multi-robot coordination

Multi-robot task allocation faces three key challenges: task priority constraints, intra-task coordination, and coalition-based collaboration among heterogeneous robots. Method: This paper proposes an online iterative reallocation algorithm that jointly models task dependencies via a task dependency graph and captures coalition-scale efficiency gains through a coalition-size effect model. To address the underlying NP-hard optimization problem, the method employs a network-flow-based approximation, validated via mixed-integer programming and greedy heuristics. High-fidelity simulation integrates realistic robot dynamics and physics-based modeling. Contribution/Results: Compared to offline approaches, the algorithm significantly improves robustness against task failures and model uncertainties. Experimental evaluation—across both stochastic task settings and real-world mission scenarios—demonstrates superior plan quality, enabling effective modeling, dynamic replanning, and coordinated execution of complex multi-robot missions.

This study addresses the absence of a unified and widely accepted formalism for specifying robotic tasks, which hinders non-experts from defining single- or multi-robot missions in complex, dynamic environments. For the first time, it systematically compares four prominent task specification paradigms—Behavior Trees, Finite State Machines, Hierarchical Task Networks (HTN), and Business Process Model and Notation (BPMN)—from the perspective of task-level description. The evaluation focuses on expressiveness, control structures, tooling support, and integration with human workflows. Through expert validation, the work clarifies the strengths, limitations, and suitable application contexts of each approach, offering researchers and practitioners a principled basis for method selection to enhance the robustness and adaptability of robotic task systems.

To address the challenge of ensuring temporal consistency for periodic Linear Temporal Logic (LTL) tasks in multi-robot systems under actuation delays and scalability constraints, this paper proposes a hierarchical planning framework integrating offline synthesis with online coordination. It employs distributed model checking for scalable initial task allocation and couples it with an event-driven synchronization protocol—implemented atop ROS 2—and a dynamic replanning mechanism to guarantee strict temporal alignment and real-time adaptability. The key innovation lies in unifying state-space abstraction, task decomposition, and delay-aware synchronization within a single LTL-based multi-agent coordination framework—the first such integration in the literature. Experimental results on physical robots demonstrate a 32% increase in task success rate and a 57% reduction in computational overhead for a 9-robot system. Simulation further validates real-time performance at scale, sustaining responsiveness with up to 90 agents—significantly outperforming existing LTL-based cooperative approaches.

To address the scalability challenge in large-scale multi-robot systems for complex temporal task allocation and coordinated control, this paper proposes a hierarchical planning framework grounded in Signal Temporal Logic (STL). The method explicitly encodes STL constraints over discrete path assignments and progress variables—rather than continuous states—to avoid combinatorial explosion. It integrates sampling-based single-robot path planning, STL semantic encoding, mixed-integer linear programming (MILP) for global optimization, and distributed local trajectory tracking. Evaluated in simulations with up to 100 robots, the approach efficiently synthesizes trajectories satisfying stringent spatiotemporal logic specifications (e.g., “sequentially visit region A within 5 seconds, while at most two robots occupy region B at any time”). The framework ensures global task satisfaction while achieving both scalability and real-time tractability.

This paper addresses the dynamic deployment problem for multi-robot systems under communication connectivity constraints: robots must collaboratively reach points of interest (primary targets) to gather information and relay data reliably to a static base station via relay nodes; post-task, robots must be reassignable to new targets. We propose a two-stage optimization framework. In Stage I, we jointly generate a connected topology—incorporating both primary targets and relay positions—via clustering and tree-based modeling. In Stage II, a heuristic scheduling algorithm optimizes intra-cluster target assignment and visit sequencing. Our method explicitly enforces connectivity constraints, supports dynamic redeployment, and scales efficiently with system size. Experiments across varying robot counts and large-scale target sets demonstrate that the approach rapidly yields high-quality suboptimal solutions, significantly improving deployment efficiency and system scalability.

To address the challenges of collaborative planning and dynamic scheduling for heterogeneous robot swarms in multi-task scenarios, this paper proposes a centralized closed-loop control framework. Methodologically, we design a modular autonomous stack that integrates large language models (LLMs) for open-world task decomposition and semantic reasoning; construct a shared declarative global state model enabling bidirectional communication and real-time re-planning; and adopt containerized deployment with a distributed communication architecture to ensure scalability. Our key contribution is the first deep integration of LLM-driven semantic reasoning and declarative world modeling into heterogeneous multi-robot coordination systems, significantly lowering development complexity. Experiments demonstrate substantial improvements in task allocation efficiency, system robustness, and re-planning response latency. The implementation is open-sourced to facilitate rapid integration and extensibility.

In high-density industrial environments, heterogeneous multi-robot systems are prone to path conflicts, increased waiting times, and congestion propagation due to communication delays and execution uncertainties. This work proposes the SCALE framework, which innovatively integrates robot motion characteristics into conflict resolution and constructs a generalized conjugate action-priority hypergraph (CAPH) to dynamically adjust robot priorities, enabling online generation of feasible paths and adaptive coordination. Leveraging a reactive architecture combined with an adaptive scheduling algorithm, the approach significantly reduces congestion propagation and waiting times in both simulations and a three-day real-world warehouse deployment, thereby enhancing coordination efficiency and execution robustness of heterogeneous robot fleets in complex operational scenarios.

This work addresses the challenges of coordination complexity and environmental coupling that often hinder multi-robot systems in applications such as healthcare, exploration, and rescue. The study proposes and implements the first real-world multi-robot service prototype based on Aggregate Programming (AP), leveraging neighborhood-based communication to construct a distributed coordination framework that supports environmental adaptability and fault tolerance. By integrating both simulation and physical robot experiments, the approach is validated in a realistic university library setting, demonstrating its feasibility, robustness, and scalability. This research marks the first successful deployment of aggregate programming in an actual multi-robot system, establishing a novel paradigm for distributed robotic collaboration.

Existing human-robot collaborative design frameworks lack temporal coordination reasoning support for dynamic, unstructured environments. Method: This paper proposes a networked computational framework integrating functional modeling and graph-theoretic representation. It explicitly models the temporal evolution of joint tasks, environmental constraints, and coordination requirements, enabling qualitative and quantitative co-analysis of coordination strategies at the conceptual design stage for the first time. The approach combines functional modeling, graph-theoretic modeling, temporal analysis of coordination requirements, and case-driven exploration of the trade-off space using post-disaster robotics scenarios. Results: Experiments demonstrate that the framework effectively identifies critical collaborative capabilities, uncovers temporal patterns in coordination overhead, and significantly enhances systematic early-stage reasoning about human-robot cooperation requirements—overcoming limitations of traditional static or real-time frameworks in temporal coordination modeling.

This work addresses the challenge of prolonged makespan in multi-robot collaborative disassembly within confined spaces, where motion conflicts frequently occur. The authors propose CoMuDi, a novel approach that deeply integrates spatiotemporal RRT* (ST-RRT*) into multi-robot disassembly planning for the first time. CoMuDi models the assembly using a dependency graph to generate composite tasks, propagates temporal constraints to coordinate robot actions, and leverages ST-RRT* to optimize the execution time of individual tasks, thereby minimizing overall makespan. Evaluated across six benchmark scenarios involving up to 49 parts and nine robots, CoMuDi significantly improves planning success rates while effectively reducing both makespan and robot idle time.