transport of high-bandwidth perception data. However, the Quality-of-Service (QoS) requirements of perception differ from conventional packet-level QoS; successful delivery of individual packets does not ensure that a complete, timely, and usable image is available for inference. We present a novel perception-aware communication middleware that treats complete perception-data samples as the communication objects for which QoS must be satisfied. The middleware extends a lightweight UDP broker-based publish-subscribe architecture with perception-specific services, including image fragmentation and reconstruction, concurrent packet transmission, priority-aware scheduling, and image quality assessment. The middleware is evaluated on a heterogeneous hardware testbed emulating a UAV swarm using YOLOv8n object detection. Experimental results demonstrate low end-to-end application latency, substantially higher throughput than a lightweight UDP broker, effective prioritization of perception traffic under increasing background load, and mitigation of object-detection degradation through middleware-level image quality assessment. This work provides an initial framework for integrating AI-specific data handling into communication middleware to support emerging distributed AI applications in multi-agent mobile cyber-physical systems.

Manveen Kaur, Kevin Loi, Ifunanya Okafor +2

Department of Computer Science, California State University, Los Angeles Los Angeles, CA, USA · Department of Civil Engineering, California State University, Los Angeles Los Angeles, CA, USA

Aerial Robotics Swarm Robotics Cooperative Perception

Sep 16, 2026cs.RO

Body-Motion Control of a Simulated Aerial Swarm from a First-Person View

First-person-view (FPV) teleoperation of aerial swarms requires an operator to coordinate collective translation, viewing direction, and formation spacing. We present an upper-body interface that maps torso inclination, hand position, and head rotation to five continuous command dimensions. Neutral postures and motion ranges are calibrated for each participant. In a within-subject study, 14 participants navigated a simulated 15-agent swarm through three-dimensional obstacle courses using this interface and a conventional transmitter. Body-motion control reduced completion time by 19.4% and centroid path length by 7.0%, and increased path directness. Delivered-command variation was 88.8% lower, and concurrent command changes were more frequent. These command measures characterize the complete interfaces, which differed in calibration and filtering. No differences were detected in gate-centering error, collection yield, crash or disconnection counts, overall workload, or usability. All participants reported higher physical demand with body-motion control. The implemented interface therefore improved FPV navigation efficiency at the cost of greater physical demand.

Yang Chen, Darius Giannoli, Dario Floreano

Laboratory of Intelligent Systems, École polytechnique fédérale de Lausanne (EPFL), Lausanne, Switzerland

Swarm Robotics UAV Navigation Robot Teleoperation

Sep 16, 2026cs.AI

AeroWeaver: An Embodied-Agent Harness for Weaving Aerial Skills into Distributed, Adaptive Swarm Execution

Collective intelligence is a collaborative autonomy paradigm in which multiple agents pursue shared objectives through local perception, information exchange, and coordinated action. UAV swarms embody this paradigm by coordinating multiple vehicles in tasks such as search, inspection, and tracking. Recent advances in large language model (LLM) agents have strengthened natural-language task understanding and high-level planning, providing a flexible semantic interface between mission descriptions and collective behavior. While these advances expand semantic reasoning, applying LLM agents to UAV swarms raises challenges in grounding model decisions in executable capabilities, reconciling global task reasoning with distributed execution, and using mission-specific experience for continual adaptation. To address these challenges, we introduce AeroWeaver, an embodied-agent harness that weaves individual UAV skills into coordinated mission-level behavior. AeroWeaver connects semantic decisions to governed skills, organizes role-conditioned local agents for distributed coordination, and uses role-indexed state-action-reward experience to refine skill selection online. Experiments and runtime validation show that AeroWeaver maintains valid skill execution under tested conditions and supports body-local multi-UAV operation without a central agent generating joint actions from global context, while reward-guided online updates provide a training-free path for adaptive learning swarm agents from accumulated execution experience. Code: https://github.com/Admire-ljb/AeroWeaver.

Jiabin Lou, Yirong Yang, Haopeng Wang +6

Beihang University, 37 Xueyuan Road, Haidian District, Beijing 100191, China

Aerial Robotics Swarm Robotics LLM Agent Skill Learning Multi-Robot Coordination LLM Agent Harnesses

Sep 16, 2026cs.MA

CC-OPI: Online Distributed Task Allocation for UAV Swarms under Communication Constraints

In multi-robot missions such as post-disaster search and rescue, a short communication range fragments a swarm of Unmanned Aerial Vehicles (UAVs) into transient information islands. Under such intermittent connectivity, the prevailing "allocate-then-execute" paradigm--which requires global consensus before any physical movement--breaks down. This paper proposes the Communication-Constrained Online Performance Impact (CC-OPI) algorithm, an event-driven method that interleaves task negotiation with physical execution. CC-OPI replans only at discrete physical and topological events and integrates two further elements. The first is a pair of cost-evaluation metrics adapted to dynamic topologies--one with a spatial locality penalty that promotes regionalized operation, the other with a deadline-aware urgency term--complemented by a non-preemptive state lock that shields each UAV's ongoing action. The second is a decentralized fault-tolerance layer that pairs version-based state synchronization with a global-time-driven emergency pool. We establish that CC-OPI terminates in finite time, free of stale-completion deadlock and of unbounded reassignment within the mission horizon. In simulations at a 250 m communication radius, CC-OPI sustains a task completion rate of about 0.80: it leads a matched online execution of the unmodified Performance Impact (PI) and Consensus-Based Bundle Algorithm (CBBA) rules by about seven percentage points, exceeds the naively transferred static baselines by roughly 20 points, and remains within several points of PI and CBBA under full connectivity. Within the tested settings, CC-OPI degrades gracefully as connectivity weakens and absorbs packet loss, terrain occlusion, and runtime task arrival. The price is more messages and some redundant travel--a deliberate trade-off of efficiency for robustness.

Biao Liu, Tong Zhang

Swarm Robotics Multi-Robot Task Allocation Multi-Agent Communication

Sep 15, 2026cs.RO

Gaussian Processes for Modelling Spatial Fields with Robot Swarms

Robot swarms, by virtue of their decentralised architecture, are a natural tool for scalable, robust modelling of spatial fields, such as water temperature, wind velocity, or terrain elevation. However, existing methods rely on external positioning systems that allow each robot to determine its own position in space. Here, we introduce location-unaware Gaussian process regression (LU-GPR) as a solution to the modelling of spatial fields in the absence of such positioning systems. LU-GPR allows each robot to infer the posterior mean and variance of the field in space, while simultaneously agreeing on a common frame of reference with its peers, using only local sensing and communication. We propose an online algorithm that allows each robot to consistently infer local estimates as its local frame of reference converges to the common one. By means of a product of experts model, each robot also combines the estimates of its peers with its own to obtain a global model. Our results show that LU-GPR scales well with the number of robots and is robust to limited communication ranges. We also demonstrate how it can be used in real-world monitoring scenarios to estimate the flow of an evacuating crowd.

Guillermo Legarda Herranz, Gianpiero Francesca, Mauro Birattari

IRIDIA, Universit´e libre de Bruxelles, 1050 Brussels, Belgium · Toyota Motor Europe

Swarm Robotics Gaussian Process Regression

Sep 15, 2026cs.MA

Calibrate Once, Fly Any Team: Residual-Grounded Low-Fidelity Training for Cooperative Drone Swarms

Training multi-agent drone-swarm policies directly in high-fidelity (HF) rigid-body physics is accurate but computationally expensive. This cost scales poorly with team size, as each additional agent multiplies contact-resolution complexity and sharply raises the in-simulation crash rate. To address this, we propose a mixed-fidelity training scheme that eliminates HF reinforcement learning entirely. A single shared, decentralized policy is optimized inside a fully-differentiable, JAX-native low-fidelity (LF) point-mass simulator. The simulator is corrected by a small, per-agent bagged residual ensemble fit once, offline, using short calibration flights in the HF simulator. Because calibration requires only one isolated drone, the data collection budget does not compound with team size. Reference trajectories are generated by rolling out an existing LF-only policy and tracked in the HF simulator by a zero-training PD controller. Evaluated across four cooperative drone tasks and team sizes from 3 to 18, the residual-corrected policy outperforms an uncorrected LF baseline in all combinations, and a from-scratch HF policy in 22 of 24 combinations tested. It trails an HF-finetuned policy by a margin that narrows steadily with team size. Ultimately, the proposed method achieves near-equivalent performance at the largest team sizes at a fraction of the computational cost, completely avoiding the high crash rates typical of HF training.

Maxim Mednikov, Oren Gal

Swarm & AI Lab (SAIL), University of Haifa, Israel

Aerial Robotics Swarm Robotics Residual Dynamics Learning Cooperative MARL

Sep 15, 2026cs.AI

CoAdapt: An LLM-based Framework for Adaptive Collaborative Perception in IIoT Robotic Swarms

Industrial IoT environments increasingly deploy autonomous mobile robots for tasks such as material handling, product assembly, or infrastructure inspection. In such deployments, collaborative perception enables robots to share LiDAR observations and collectively construct a richer model of their environment than an individual agent could produce alone. However, industrial environments are dynamic spaces where robot positions shift continuously, network bandwidth fluctuates, and the marginal contribution of robots to perception quality varies at runtime. Existing collaborative perception approaches are designed for static participation assumptions and cannot adapt to these dynamics without sacrificing either detection precision or communication efficiency. This paper presents CoAdapt, an adaptive collaborative perception framework for IIoT robotic swarms in which a Large Language Model (LLM) serves as a runtime fusion controller, jointly deciding which robots participate in the fusion process and which fusion algorithm to apply based on the current spatial configuration and network state. The LLM reasons over structured natural language descriptions of the scene derived from raw LiDAR point clouds, requiring no taskspecific training and generalizing to unseen swarm topologies. Evaluated on the OPV2V benchmark across 25 scenarios, our approach achieves a 38% reduction in communication cost while maintaining detection precision comparable to static baseline approaches.

Houssam Hajj Hassan, Antonia Maria Masucci, Lynda Zitoune +1

L2S · Orange Innovation, Chˆatillon, France. · Universit´e Paris-Saclay, CNRS, CentraleSup´elec, Laboratoire des Signaux et Syst`emes, France.

Swarm Robotics Communication-Efficient Collaborative Perception Multi-Robot Coordination Cooperative Perception

Sep 14, 2026cs.RO

Auto-HSI: Personalized human control of a robot swarm on demand by using LLMs for online automatic code generation

This paper presents Auto-HSI, a method for generating personalized human-swarm interaction (HSI) interfaces on demand. The objective is to enable untrained operators to use natural language descriptions and gesture demonstrations to explain how they want the robots to collectively behave in response to their gestures. Based on these inputs, the code should automatically be generated for personalized state machines that will control the robots as desired, in response to the desired gesture inputs. In the developed Auto-HSI prototype, the generated code produces a personalized interface for centralized control using one- and two-handed gestures, enabling a user to teleoperate the robots' motion, formation shape, and shape deformation. We test the gesture tracking and code generation components of Auto-HSI against performance benchmarks. We then test the full Auto-HSI prototype in ``live'' operation experiments, in which real human operators centrally control 50 simulated robots in a physics-based simulator, under nominal and noisy conditions. In these experiments, robots are teleoperated to: score a goal, traverse a maze that requires shape deformation, and score two simultaneous goals by splitting into two groups. We also demonstrate a real human operator making live updates to their personalized Auto-HSI interface during operation (in simulation). Finally, we demonstrate live operation of real robots.

Alessandro Nazzari, Nathan Cerisara, Dorian Tonnis +5

IRIDIA, Universit´e Libre de Bruxelles, Brussels, Belgium · Department of Aerospace Science and Technology, Politecnico di Milano, Milan, Italy · T´el´ecom Physique Strasbourg, Universit´e de Strasbourg, France +2

Robotic Control Swarm Robotics Robot Teleoperation

Sep 14, 2026cs.CV

EECTracker: Swarm Motion Prior-Guided Feature Compensation for Airborne Optical UAV Swarm Tracking

Airborne optical tracking of uncrewed aerial vehicle (UAV) swarms is challenging due to extremely small target scales, rapid viewpoint changes, and cluttered backgrounds, which can weaken target feature responses and lead to intermittent or temporarily missing detector responses. Existing multi-object tracking methods generally depend on reliable target-specific detector responses to maintain target states and identities across frames. When such responses become unreliable, target states cannot be reliably updated and cross-frame association cues become ambiguous, resulting in fragmented trajectories and identity switches. To address this problem, we propose EECTracker, a swarm-motion-prior-guided joint detection-and-tracking framework for airborne optical UAV swarm tracking. EECTracker constructs a probabilistic swarm motion prior from reliable historical tracklets to capture the shared short-term image-plane motion tendency of the swarm and its uncertainty, providing spatial guidance for cross-frame feature compensation. Building on this prior, we introduce Energy--Entropy Consistency Activation (EEC Activation) to evaluate motion-prior-conditioned feature consistency using feature residual energy and local residual entropy. The resulting Local EEC score guides pixel-level feature compensation by enhancing motion-prior-consistent feature responses in potential target regions while suppressing inconsistent background responses. Experiments on AIRMOT and UAVSwarm show that EECTracker achieves superior overall tracking performance compared with state-of-the-art methods. Compared with the strongest competing method SCT-MOT, EECTracker improves MOTA/IDF1 by 3.89/1.79 percentage points on AIRMOT and by 2.81/1.74 percentage points on UAVSwarm, while maintaining online inference speed.

Zhaochen Chu, Tao Song, Ren Jin +2

China-UAE Belt and Road Joint Laboratory on Intelligent Unmanned Systems, School of Aerospace Engineering, Beijing Institute of Technology, Beijing, 100081, China

Swarm Robotics Aerial Object Detection Multi-Object Tracking

Sep 11, 2026cs.RO

Distributed Stochastic Optimal Control for Pattern-Oriented Swarms

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.

Qingrui Zhang, Chenghao Yu, Feng Xue +1

School of Aeronautics and Astronautics, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, P.R. China

Multi-Robot Formation Control Swarm Robotics Stochastic Optimal Control Robot Collision Avoidance Emergent Behavior in Multi-Agent Systems

Sep 11, 2026cs.RO

Freehand Sketching for End-User Programming of Robot Swarms

Robot swarms are increasingly used in applications where accessible interaction with non-expert users is desirable. This paper investigates freehand sketching as an end-user programming interface for specifying robot swarm geometries. Users communicate spatial intent through a drawing, while the swarm autonomously extracts target formation points, constructs a rigid formation graph, assigns robots to formation nodes, and executes distributed formation control with a guarantee against unintended reflected formations. The resulting sketch-to-swarm framework is evaluated through a human study examining the usability of freehand formation specification. Twenty participants generated

42

geometric shapes, and the interface achieved a mean System Usability Scale score of

84.25

, which conventionally indicates high perceived usability. The results support freehand sketching as an intuitive interaction abstraction for human-swarm collaboration without requiring robotics or programming expertise.

Riwa Karam, Ian Kuo, Magnus Egerstedt

Samueli School of Engineering, University of California, Irvine, Irvine, CA, 92697, USA · University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA

Multi-Robot Formation Control Swarm Robotics Robotics Human-Robot Interaction

Sep 11, 2026cs.RO

SwarmNxt: Open-source Software-Hardware Platform for Fast and Agile Aerial Swarms

Aerial robot swarms have the potential to transform time-critical safety, security, and search-and-rescue operations. By coordinating multiple robots, they can rapidly survey disaster sites, map collapsed or GPS-denied environments, and search cluttered areas faster than a single robot, reducing response times and minimizing risks to first responders. Realizing this potential, however, requires robust autonomous swarm navigation, which remains an active research challenge. Progress is further constrained by existing platforms, as commercial drones are often closed-source or lack the onboard computational resources needed for agile, vision-based collective flight. Moreover, developing, deploying, and maintaining software across multiple aerial robots requires significant engineering effort. To address these challenges, we present SwarmNxt, an open-source software platform built on the open-source OmniNxt drone hardware. SwarmNxt provides an end-to-end toolkit, including detailed hardware assembly instructions with a video tutorial, automation tools for parallel software deployment and swarm-wide updates, and a ROS 2-based framework for autonomous navigation. The platform integrates state-of-the-art control, planning, and depth estimation into a single ROS 2 multi-agent system, providing an open research infrastructure for physical swarm experimentation. We validate SwarmNxt through two real-world experiments: a six-drone swarm performing decentralized planning with high-speed inter-drone collision avoidance, and a four-drone swarm executing collective flight with onboard depth estimation in an obstacle-filled environment. Both experiments were run indoors with global position from external motion capture; perception, planning, and control run onboard.

Charbel Toumieh, Niel Mistry, Benjamin Jarvis +4

Laboratory of Intelligent Systems, Ecole Polytechnique Federale de Lausanne (EPFL), CH1015 Lausanne, Switzerland · Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Hong Kong

Aerial Robotics Swarm Robotics

Sep 1, 2026cs.RO

Autonomous robotic bridging using distributed swarm control without inter-agent communication

We describe SCARAB--Swarm-Capable Autonomous Robotic Aquatic Bridging. Using distributed swarm control and multi-model sensing of agents and docking targets, agents can localize themselves, join into formations and proceed to desired target locations. Our technologies would eventually allow the Army to perform unpredictable, dispersed river crossings, enhance crew survivability, and minimize the logistics footprint compared to the current Improved Ribbon Bridge. Our methods operate without GPS or RF communications, though these can be used in non-contested environments (e.g., civilian disaster relief for flooding, etc.). We demonstrate our system via physics-engine-based simulation of several agents using unmanned surface vehicles (USVs) in the presence of currents and wind.

Vishwaak C. Thamaraiselvan, Cody L. Lundberg, Michail Theofandis +2

The University of Texas at Arlington Research Institute (UTARI), Fort Worth, TX

Multi-Robot Formation Control Robotic Construction Swarm Robotics Multi-Robot Coordination

Aug 6, 2026cs.RO

SyncSBC: Decentralized Swarm Behavior Prediction for Synchronized Autonomous Control

Robot swarms utilize many independent limited-sensing agents to produce complex emergent behaviors without requiring centralized control. However, little research explores how agents can infer swarm-level behavior from purely local perception, a capability critical for detecting faults and behavior changes. In this paper, we introduce Synchronized Swarm Behavior Classification (SyncSBC), which combines improvements in machine learning and distributed consensus to classify collective swarm behavior and synchronize swarm decision-making in an entirely decentralized manner. We show that SyncSBC achieves high classification accuracy and low synchronization delay, making it suitable for real-world deployment. Finally, we use SyncSBC to demonstrate two promising swarm applications on real robots where we show that swarms utilizing SyncSBC can accurately identify anomalies in robot behavior and autonomously coordinate collective changes in swarm behavior. Videos, code and supplemental experiments are available at https://sites.google.com/view/sync-sbc/home.

Varun Raveendra, Connor Mattson, Daniel S. Brown

Kahlert School of Computing, University of Utah, Salt Lake City, USA

Swarm Robotics Multi-Robot Coordination Emergent Behavior in Multi-Agent Systems

Aug 5, 2026cs.RO

Enabling Urgency-aware Robot Swarm Intralogistics using Smart IoT Tags

Warehouse items differ in how urgently they must be moved: perishable goods, pharmaceutical shipments, and just-in-time production materials must be delivered sooner than the rest of the stock. Decentralised robot swarms suit warehouses that cannot justify fixed automation infrastructure, but current swarm controllers treat all items alike or rely on an external scheduler to set priorities, so urgent items wait as long as ordinary ones. This paper presents a swarm logistics system in which each warehouse carrier holds an ultra-low-power Internet-of-Things (IoT) tag that broadcasts the urgency of its item over Bluetooth Low Energy (BLE). Robots read these broadcasts directly and weigh urgency against travel distance when choosing which carrier to serve, so prioritisation happens at the item level without central scheduling. The system is evaluated in simulation and validated on real robots and IoT-tagged carriers against a proximity-only baseline. In the physical trials, priority alignment (i.e. proportion of urgent items served first), improved from 0.41 to 0.64, with a nonsignificant trend toward lower 95th-percentile (P95) delivery latency and throughput within 1.2% of the baseline. In simulation, the benefit grew with system size: across three larger configurations, P95 latency fell by 5.2% to 11.8% and priority alignment improved by 41.7% to 51.6%. Attaching urgency to the items themselves therefore allows a decentralised swarm to serve time-critical stock sooner while keeping the low infrastructure requirements that make swarm systems attractive for warehouse automation.

Youssef Alboraei, Murray Groves, Shane Wen +6

School of Engineering Mathematics and Technology · Bristol Robotics Laboratory · University of Bristol, UK +5

Swarm Robotics Robotics Multi-Robot Task Allocation

Aug 3, 2026cs.RO

DeRP: An Algorithm for Self-Assembly of Power-Delivery Networks using Recursive Branching in Information-Limited Environments

Delivering sustained power to distributed equipment in unstructured field environments using pre-planned wired networks or battery-based solutions presents significant infrastructure and logistics challenges. This paper presents Dendritic Recursive Pivoting (DeRP), a decentralized framework for multi-target network formation in robot swarms based solely on local communication and bearing-based sensing toward sinks. We envision a system in which robots, acting as a conduit, self-assemble a power network from a common source, forming branches at locally selected pivot points that approximate the Steiner points of Steiner trees to efficiently route to multiple Sinks. This branching operation is performed recursively to enable scalable and adaptive network formation without global planning. The proposed method is evaluated in terms of the total network length and estimated power loss, and is quantitatively compared against global baselines such as the Minimum Spanning Tree and Steiner tree solutions (GeoSteiner), which require complete knowledge of Sink locations. Specifically, we found that the networks formed by DeRP asymptotically form approximately 125% of the global minimum length while reducing power losses to 65% relative to Euclidean Steiner trees. In addition, we empirically characterize scaling behavior by measuring simulation completion time as the number of Sinks and robots increases, and find that this scaling was sub-linear for up to 100 sinks. The proposed approach enables resilient, adaptive power delivery in environments where deployment of traditional infrastructure is challenging.

Mohammadali Rashidioun, Sangwoo Park, Petras Swissler

department of Mechanical and Industrial Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA

Multi-Robot Systems Swarm Robotics Combinatorial Optimization Power Systems

Aug 3, 2026cs.RO

Contact-Driven Localization in a Freeform Robotic Self-Assembled Structure

Accurate localization remains a key challenge in swarm robotics, particularly for self-reconfigurable systems that must identify relative positions to form diverse structures. Most existing approaches rely on external tracking infrastructure or high-cost sensors, which limit scalability and deployment in unstructured environments. In this paper, we propose a novel contact-driven localization method for modular robots that leverages only local communication through binary contact information (whether two robots are physically connected or not). To exploit these contact cues, we introduce a virtual-force framework in which robots iteratively refine their poses attracting toward dock-connected neighbors and repelling from non-connected ones. The method requires no external infrastructure and relies only on minimal onboard sensing. Simulations show effective localization during the assembly of towers and cantilevers, enabling accurate, scalable, free-form self-assembly.

Mohammadali Rashidioun, Michael Sosa, Petras Swissler

Institute of Technology · Industrial and Mechanical Engineering Department, New Jersey Institute of Technology

Modular Robotics Swarm Robotics Robotics Cooperative Localization Robotic Assembly Robot Localization

Jul 28, 2026cs.RO

Decentralized Scalable Exploration via Emergent Adaptive Lévy Walks on Minimal-Sensing Platforms

Efficient autonomous exploration with palm-sized nano-UAVs remains challenging due to severe limitations in sensing, computation, and flight endurance. We present a lightweight sensor-driven Lévy walk (SDLW) controller for aerial robots weighing under 50 grams and equipped with sparse local sensing. The method combines discrete Lévy step-length sampling with a sensor-reactive heading policy using directional range measurements. Each robot independently samples its Lévy exponent from a uniform prior to diversify exploration without inter-robot communication for exploration control. Each robot then selects headings using a von Mises distribution that biases motion toward open directions while preserving superdiffusive exploration properties. The controller operates at constant computational cost, enabling scalable multi-UAV exploration. Simulation results show coverage improvements of 79.6% in open arenas, 43.1% in rooms-and-corridors layouts, and 13.6% in cluttered environments, with collision reductions of 13.0%, 7.1%, and 1.4%, respectively, relative to a uniform-heading Lévy walk baseline. This work provides a practical framework for scalable multi-robot exploration on minimal-sensing, resource-constrained nano-UAVs.

Wai Lun Leong, Teo Swee Huat Rodney

National University of Singapore, Singapore

Aerial Robotics Swarm Robotics Multi-Robot Exploration

Jul 26, 2026cs.CR

Mission-Level Runtime Assurance for LLM-Assisted ISR Swarms over a Verification-Aware Fabric

Swarms of LLM-assisted autonomous robots are increasingly proposed for cooperative intelligence, surveillance, and reconnaissance (ISR) in contested environments. A growing class of their assurance failures arises not within any single platform but across the swarm: individually-compliant actions compose into a mission-level violation: a prohibited objective split across platforms to evade per-platform lim- its, or a collective budget quietly exceeded. Per-platform guardrails miss these by construction, and contested communications let the violation hide behind lost or delayed evidence. We present a three-tier (platfor- m/squad/mission) compositional runtime-verification framework that de- composes a mission policy into per-agent and cross-agent aspects, aggre- gates per-platform verdicts over a verification-aware messaging fabric, and fuses them with an evidence-aware, two-axis (security x complete- ness) algebra whose provenance names the platforms that jointly trig- gered a violation. Because the fabric makes evidence loss and silence observable, unsupported negative verdicts are downgraded to an explicit unknown rather than reported as mission-wide all-clears. On a simulated ISR mission, an indirect prompt injection that causes real LLM planners to split a prohibited collection task across four platforms is invisible to every per-platform monitor yet detected compositionally with full prove- nance; under an injected fault campaign a best-effort central monitor emits silent false all-clears while the verification-aware fabric emits none

Nikolaos Kekatos, Panagiotis Katsaros, Alexios Lekidis +2

Clone Systems, Larnaca, Cyprus · Aristotle University of Thessaloniki, Thessaloniki, Greece · University of Thessaly, Larissa, Greece

Swarm Robotics AI Agent Monitoring LLM Agent Safety Multi-Agent System Security Runtime Verification

Jul 22, 2026cs.RO

Decentralized UAV Swarms for Ground Target Protection in GPS- and Communication-Denied Environments

The presence of UAVs in military operations has recently increased, also increasing the demand for defense systems against UAV attacks. UAVs can also be used as countermeasures. Most available methods rely on UAV-to-UAV communication and global positioning. However, such resources may not be available in modern warfare scenarios. To address these limitations, we propose a pipeline for ground-target protection against UAV attacks that employs autonomous swarms of UAVs. We assume a communication- and GPS-denied environment in which the UAVs use onboard sensors to track the target and coordinate as a swarm. We developed Kalman filters to estimate the states of unknown targets and the positions of UAVs in the swarm using only relative measurements. Also, our strategy is to encircle the target of interest to maximize coverage. To achieve that, we propose a decentralized swarm encirclement technique that adapts to the target's motion. Our approach was extensively validated using real robots, demonstrating its effectiveness in detecting, encircling, and intercepting hostile UAVs.

Dimitria Silveria, Paulo Ricardo Marques de Araujo, Tiago Nascimento +1

Department of Electrical and Computer Engineering, and the Ingenuity Labs Research Institute, Queen’s University, Kingston, ON, Canada · School of Computing and the Ingenuity Labs Research Institute, Queen’s University, Kingston, ON, Canada · Lab of Systems Engineering and Robotics (LASER), Department of Computer Systems, Universidade Federal da Para´ıba, and with the Department of Cybernetics, Czech Technical University in Prague, Prague 16629, Czechia

Swarm Robotics Multi-Robot Coordination

Jul 15, 2026cs.RO

Stochastic Filtering for Quorum Sensing in Robot Swarms under Anonymous Communication

Quorum Sensing (QS) is a key capability for robot swarms, useful for coordination of activities at the group level. Effective communication is instrumental for individuals to estimate the quorum level of the entire swarm. Anonymous communication protocols where individuals exchange local information without revealing unique identities are helpful to support quorum estimates by sampling information from neighbours and maintain scalability of the QS process. However, because anonymous protocols cannot distinguish message sources, repeated messages from the same sender may be double-counted, thereby biasing collective quorum estimates. In this study, we introduce a stochastic filtering protocol inspired by

k

-priority sampling to improve estimate stability (\ANTk), and we compare it with a baseline anonymous protocols (\AN) and a randomised variant designed to improve accuracy (\ANT). We find that the baseline protocol \AN provides a parsimonious and fast solution, but remains highly inaccurate due to double-counting bias. The \ANT variant improves accuracy but suffers from information inertia, resulting in slower convergence. Finally, actively filtering the message buffer via the \ANTk protocol successfully decreases temporary errors and stabilises the estimate, at the cost of an increased time of recovery from errors.

Fabio Oddi, Andreagiovanni Reina, Vito Trianni

DIAG, Sapienza University of Rome, Italy · ISTC, National Research Council, Rome, Italy · Centre for the Advanced Study of Collective Behaviour, University of Konstanz, Konstanz, Germany +2

Multi-Robot Systems Swarm Robotics

Jul 14, 2026cs.RO

Unveiling Complex Collective Behaviors from Simple Rewards

Multi-agent Reinforcement Learning (MARL) holds great potential for robot swarms, but the black-box nature of neural policies complicates strategic analysis, limiting multi-robot applications. Furthermore, complex swarm behaviors can surprisingly emerge from simple rewards without explicit aggregation incentives. Unveiling the mechanisms behind this emergence is critical, but the disconnection between simple rewards and collective behaviors exacerbates interpretability challenges. This paper aims to reveal the hidden mechanisms in this process. We propose a two-stage EEC (\LinkIII) explanatory framework. This includes a novel analytical tool called the Agent Response Map (ARM), which reveals agents' decision-making patterns across space and identifies regions of aggregation and avoidance. ARM reveals that the robots implicitly learn the geometric fields of the environment and utilize these structures as desired targets for coordinated movement. We validate this finding across two distinct tasks: a cooperative multi-robot shape assembly and a competitive predator-prey pursuit-evasion. 1) In the cooperative task, ARM identifies the unoccupied target interior as the desired destination for robot navigation. As the center becomes occupied, this target region automatically shifts toward the boundary, demonstrating the robots' capacity to autonomously explore unoccupied areas. 2) In the competitive task, ARM surprisingly identifies the boundary of the predators' Voronoi diagram as the convergence destination for prey agents. Together, these two tasks demonstrate the capability of ARM to discover the hidden geometric structures underlying MARL policies in robot swarms.

Yize Mi, Jianan Li, Liang Li +1

College of Computer Science and Technology, Zhejiang University, Hangzhou, China. · WINDY Lab, School of Engineering, Westlake University, Hangzhou, China · Shanghai AI Laboratory, 200030, Shanghai, China +4

Swarm Robotics Multi-Robot Coordination Multi-Agent Systems Cooperative MARL Multi-Agent Reinforcement Learning Emergent Behavior in Multi-Agent Systems

Jul 14, 2026cs.RO

Infra-Swarm: Robust Vision-Based Multi-Robot Swarming via Near-Infrared Spectral Vision

Distributed swarms typically rely on either active wireless communication or passive vision, and they are frequently hindered by bandwidth constraints or environmental sensitivity. This paper proposes Infra-Swarm, a robust vision-based swarm. Each robot is equipped with a near-infrared light source and four ordinary gray-scale cameras. The Infra-Swarm system directly measures the centimeter-level 3D position of neighbors based on the position (bearing) and intensity (strength) of optical flares in the captured images. By utilizing 940 nm narrow-band filters to physically reject 99.2% of ambient light interference, the perception front-end achieves hardware-level robustness against illumination variations. Furthermore, its minimal computational overhead provides a resilient foundation for the massive scalability of robotic collectives on resource-constrained hardware.

Haoyu Chen, Qijin Li, Wanyu Xiang +3

WINDY Lab in the School of Engineering at Westlake University, Hangzhou, China · The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China · Zhejiang University, Hangzhou, China

Multi-Robot Systems Swarm Robotics Multi-Robot Coordination Robotic Perception

Jul 12, 2026nlin.AO

Self-Healing Coordination in Cognitive Swarm Agents with Bloch-Type Perceptual Memory

Reactive flocking models usually map current local observations directly to motion, leaving limited room for internal perceptual state to shape recovery after disruption. Building on a non-Markovian collective-motion model based on self-regulated perceptual dynamics, we ask whether the Bloch-type slow-fast architecture can support self-healing coordination in cognitive swarm agents. Each agent carries a bounded Bloch-type perceptual register coupled to a slow regulatory state. The slow state is not treated as a standalone memory store; here, perceptual memory is used operationally to denote history-dependent cue resolution within the closed slow-fast loop. The Bloch update is a positivity-preserving effective dynamics for internal perceptual alternatives, not a microscopic quantum claim. We evaluate the architecture in a non-periodic, obstacle-rich drone migration task with finite speed, bounded turning, collision avoidance, altitude regulation, and a fixed migratory drive. Multi-seed ablations compare the full slow-fast architecture with memoryless and partial-feedback baselines using recovery time, largest-cluster restoration, polar order, local coherence, collision risk, and path efficiency. Results show that the main functional impact is on self-healing: after obstacle-induced fragmentation, the closed slow-fast loop accelerates restoration of spatial connectedness, whereas an uncoupled slow trace behaves like a memoryless controller.

Jyotiranjan Beuria

IKS Research Center, ISS Delhi, India

Multi-Agent Coordination Multi-Robot Systems Cognitive Modeling Swarm Robotics Multi-Agent Collaboration Multi-Robot Coordination