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arxiv: 2503.10475 · v4 · submitted 2025-03-13 · 💻 cs.RO · cs.SY· eess.SY

Stratified Topological Autonomy for Long-Range Coordination (STALC)

Cora A. Duggan , Adam Goertz , Adam Polevoy , Mark Gonzales , Kevin C. Wolfe , Bradley Woosley , John G. Rogers III , Joseph Moore This is my paper

Pith reviewed 2026-05-22 23:41 UTC · model grok-4.3

classification 💻 cs.RO cs.SYeess.SY
keywords multi-robot planningtopological graphsmixed-integer programminghierarchical planningreconnaissancerisk minimizationreceding-horizon controlcoordination
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The pith

STALC combines topological graphs with mixed-integer programming to coordinate multi-robot plans across large environments.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces a hierarchical method for multi-robot teams to plan coordinated actions over long ranges and times. It builds topological graphs that link free-space regions with features like risk and traversability, then applies a mixed-integer program to create global plans quickly. Local receding-horizon controllers handle short-term collision avoidance and formation keeping. This structure is tested in a reconnaissance task where robots must avoid detection while navigating together. The approach matters because it allows scaling to complex scenarios where full central planning would be too slow.

Core claim

STALC constructs topological graphs from environment data to capture connectivity between regions and problem-specific features such as traversability or risk. A novel mixed-integer programming formulation on these graphs generates highly-coupled multi-robot plans in seconds. Receding-horizon planners then achieve local collision avoidance and formation control, allowing the team to meet shared objectives like minimizing detection risk in reconnaissance scenarios, as shown in both simulation scaling tests and hardware experiments with real-world data.

What carries the argument

The stratified topological graph combined with a mixed-integer programming formulation for generating globally consistent multi-robot trajectories.

If this is right

  • Plans for complex multi-robot scenarios can be generated in seconds.
  • Graphs can be built from real-world data for hardware deployment.
  • The hierarchy allows planning across different spatial and temporal scales.
  • Local controllers execute global plans while maintaining collision avoidance and formations.
  • Shared objectives such as risk minimization are achieved through the coupled plans.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Similar layering might apply to other tasks requiring coordination without constant communication.
  • The method could extend to environments where risk features change over time if graphs are updated periodically.
  • By separating global and local planning, the approach may lower the computational burden compared to solving one large optimization at every step.

Load-bearing premise

Topological graphs that encode connectivity and features, together with the mixed-integer program, will yield plans that receding-horizon local controllers can execute without breaking the global coordination goals.

What would settle it

A hardware experiment where robots following the generated plans are detected by observers more often than the minimized risk level predicts, or where local controllers deviate in ways that violate the planned connectivity.

Figures

Figures reproduced from arXiv: 2503.10475 by Adam Goertz, Adam Polevoy, Bradley Woosley, Cora A. Duggan, John G. Rogers III, Joseph Moore, Kevin C. Wolfe, Mark Gonzales.

Figure 1
Figure 1. Figure 1: Experimental operational scenario to minimize visibility while travers [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: STALC: Hierarchical planning architecture for a multi-robot team to autonomously coordinate while traversing an environment. The high-level planner [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Piecewise Linear Cost Functions TABLE I MIP PARAMETERS Category Var Description Problem Size nA Number of agents/robots nT Number of time steps in the time horizon nO Number of overwatch opportunities nE Number of directional edges nV Number of nodes/vertices nL Number of locations (nE + nV ) nB Number of start/begin locations nD Number of goal/destination locations Scenario Variables E Set of edges e V Se… view at source ↗
Figure 4
Figure 4. Figure 4: A visibility map: the observer is positioned in a clearing. The observer [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Regions of cover and A∗ paths overlaid on satellite imagery of the environment: cover regions are outlined in red and node numbers are located within the regions near the centroid of each region. 4) SplitRegions: For some maps and observer posi￾tions, the cover regions can be large and nonconvex. Conse￾quently, we enforce a maximum size, Ξmax, using the algorithm from [62] to find the minimum-length cut th… view at source ↗
Figure 6
Figure 6. Figure 6: The overwatch map from node 3. Pink and red shading denotes areas [PITH_FULL_IMAGE:figures/full_fig_p011_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Schematic topological graph with overwatch opportunities indicated [PITH_FULL_IMAGE:figures/full_fig_p011_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: MIP problem solution to an illustrative MCRP, sketched in (a). Ten [PITH_FULL_IMAGE:figures/full_fig_p013_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Example bounding overwatch solution as all robots move from node 1 [PITH_FULL_IMAGE:figures/full_fig_p014_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Aerial map of a meadow environment showing a sample scenario. [PITH_FULL_IMAGE:figures/full_fig_p014_10.png] view at source ↗
Figure 13
Figure 13. Figure 13: Comparison of average computation time across state-of-the-art algorithms for multi-robot planning with overwatch opportunities across various [PITH_FULL_IMAGE:figures/full_fig_p015_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Optimality gap versus the number of robots for the learning-based [PITH_FULL_IMAGE:figures/full_fig_p015_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Sequential steps in our environment segmentation approach for a simulated meadows environment, as seen in (a). In (b), we generate a visibility [PITH_FULL_IMAGE:figures/full_fig_p016_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: Graph generation for two different observer locations in an forested [PITH_FULL_IMAGE:figures/full_fig_p016_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: Schematic forested graphs showing overwatch opportunities and team routes. Both robots start at node 0 and the goal is for one robot to node 6 and [PITH_FULL_IMAGE:figures/full_fig_p017_17.png] view at source ↗
Figure 18
Figure 18. Figure 18: Robots moving in formation approaching a cover region. [PITH_FULL_IMAGE:figures/full_fig_p017_18.png] view at source ↗
Figure 21
Figure 21. Figure 21: Environment segmentation for three different observer locations in an urban environment, including a case with multiple observers. The visibility [PITH_FULL_IMAGE:figures/full_fig_p018_21.png] view at source ↗
Figure 22
Figure 22. Figure 22: Planned routes on Urban Graph 1 showing the difference in routes [PITH_FULL_IMAGE:figures/full_fig_p018_22.png] view at source ↗
Figure 23
Figure 23. Figure 23: Planned routes on Urban Graph 2 showing the difference in routes [PITH_FULL_IMAGE:figures/full_fig_p018_23.png] view at source ↗
Figure 25
Figure 25. Figure 25: Observer views for Urban Graphs 1 and 2. The traversing robots are circled in red. In (c), the tower the observer is on in Urban Graph 1 is shown [PITH_FULL_IMAGE:figures/full_fig_p020_25.png] view at source ↗
read the original abstract

In this paper, we present Stratified Topological Autonomy for Long-Range Coordination (STALC), a hierarchical planning approach for multi-robot coordination in real-world environments with significant inter-robot spatial and temporal dependencies. At its core, STALC consists of a multi-robot graph-based planner which combines a topological graph with a novel, computationally efficient mixed-integer programming formulation to generate highly-coupled multi-robot plans in seconds. To enable autonomous planning across different spatial and temporal scales, we construct our graphs so that they capture connectivity between free-space regions and other problem-specific features, such as traversability or risk. We then use receding-horizon planners to achieve local collision avoidance and formation control. To evaluate our approach, we consider a multi-robot reconnaissance scenario where robots must autonomously coordinate to navigate through an environment while minimizing the risk of detection by observers. Through simulation-based experiments, we show that our approach is able to scale to address complex multi-robot planning scenarios. Through hardware experiments, we demonstrate our ability to generate graphs from real-world data and successfully plan across the entire hierarchy to achieve shared objectives.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 0 minor

Summary. The paper proposes Stratified Topological Autonomy for Long-Range Coordination (STALC), a hierarchical multi-robot planning method that constructs topological graphs capturing free-space connectivity and problem-specific features (e.g., traversability or risk), solves a mixed-integer program over these graphs to produce coupled plans, and delegates local execution to receding-horizon controllers for collision avoidance and formation control. Evaluation is described in a reconnaissance scenario minimizing detection risk, with claims of scalability shown via simulation experiments and hardware feasibility demonstrated by generating graphs from real-world data and executing plans across the hierarchy.

Significance. If substantiated, the approach could supply a computationally tractable bridge between topological abstraction and MIP-based coordination for long-range multi-robot tasks with spatial-temporal coupling, extending standard hierarchical planning techniques to real-world settings.

major comments (1)
  1. [Abstract] Abstract: the central claims that the method 'is able to scale to address complex multi-robot planning scenarios' and 'successfully plan across the entire hierarchy' in hardware are unsupported by any quantitative results, error metrics, baseline comparisons, computation times, success rates, or derivation details on the MIP formulation or graph construction, preventing evaluation of the scalability and executability assertions.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their review and the opportunity to clarify the presentation of our results. We respond to the major comment below.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central claims that the method 'is able to scale to address complex multi-robot planning scenarios' and 'successfully plan across the entire hierarchy' in hardware are unsupported by any quantitative results, error metrics, baseline comparisons, computation times, success rates, or derivation details on the MIP formulation or graph construction, preventing evaluation of the scalability and executability assertions.

    Authors: The manuscript body (Sections V and VI) reports simulation results showing scaling with robot count and environment size, along with MIP solve times; graph construction and MIP formulation details appear in Sections III and IV. Hardware experiments are presented as a feasibility demonstration of end-to-end execution from real-world sensor data rather than a quantitative benchmark study. We agree the abstract would be strengthened by explicit reference to these quantitative elements. We will revise the abstract to note reported computation times and scaling observations from simulation while describing the hardware component strictly as a successful hierarchy-wide execution demonstration. revision: yes

Circularity Check

0 steps flagged

No significant circularity identified

full rationale

The paper describes STALC as a hierarchical method using topological graphs capturing connectivity and features like traversability, combined with a mixed-integer programming formulation for multi-robot plans, followed by receding-horizon local controllers. Scalability and validity are asserted via simulation experiments on reconnaissance scenarios and hardware tests generating graphs from real-world data. No equations, fitting procedures, self-citations, uniqueness theorems, or ansatzes are present in the text. The central claims rest on empirical demonstration rather than any closed mathematical derivation that reduces outputs to inputs by construction, rendering the approach self-contained.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Only the abstract is available; no technical sections, equations, or implementation details are present from which free parameters, axioms, or invented entities can be extracted.

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Reference graph

Works this paper leans on

66 extracted references · 66 canonical work pages

  1. [1]

    A comprehensive taxonomy for multi-robot task allocation,

    G. A. Korsah, A. Stentz, and M. B. Dias, “A comprehensive taxonomy for multi-robot task allocation,”Intl. J. Rob. Res. (IJRR), vol. 32, no. 12, pp. 1495–1512, 2013

    work page 2013

  2. [2]

    Cooperative Multi-Agent Planning: A Survey,

    A. Torre ˜no, E. Onaindia, A. Komenda, and M. ˇStolba, “Cooperative Multi-Agent Planning: A Survey,”ACM Comp. Surv., vol. 50, no. 6, Nov 2017

    work page 2017

  3. [3]

    Multi-Robot Planning on Dynamic Topological Graphs Using Mixed-Integer Programming,

    C. A. Dimmig, K. C. Wolfe, and J. Moore, “Multi-Robot Planning on Dynamic Topological Graphs Using Mixed-Integer Programming,” in Intl. Conf. Int. Rob. Syst. (IROS). IEEE, 2023, pp. 5394–5401

    work page 2023

  4. [4]

    A survey and analysis of multi-robot coordination,

    Z. Yan, N. Jouandeau, and A. A. Cherif, “A survey and analysis of multi-robot coordination,”Intl. J. of Adv. Rob. Sys., vol. 10, no. 12, p. 399, 2013

    work page 2013

  5. [5]

    Multi-robot coordination analysis, taxonomy, challenges and future scope,

    J. K. Verma and V . Ranga, “Multi-robot coordination analysis, taxonomy, challenges and future scope,”J. of Int. & Rob. Sys., vol. 102, pp. 1–36, 2021

    work page 2021

  6. [6]

    Multiagent allocation of markov decision process tasks,

    T. Campbell, L. Johnson, and J. P. How, “Multiagent allocation of markov decision process tasks,” inAmer. Contr. Conf. (ACC). IEEE, 2013, pp. 2356–2361

    work page 2013

  7. [7]

    A formal analysis and taxonomy of task allocation in multi-robot systems,

    B. P. Gerkey and M. J. Matari ´c, “A formal analysis and taxonomy of task allocation in multi-robot systems,”Intl. J. Rob. Res. (IJRR), vol. 23, no. 9, pp. 939–954, 2004

    work page 2004

  8. [8]

    A systematic literature review on multi- robot task allocation,

    A. KA and U. Subramaniam, “A systematic literature review on multi- robot task allocation,”ACM Comp. Surv., vol. 57, no. 3, pp. 1–28, 2024

    work page 2024

  9. [9]

    Cooperative pathfinding,

    D. Silver, “Cooperative pathfinding,” inAAAI Conf. on Art. Intel. and Int. Dig. Ent., vol. 1, no. 1, 2005, pp. 117–122

    work page 2005

  10. [10]

    Combined vehicle routing and scheduling with temporal precedence and synchronization constraints,

    D. Bredstr ¨om and M. R ¨onnqvist, “Combined vehicle routing and scheduling with temporal precedence and synchronization constraints,” Euro. J. of Op. Res., vol. 191, no. 1, pp. 19–31, 2008

    work page 2008

  11. [11]

    Set partitioning: A survey,

    E. Balas and M. W. Padberg, “Set partitioning: A survey,”SIAM review, vol. 18, no. 4, pp. 710–760, 1976

    work page 1976

  12. [12]

    Simple auctions with performance guarantees for multi-robot task allocation,

    M. G. Lagoudakis, M. Berhault, S. Koenig, P. Keskinocak, and A. J. Kleywegt, “Simple auctions with performance guarantees for multi-robot task allocation,” inIntl. Conf. Int. Rob. Syst. (IROS), vol. 1. IEEE, 2004, pp. 698–705

    work page 2004

  13. [13]

    Multi-robot routing under limited communication range,

    A. R. Mosteo, L. Montano, and M. G. Lagoudakis, “Multi-robot routing under limited communication range,” inIntl. Conf. Rob. Aut. (ICRA). IEEE, 2008, pp. 1531–1536

    work page 2008

  14. [14]

    Local-game Decomposition for Multiplayer Perimeter-defense Problem,

    D. Shishika and V . Kumar, “Local-game Decomposition for Multiplayer Perimeter-defense Problem,” inConf. Decis. Contr. (CDC). IEEE, Dec 2018, pp. 2093–2100

    work page 2018

  15. [15]

    Conflict-based search for optimal multi-agent pathfinding,

    G. Sharon, R. Stern, A. Felner, and N. R. Sturtevant, “Conflict-based search for optimal multi-agent pathfinding,”Art. Intel., vol. 219, pp. 40–66, 2015

    work page 2015

  16. [16]

    Finding optimal solutions to cooperative pathfinding prob- lems,

    T. Standley, “Finding optimal solutions to cooperative pathfinding prob- lems,” inAAAI Conf. on Art. Intel., vol. 24, no. 1, 2010, pp. 173–178

    work page 2010

  17. [17]

    Tabu search heuristics for the vehicle routing problem with time windows,

    O. Br ¨aysy and M. Gendreau, “Tabu search heuristics for the vehicle routing problem with time windows,”Top, vol. 10, no. 2, pp. 211–237, 2002

    work page 2002

  18. [18]

    Team Coordination on Graphs with State-Dependent Edge Costs,

    M. Limbu, Z. Hu, S. Oughourli, X. Wang, X. Xiao, and D. Shishika, “Team Coordination on Graphs with State-Dependent Edge Costs,” in Intl. Conf. Int. Rob. Syst. (IROS). IEEE, 2023, pp. 679–684

    work page 2023

  19. [19]

    Team Coordination on Graphs: Problem, Analysis, and Algorithms,

    Y . Zhou, M. Limbu, G. J. Stein, X. Wang, D. Shishika, and X. Xiao, “Team Coordination on Graphs: Problem, Analysis, and Algorithms,” in Intl. Conf. Int. Rob. Syst. (IROS). IEEE, 2024, pp. 5748–5755

    work page 2024

  20. [20]

    Heterogeneous multirobot coordination with spatial and temporal constraints,

    M. Koes, I. Nourbakhsh, and K. Sycara, “Heterogeneous multirobot coordination with spatial and temporal constraints,” inAAAI Conf. on Art. Intel., vol. 5, 2005, pp. 1292–1297

    work page 2005

  21. [21]

    Multi-robot long-term persistent coverage with fuel constrained robots,

    D. Mitchell, M. Corah, N. Chakraborty, K. Sycara, and N. Michael, “Multi-robot long-term persistent coverage with fuel constrained robots,” inIntl. Conf. Rob. Aut. (ICRA). IEEE, 2015, pp. 1093–1099

    work page 2015

  22. [22]

    A mixed integer programming model for timed deliveries in multirobot systems,

    N. Kamra and N. Ayanian, “A mixed integer programming model for timed deliveries in multirobot systems,” inIntl. Conf. on Auto. Sci. and Eng. (CASE). IEEE, Aug 2015, pp. 612–617

    work page 2015

  23. [23]

    Simultaneous task allocation, data routing, and transmission scheduling in mobile multi-robot teams,

    E. F. Flushing, L. M. Gambardella, and G. A. Di Caro, “Simultaneous task allocation, data routing, and transmission scheduling in mobile multi-robot teams,” inIntl. Conf. Int. Rob. Syst. (IROS). IEEE, Sept 2017, pp. 1861–1868

    work page 2017

  24. [24]

    Mixed-Integer Linear Programming Models for Multi-Robot Non-Adversarial Search,

    B. A. Asfora, J. Banfi, and M. Campbell, “Mixed-Integer Linear Programming Models for Multi-Robot Non-Adversarial Search,”Rob. Autom. Lett. (RA-L), vol. 5, no. 4, pp. 6805–6812, Oct 2020

    work page 2020

  25. [25]

    Optimal Multirobot Path Planning on Graphs: Complete Algorithms and Effective Heuristics,

    J. Yu and S. M. LaValle, “Optimal Multirobot Path Planning on Graphs: Complete Algorithms and Effective Heuristics,”Trans. Rob. (T-RO), vol. 32, no. 5, pp. 1163–1177, Oct 2016

    work page 2016

  26. [26]

    The graph neural network model,

    F. Scarselli, M. Gori, A. C. Tsoi, M. Hagenbuchner, and G. Monfardini, “The graph neural network model,”Trans. Neur. Net., vol. 20, no. 1, pp. 61–80, 2008

    work page 2008

  27. [27]

    Graph neural networks for decentralized multi-robot path planning,

    Q. Li, F. Gama, A. Ribeiro, and A. Prorok, “Graph neural networks for decentralized multi-robot path planning,” inIntl. Conf. Int. Rob. Syst. (IROS). IEEE, 2020, pp. 11 785–11 792

    work page 2020

  28. [28]

    Multi-robot coverage and exploration using spatial graph neural networks,

    E. Tolstaya, J. Paulos, V . Kumar, and A. Ribeiro, “Multi-robot coverage and exploration using spatial graph neural networks,” inIntl. Conf. Int. Rob. Syst. (IROS). IEEE, 2021, pp. 8944–8950. (a) Urban Graph 1: robots moving in formation on edge (4,3). (b) Urban Graph 1: robot on edge (2,0). (c) Urban Graph 2: robot on edge (5,3). Fig. 25. Observer views ...

    work page 2021

  29. [29]

    Learning Scheduling Policies for Multi- Robot Coordination With Graph Attention Networks,

    Z. Wang and M. Gombolay, “Learning Scheduling Policies for Multi- Robot Coordination With Graph Attention Networks,”Rob. Autom. Lett. (RA-L), vol. 5, no. 3, pp. 4509–4516, July 2020

    work page 2020

  30. [30]

    Heterogeneous graph attention networks for scalable multi-robot scheduling with temporospatial con- straints,

    Z. Wang, C. Liu, and M. Gombolay, “Heterogeneous graph attention networks for scalable multi-robot scheduling with temporospatial con- straints,”Aut. Rob., pp. 1–20, 2022

    work page 2022

  31. [31]

    Dynamic coalition formation and routing for multirobot task allocation via reinforcement learning,

    W. Dai, A. Bidwai, and G. Sartoretti, “Dynamic coalition formation and routing for multirobot task allocation via reinforcement learning,” inIntl. Conf. Rob. Aut. (ICRA). IEEE, 2024, pp. 16 567–16 573

    work page 2024

  32. [32]

    Heterogeneous multi-robot task allocation and scheduling via reinforcement learning,

    W. Dai, U. Rai, J. Chiun, C. Yuhong, and G. Sartoretti, “Heterogeneous multi-robot task allocation and scheduling via reinforcement learning,” Rob. Autom. Lett. (RA-L), 2025

    work page 2025

  33. [33]

    Scaling team coordination on graphs with reinforcement learning,

    M. Limbu, Z. Hu, X. Wang, D. Shishika, and X. Xiao, “Scaling team coordination on graphs with reinforcement learning,” inIntl. Conf. Rob. Aut. (ICRA). IEEE, 2024, pp. 16 538–16 544

    work page 2024

  34. [34]

    A Motion Planning Approach for Marsupial Robotic Systems,

    P. G. Stankiewicz, S. Jenkins, G. E. Mullins, K. C. Wolfe, M. S. Johannes, and J. L. Moore, “A Motion Planning Approach for Marsupial Robotic Systems,” inIntl. Conf. Int. Rob. Syst. (IROS). IEEE, 2018, pp. 1–9

    work page 2018

  35. [35]

    Multi-agent generalized probabilistic RoadMaps: MAGPRM,

    S. Kumar and S. Chakravorty, “Multi-agent generalized probabilistic RoadMaps: MAGPRM,” inIntl. Conf. Int. Rob. Syst. (IROS). IEEE, 2012, pp. 3747–3753

    work page 2012

  36. [36]

    Combining multi-robot motion planning and goal allocation using roadmaps,

    J. Salvado, M. Mansouri, and F. Pecora, “Combining multi-robot motion planning and goal allocation using roadmaps,” inIntl. Conf. Rob. Aut. (ICRA). IEEE, 2021, pp. 10 016–10 022

    work page 2021

  37. [37]

    CAPT: Concurrent assignment and planning of trajectories for multiple robots,

    M. Turpin, N. Michael, and V . Kumar, “CAPT: Concurrent assignment and planning of trajectories for multiple robots,”Intl. J. Rob. Res. (IJRR), vol. 33, no. 1, pp. 98–112, 2014

    work page 2014

  38. [38]

    LSwarm: Efficient collision avoidance for large swarms with coverage constraints in complex urban scenes,

    S. H. Arul, A. J. Sathyamoorthy, S. Patel, M. Otte, H. Xu, M. C. Lin, and D. Manocha, “LSwarm: Efficient collision avoidance for large swarms with coverage constraints in complex urban scenes,”Rob. Autom. Lett. (RA-L), vol. 4, no. 4, pp. 3940–3947, 2019

    work page 2019

  39. [39]

    Multi- robot task and motion planning with subtask dependencies,

    J. Motes, R. Sandstr ¨om, H. Lee, S. Thomas, and N. M. Amato, “Multi- robot task and motion planning with subtask dependencies,”Rob. Autom. Lett. (RA-L), vol. 5, no. 2, pp. 3338–3345, 2020

    work page 2020

  40. [40]

    Roboballet: Planning for multirobot reaching with graph neural net- works and reinforcement learning,

    M. Lai, K. Go, Z. Li, T. Kr ¨oger, S. Schaal, K. Allen, and J. Scholz, “Roboballet: Planning for multirobot reaching with graph neural net- works and reinforcement learning,”Sci. Rob., vol. 10, no. 106, 2025

    work page 2025

  41. [41]

    Decentralized multi- robot navigation for autonomous surface vehicles with distributional reinforcement learning,

    X. Lin, Y . Huang, F. Chen, and B. Englot, “Decentralized multi- robot navigation for autonomous surface vehicles with distributional reinforcement learning,” inIntl. Conf. Rob. Aut. (ICRA). IEEE, 2024

    work page 2024

  42. [42]

    Latombe,Robot motion planning

    J.-C. Latombe,Robot motion planning. Springer Science & Business Media, 2012, vol. 124

    work page 2012

  43. [43]

    Room segmentation: Survey, implementation, and analysis,

    R. Bormann, F. Jordan, W. Li, J. Hampp, and M. H ¨agele, “Room segmentation: Survey, implementation, and analysis,” inIntl. Conf. Rob. Aut. (ICRA). IEEE, 2016, pp. 1019–1026

    work page 2016

  44. [44]

    Proofs and experiments in scalable, near- optimal search by multiple robots,

    G. Hollinger and S. Singh, “Proofs and experiments in scalable, near- optimal search by multiple robots,”Rob.: Sci. and Syst., vol. 1, 2008

    work page 2008

  45. [45]

    Topological mapping for traversability-aware long-range navigation in off-road terrain,

    J.-F. Tremblay, J. Alhosh, L. Petit, F. Lotfi, L. Landauro, and D. Meger, “Topological mapping for traversability-aware long-range navigation in off-road terrain,” inIntl. Conf. Rob. Aut. (ICRA). IEEE, 2025, pp. 14 850–14 856

    work page 2025

  46. [46]

    Finding a needle in an exponential haystack: Discrete rrt for exploration of implicit roadmaps in multi-robot motion planning,

    K. Solovey, O. Salzman, and D. Halperin, “Finding a needle in an exponential haystack: Discrete rrt for exploration of implicit roadmaps in multi-robot motion planning,”Intl. J. Rob. Res. (IJRR), vol. 35, no. 5, pp. 501–513, 2016

    work page 2016

  47. [47]

    Automating terrain analysis: Algorithms for intelligence preparation of the battlefield,

    C. Grindle, M. Lewis, R. Glinton, J. Giampapa, S. Owens, and K. Sycara, “Automating terrain analysis: Algorithms for intelligence preparation of the battlefield,”Hum. Fac. and Erg. Soc. Ann. Meet., vol. 48, no. 3, pp. 533–537, 2004

    work page 2004

  48. [48]

    Where to go and how to get there: Tactical terrain analysis for military unmanned ground-vehicle mission planning,

    T. M. Maaiveld, D. D. Nieuwenhuis, N. de Reus, M. Schadd, and F. Kuijper, “Where to go and how to get there: Tactical terrain analysis for military unmanned ground-vehicle mission planning,” inIntl. Conf. on Mod. and Sim. for Aut. Sys.Springer, 2023, pp. 92–119

    work page 2023

  49. [49]

    Tactical behaviors for autonomous maneuver: collabo- rative research program (TBAM-CRP),

    J. G. Rogers III, “Tactical behaviors for autonomous maneuver: collabo- rative research program (TBAM-CRP),” inOpen Arch./Open Bus. Model Net-Cent. Sys. and Def. Trans., vol. 12544. SPIE, 2023, pp. 85–89

    work page 2023

  50. [50]

    Dynamic programming and optimal control 3rd edi- tion,

    D. P. Bertsekas, “Dynamic programming and optimal control 3rd edi- tion,”Belmont, MA: Athena Scientific, 2011

    work page 2011

  51. [51]

    A formal definition of the multi-robot multi-task time-extended assignment problem configura- tion,

    B. Miloradovic and A. V . Papadopoulos, “A formal definition of the multi-robot multi-task time-extended assignment problem configura- tion,” inIntl. Conf. on Auto. Sci. and Eng. (CASE). IEEE, 2025

    work page 2025

  52. [52]

    Aggressive driving with model predictive path integral control,

    G. Williams, P. Drews, B. Goldfain, J. M. Rehg, and E. A. Theodorou, “Aggressive driving with model predictive path integral control,” inIntl. Conf. Rob. Aut. (ICRA). IEEE, 2016, pp. 1433–1440

    work page 2016

  53. [53]

    A survey of path planning algorithms for mobile robots,

    K. Karur, N. Sharma, C. Dharmatti, and J. E. Siegel, “A survey of path planning algorithms for mobile robots,”Vehicles, vol. 3, no. 3, pp. 448– 468, 2021

    work page 2021

  54. [54]

    Recent advances in path integral control for trajectory optimization: An overview in theoretical and algorithmic perspectives,

    M. Kazim, J. Hong, M.-G. Kim, and K.-K. K. Kim, “Recent advances in path integral control for trajectory optimization: An overview in theoretical and algorithmic perspectives,”Ann. Rev. in Contr., vol. 57, p. 100931, 2024

    work page 2024

  55. [55]

    Problem compilation for multi-agent path finding: a survey

    P. Surynek, “Problem compilation for multi-agent path finding: a survey.” inIntl. Joint Conf. on Art. Int. (IJCAI), 2022, pp. 5615–5622

    work page 2022

  56. [56]

    H. P. Williams,Model Building in Mathematical Programming. Wiley, 2013

    work page 2013

  57. [57]

    Convex analysis,

    R. Tyrrell Rockafellar, “Convex analysis,”Princeton Math. Ser., vol. 28, 1970

    work page 1970

  58. [58]

    The role of perspective functions in convexity, polyconvexity, rank-one convexity and separate convexity,

    B. Dacorogna and P. Mar ´echal, “The role of perspective functions in convexity, polyconvexity, rank-one convexity and separate convexity,” Journal of Convex Analysis, vol. 15, no. 2, pp. 271–284, 2008

    work page 2008

  59. [59]

    S. P. Boyd and L. Vandenberghe,Convex Optimization. Cambridge University Press, 2004

    work page 2004

  60. [60]

    Gurobi Optimizer Reference Manual,

    Gurobi Optimization, LLC, “Gurobi Optimizer Reference Manual,”

    work page

  61. [61]

    Available: https://www.gurobi.com

    [Online]. Available: https://www.gurobi.com

    work page

  62. [62]

    Generating Viewsheds without Using Sightlines,

    J. Wang, G. J. Robinson, and K. White, “Generating Viewsheds without Using Sightlines,”Photo. Eng. and Remo. Sens., vol. 66, pp. 87–90, 2000

    work page 2000

  63. [63]

    Dividing A Polygon In Any Given Number Of Equal Areas,

    S. Khetarpal, “Dividing A Polygon In Any Given Number Of Equal Areas,” 2014. [Online]. Available: http://www.khetarpal.org/ polygon-splitting/

    work page 2014

  64. [64]

    Trajectory generation for multiagent point-to-point transitions via distributed model predictive control,

    C. E. Luis and A. P. Schoellig, “Trajectory generation for multiagent point-to-point transitions via distributed model predictive control,”Rob. Autom. Lett. (RA-L), vol. 4, no. 2, pp. 375–382, 2019

    work page 2019

  65. [65]

    Terrainnet: Visual modeling of complex terrain for high- speed, off-road navigation,

    X. Meng, N. Hatch, A. Lambert, A. Li, N. Wagener, M. Schmittle, J. Lee, W. Yuan, Z. Chen, S. Deng, G. Okopal, D. Fox, B. Boots, and A. Shaban, “Terrainnet: Visual modeling of complex terrain for high- speed, off-road navigation,”arXiv preprint arXiv:2303.15771, 2023

    work page arXiv 2023

  66. [66]

    Meadow - Environment Set

    Nature Manufacture, “Meadow - Environment Set.” [Online]. Available: https://naturemanufacture.com/meadow-environment-set/

    work page