Resilient Multi-Robot Coordination

Latest papers

11 papers4 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Oct 20, 2026

Safe Multi-Robot Collaboration With Uniform Exclusion Using Trust

Robot swarms coordinate through directed, time-varying local communication links, some of which may be compromised to allow the transmission of arbitrary malicious data. We provide an algorithm that acts as a wrapper around robot communication---when a robot seeks to update its state using data from some incoming links, our wrapper provides a subset of trusted links whose data can be safely incorporated. It selects these links by testing a stream of trust values---data collected from an anomaly detector on each robot---using Safe Anytime-Valid Inference. We require only that the detector be better than chance. This wrapper ensures safe and performant autonomy by providing two powerful statistical guarantees: it uniformly excludes all compromised links for all time with high user-chosen probability, but nevertheless quickly learns to trust legitimate links. Both guarantees are vital to secure generic tasks, but no previous work provides them simultaneously without restrictive assumptions on connectivity structure or the number of compromised links that are not guaranteed in practice. We study our Safe Wrapper's performance in hardware for two contrasting tasks---neural implicit mapping and heading consensus---deploying on 3 LoCoBots and 8 GoPiGos, respectively. We provide simulations using trust values sampled from hardware to validate that our approach scales well to swarms of hundreds of robots.

Thomas Kaminsky, Áron Vékássy, Hammad Izhar +2

School of Engineering and Applied Sciences, Harvard University, Cambridge, USA · School of Electrical, Computer, and Energy Engineering, Arizona State University, Tempe, USA

Resilient Multi-Robot Coordination Swarm Robotics

Ask PDF

Oct 9, 2026

Failure-Resilient Multi-Robot Coordination with $k$ -Connectivity Constrained Submodular Maximization

Multi-robot coordination is essential in applications such as coverage, target tracking, and information gathering, where robots must jointly select actions to maximize team performance. Submodular maximization provides an effective framework for such coordination, and resilient variants further account for robot failures or adversarial removals. However, existing methods typically overlook the impact of robot loss on the communication network, which may disconnect the surviving team and prevent continued coordination. To address this issue, we introduce Resilient

k

-Connected Submodular Maximization, which jointly considers task performance, resilience, and communication connectivity. The resulting feasible family is not a matroid, preventing the direct application of classical curvature-greedy guarantees. We overcome this challenge through a Menger-based connectivity reduction, a connectivity-aware greedy algorithm with a feasibility-preserving fallback, and a new curvature-based analysis that captures the cost of enforcing connectivity through a single factor

ρ

. Our method achieves a worst-case post-failure objective within a factor of

[\max(1-ν_f(\mathcal{I}),h(N,β))]^2 ρ\frac{1}{κ_f}(1-e^{-κ_f})

of the optimal connectivity-respecting resilient assignment, while recovering the classical unconstrained guarantee in the appropriate limits. Experiments on multi-robot target tracking validate the proposed method and characterize the practical impact of connectivity constraints.

Yoonjae Hwang, Hojune Kim, Ellen Novoseller +2

School of Computer Science, University of Southern California, Los Angeles, CA, USA · U.S. Army DEVCOM Army Laboratory

Resilient Multi-Robot Coordination Multi-Robot Coordination Submodular Optimization

Ask PDF

Oct 3, 2026

Budget-Constrained Fault-Tolerant Mutual Visibility for Autonomous Robots under the Mobility Fault Model

We investigate the mutual visibility problem for a swarm of

n\ge3

autonomous mobile robots under the budget-constrained mobility fault model. The robots are opaque, so if three robots are collinear, the middle robot obstructs the visibility between the other two. Each robot is assigned a finite movement budget, reflecting its limited energy, that bounds the total distance it may traverse during the execution. Moreover, an arbitrary number of robots may become permanently immobile due to mobility faults. The objective is to design a distributed algorithm that enables the non-faulty robots to coordinate their movements so that, within a finite time, every non-faulty robot attains unobstructed visibility of all robots in the system, including the faulty ones, while respecting the prescribed movement budget. We consider luminous robots operating under the

\mathsf{SSYNC}

model with non-rigid movements, without any agreement on their local coordinate systems, and equipped only with a {\it common fixed reference point}. We present a deterministic distributed algorithm that solves the problem despite an arbitrary number of mobility faults. The algorithm guarantees mutual visibility for the non-faulty robots, respects the movement budget of every robot, provides collision-free movements for the robots, and uses only 12 light colors.

Prakhar Shukla, Animesh Maiti, Shivam Kumar +1

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

Resilient Multi-Robot Coordination Multi-Robot Coordination

Ask PDF

Oct 3, 2026

When Robots Crash: Optimal Asynchronous Gathering at Weber Meeting Nodes

We study the optimal gathering problem over a finite set of designated meeting nodes for asynchronous, anonymous, and oblivious mobile robots on an infinite grid under crash faults. The robots have global visibility and strong multiplicity detection, but share neither a coordinate system nor chirality. The objective is to gather all non-faulty robots at a Weber Meeting Node, minimizing the total Manhattan distance from their initial positions. Up to

n-2

robots may crash permanently, and such crashes are indistinguishable from arbitrary delays. Existing approaches often rely on a designated robot to break symmetry, whose crash may block the remaining robots indefinitely. Instead, our approach enables every robot to independently select the same target from its snapshot, while target-dependent restricted shortest paths preserve the target as a Weber Meeting Node. We prove that, under strong multiplicity detection, optimal gathering is impossible from certain fully symmetric configurations. For all remaining configurations, our algorithm CrashTolerantWeberGathering() selects a unique common target, preserves its optimality throughout the execution, and allows non-faulty robots to progress without waiting for crashed robots, thereby guaranteeing gathering in finite time.

Animesh Maiti, Prakhar Shukla, Abhinav Chakraborty +1

Department of Mathematics, Indian Institute of Technology Jodhpur, Jodhpur, Rajasthan, India · Department of Mathematics, Birla Institute of Technology Mesra, Ranchi, Jharkhand, India

Multi-Robot Systems Multi-Robot Coordination Resilient Multi-Robot Coordination

Ask PDF

Sep 27, 2026

Hierarchical Multi-agent Reinforcement Learning for Warehouse Robot Coordination under Communication Loss

In this paper, we propose a hierarchical multi-agent reinforcement learning framework for coordinating robot teams in warehouse environments under communication loss. We partition the robot team into groups, with centralized coordination within each group and distributed coordination across groups. Each group uses a recurrent predictor to estimate unavailable interaction information due to communication loss. A higher-level policy then generates a compact coordination reference that conditions the local control policy within each group. A predictive safety filter evaluates and modifies the proposed controls when they violate safety constraints. Simulation results show improved task completion under communication loss, reduced communication growth as the team size increases, and safe operation in the tested scenarios.

Weihao Sun, Gehui Xu, Andreas A. Malikopoulos

Systems Engineering Program, Cornell University, Ithaca, NY 14850 USA · Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK · Applied Mathematics, Systems Engineering, Mechanical Engineering, Electrical & Computer Engineering, and School of Civil & Environmental Engineering, Cornell University, Ithaca, NY, USA

Hierarchical RL Multi-Robot Coordination Safe RL Multi-Agent Reinforcement Learning Resilient Multi-Robot Coordination

Ask PDF

Sep 1, 2026

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 Resilient Multi-Robot Coordination Multi-Robot Coordination

Ask PDF

Jul 27, 2026

Distributed Coordination for Resilient Multi-UAV Remote Sensing: A Photovoltaic Inspection Case Study

Deploying multiple UAVs for remote sensing enables proportional reductions in mission time, but realizing these benefits requires the fleet to coordinate at runtime: distributing sensing targets, responding to platform failures, and recovering from degraded data quality. In inspection campaigns, where mission value depends on complete coverage and the usability of every capture, a centralized ground-station coordinator is a single point of failure: a lost link or station fault leaves sensing gaps that cannot be filled without operator intervention. We propose the SwarmLink, an inter-agent communication infrastructure that non-invasively extends any existing aerial framework with peer-to-peer coordination capability, without modifying the host system. We apply it to photovoltaic plant inspection as a representative large-scale sensing campaign, extending Aerostack2 with a distributed auction that unifies initial sensing-target allocation, platform-failure recovery, and data-quality-triggered reassignment into a single runtime mechanism. All three disruption scenarios reduce to the same re-auction over remaining targets and active platforms, requiring zero modifications to the Aerostack2 core and no ground-station involvement during the mission.

Guillermo GP-Lenza, Miguel Fernandez-Cortizas, Martin Molina +1

Centre for Automation and Robotics C.A.R, (UPM-CSIC), Universidad Politécnica de Madrid, Spain · Department of Artificial Intelligence, Universidad Politécnica de Madrid,, Spain · Automation and Robotics Group (ARG-SnT), University of Luxembourg - SnT, Luxembourg

Resilient Multi-Robot Coordination Multi-Robot Coordination Multi-Robot Task Allocation UAV Remote Sensing

Ask PDF

Jul 1, 2026

From Real-Time Planning to Reliable Execution:Scalable Coordination for Heterogeneous Multi-Robot Fleets in Industrial Environments

With the increasing deployment of heterogeneous robot fleets in industrial environments, efficient coordination remains a critical challenge. Real-time path planning must simultaneously accommodate high robot densities and heterogeneous motion capabilities, while communication delays, execution uncertainties, and other disturbances may cause robots to deviate from the temporal assumptions underlying planned paths. Such deviations can lead to excessive waiting and congestion propagation across the fleet. This paper presents SCALE, a reactive online coordination framework that enables real-time planning while maintaining robust execution. Within this framework, we introduce a motion-induced conflict reduction mechanism to support the online generation of feasible paths for online conflict resolution. To mitigate the effects of disturbances, we further design a generalized Conjugate Action-Precedence Hypergraph (CAPH) that adaptively adjusts precedence relations among robots. Extensive validation experiments, together with a three-day deployment in a warehouse, demonstrate the

Bo Cao

Multi-Robot Motion Planning Robotics Multi-Robot Coordination Industrial Robotics Multi-Agent Path Finding Resilient Multi-Robot Coordination

Ask PDF

Jun 6, 2026

Continual Quadruped Robots Coordination via Semantic Skill Discovery

Multi-quadruped coordination has attracted increasing attention due to its enhanced payload capacity, broader contact coverage, and improved adaptability to challenging tasks. Existing methods for multi-quadruped manipulation typically focus on predefined or closed task families, often relying on multi-agent reinforcement learning (MARL) to train task-specific coordination policies. However, such methods struggle in open-ended continual learning settings, where tasks arrive sequentially and robots are expected to acquire new coordination skills while reusing previously learned ones without catastrophic forgetting. To address this challenge, we propose Conquer, a semantic skill-library framework that formulates continual multi-quadruped coordination as a retrieve-adapt-update process. First, to accommodate varying team sizes across tasks, we design a team-structured Self-Allies-Goal (SAG) backbone that supports variable-cardinality robot teams by explicitly modeling each robot's own state, teammate context, and task goal. For each incoming task, Conquer constructs a task-level semantic descriptor from pre-execution information and retrieves a relevant skill from the library for adaptation. After successful execution, Conquer updates the skill library by extracting trajectory-level semantic descriptors and organizing them according to semantic distance, thereby enabling continual skill accumulation and cross-task knowledge transfer. Simulation experiments show that Conquer achieves a final average success rate of 95.6%, demonstrating strong forward transfer and negligible catastrophic forgetting. Real-world rollouts on Unitree Go2 teams further validate the deployment feasibility of Conquer for practical multi-quadruped coordination. Simulation and real-robot demonstration videos are available at: https://conquer-project.pages.dev/.

Daoqing Wang, Yuchen Xiao, Weixuan Huang +5

National Key Laboratory of Novel Software Technology, Nanjing University, Nanjing, China · School of Artificial Intelligence, Nanjing University, Nanjing, China · Polixir Technologies, Nanjing, China

Continual Learning Resilient Multi-Robot Coordination Multi-Robot Coordination

Ask PDF

May 21, 2026

Higher Order Reasoning for Collaborative Communicationless Mobile Robot Operations

In communicationless environments, multi-robot systems must operate without the constant information exchange that many coordination strategies typically assume. This paper presents a novel dynamic epistemic planning framework that enables implicit coordination and long horizon planning through higher-order reasoning among robots. With our approach, robots form and propagate higher-order belief particles, update world beliefs using Bayesian inference, and select actions via a behavior tree that anticipates teammates' likely decisions. A temporally aware Model Predictive Path Integral (MPPI) controller integrates this reasoning into low-level execution, allowing robots to plan intercepts and adapt trajectories under partial observability. The proposed framework is evaluated in both simulations and physical experiments, where it consistently reduces task completion time compared to a first-order baseline, demonstrating that epistemic logic can serve as a robust foundation for resilient coordination in communication-restricted domains.

Jonathan Reasoner, Nicola Bezzo

Department of Electrical and Computer Engineering, University of Virginia, Charlottesville, VA 22904, USA

Belief-Space Planning Multi-Robot Motion Planning Resilient Multi-Robot Coordination Multi-Robot Coordination Multi-Agent Reasoning

Ask PDF

Apr 5, 2026

Resilient Decentralized Ergodic Coverage for Scalable Multi-Robot Systems in Unknown Time-Varying Environments

Maintaining situational awareness in high-stakes multi-robot applications requires balancing exploration of unobserved regions with sustained monitoring of changing Regions of Interest (ROIs), often under unknown and time-varying distributions, partial observability, and limited communication. We propose a decentralized multi-agent coverage framework that serves as a high-level planning strategy, in which each agent computes an adaptive ergodic policy, implemented via a Markov-chain, that tracks an updated belief over the underlying importance map. Beliefs are maintained online via Gaussian Process (GP) regression from local noisy observations exchanged with neighbors. The resulting policy drives agents to spend time in ROIs in proportion to their estimated importance, while preserving sufficient exploration to detect and adapt to time-varying environmental changes. Unlike existing approaches that assume known importance maps, centralized coordination, or a static environment, our framework addresses the combined challenges of unknown, time-varying distributions under a decentralized, partially observable setting. We further show that our framework is robust to communication and memory degradation, robot loss, and can scale up to hundreds of robots.

Maria G. Mendoza, Victoria Marie Tuck, Chinmay Maheshwari +1

Department of Mechanical Engineering, University of California, Berkeley, CA 94720, USA · Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA · Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21209, USA +1

Multi-Robot Systems Exploration-Exploitation Tradeoff Resilient Multi-Robot Coordination Multi-Robot Exploration Multi-Robot Coordination

Ask PDF

Mar 16, 2026

BodyGuards: Escorting by Multiple Robots in Unknown Environment under Limited Communication

Multi-robot systems are increasingly deployed in high-risk missions such as reconnaissance, disaster response, and subterranean operations. Protecting a human operator while navigating unknown and adversarial environments remains a critical challenge, especially when the communication among the operator and robots is restricted. Unlike existing collaborative exploration methods that aim for complete coverage, this work focuses on task-oriented exploration to minimize the navigation time of the operator to reach its goal while ensuring safety under adversarial threats. A novel escorting framework BodyGuards, is proposed to explicitly integrate seamlessly collaborative exploration, inter-robot-operator communication and escorting. The framework consists of three core components: (I) a dynamic movement strategy for the operator that maintains a local map with risk zones for proactive path planning; (II) a dual-mode robotic strategy combining frontier based exploration with optimized return events to balance exploration, threat detection, and intermittent communication; and (III) multi-robot coordination protocols that jointly plan exploration and information sharing for efficient escorting. Extensive human-in-the-loop simulations and hardware experiments demonstrate that the method significantly reduces operator risk and mission time, outperforming baselines in adversarial and constrained environments.

Zhuoli Tian, Yanze Bao, Meng Guo

Resilient Multi-Robot Coordination Human-Robot Collaboration Robot Safety Multi-Robot Coordination Multi-Robot Exploration

Ask PDF