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TIRAS Transactions on Intelligent Robotics and Autonomous Systems An open access journal of Kaggleyes Academy
TIRAS > Vol. 1, No. 3 (September 2026) > KAGG:2609.00005
Vol. 1, No. 3 (September 2026) — Article KAGG:2609.00005

Shared Configuration Abstractions for Heterogeneous Fleet Navigation

Authors: Hana Sato, Tomas Keller, Mei-Lin Chao

Advanced Robotics Group, Toyota Research Institute, Palo Alto, CA, USA

[Received 2026-08-27; accepted 2026-09-07; published online 2026-09-10]

Abstract:

Heterogeneous robot fleets that must follow, avoid, or rendezvous with one another need more than per-robot planning: each robot must reason about how its peers are shaped, how quickly they can stop, and how these properties change during operation. Configuration-driven local planners such as AgniNav describe a single robot through an abstract configuration of body geometry, sensing, and velocity limits, but they treat other robots as generic obstacles. This paper extends the configuration descriptor into a fleet-shared abstraction. Each robot publishes a versioned configuration handle on the fleet bus, and the unchanged local planner evaluates interaction-critical derived quantities, namely a predicted clearance envelope and a stop distance, as functions of the peer's live configuration, with conservative inflation when a handle is stale. The approach is evaluated in simulation on a mixed fleet of a Go2-class quadruped, a B2-class quadruped, and a wheeled robot across 12 corridor and intersection scenarios with 5 seeds each. Relative to a configuration-blind planner that models peers as footprint blobs, interaction success increases from 77.7 to 91.6 percent, envelope prediction error decreases from 12.2 cm to 10.8 cm, and stop-distance error decreases from 15.3 cm to 14.3 cm, while the 95th-percentile planner overhead is 3.1 ms compared with 3.5 ms. Ablations indicate that the stop-distance derivation, the clearance envelope, and live configuration updates each account for 9.0 to 10.1 percentage points of interaction success. Recorded failure cases illustrate behavior under stale handles and mid-episode payload changes.

Journal: Transactions on Intelligent Robotics and Autonomous Systems (TIRAS)
Volume / Issue: Vol. 1, No. 3 (September 2026)
Article ID: KAGG:2609.00005
DOI: 10.5803/kagg.2609.00005
Received: 2026-08-27
Accepted: 2026-09-07
Published online: 2026-09-10
License: CC BY 4.0

References (42)

  1. T. Zang, S. Cheng, H. Huang, S. Wang, and W. Zhang. AgniNav: Configuration-Driven Cross-Embodiment Local Planning for Robot Navigation. arXiv:2606.10903, 2026. arXiv:2606.10903
  2. H. Xi, M. Tan, X. Zhang, S. Cheng, S. Wang, Y. Gu, X. Shen, and W. Zhang. CeRLP: A Cross-embodiment Robot Local Planning Framework for Visual Navigation. arXiv:2603.19602, 2026. arXiv:2603.19602
  3. D. Shah, A. Sridhar, A. Bhorkar, N. Hirose, and S. Levine. GNM: A General Navigation Model to Drive Any Robot. arXiv:2210.03370, 2022. arXiv:2210.03370
  4. D. Shah, A. Sridhar, N. Dashora, K. Stachowicz, K. Black, N. Hirose, and S. Levine. ViNT: A Foundation Model for Visual Navigation. arXiv:2306.14846, 2023. arXiv:2306.14846
  5. A. Sridhar, D. Shah, C. Glossop, and S. Levine. NoMaD: Goal Masked Diffusion Policies for Navigation and Exploration. arXiv:2310.07896, 2023. arXiv:2310.07896
  6. A. Eftekhar et al.. The One RING: a Robotic Indoor Navigation Generalist. arXiv:2412.14401, 2024. arXiv:2412.14401
  7. J. Zhang et al.. Embodied Navigation Foundation Model. arXiv:2509.12129, 2025. arXiv:2509.12129
  8. J. Zhang, Y. Du, X. Guo, S. Sun, X. Liu, Y. Sun, G. Lu, W. Sui, and J. Li. EA-Nav: Learning Safe Visual Navigation Policies with Embodiment Awareness. arXiv:2607.19880, 2026. arXiv:2607.19880
  9. J. Hu, S. Yuan, G. C. R. Bethala, A. Tzes, and Y. Fang. Learning Adaptive Safety Margins for Visual Navigation. arXiv:2607.18200, 2026. arXiv:2607.18200
  10. D. Fox, W. Burgard, and S. Thrun. The Dynamic Window Approach to Collision Avoidance. IEEE Robotics and Automation Magazine, 4(1), 1997. doi:10.1109/100.580977
  11. C. Rösmann, F. Hoffmann, and T. Bertram. Integrated Online Trajectory Planning and Optimization in Distinctive Topologies. Robotics and Autonomous Systems, 88, 2017. doi:10.1016/j.robot.2016.11.007
  12. G. Williams, N. Wagener, B. Goldfain, P. Drews, J. M. Rehg, B. Boots, and E. A. Theodorou. Information Theoretic MPC for Model-Based Reinforcement Learning. IEEE International Conference on Robotics and Automation (ICRA), 2017. doi:10.1109/ICRA.2017.7989202
  13. S. Macenski, F. Martín, R. White, and J. Ginés Clavero. The Marathon 2: A Navigation System. arXiv:2003.00368, 2020. arXiv:2003.00368
  14. S. Macenski, T. Foote, B. Gerkey, C. Lalancette, and W. Woodall. Robot Operating System 2: Design, Architecture, and Uses in the Wild. arXiv:2211.07752, 2022. arXiv:2211.07752
  15. P. Fiorini and Z. Shiller. Motion Planning in Dynamic Environments Using Velocity Obstacles. The International Journal of Robotics Research, 17(7), 1998. doi:10.1177/027836499801700706
  16. J. van den Berg, M. Lin, and D. Manocha. Reciprocal Velocity Obstacles for Real-Time Multi-Agent Navigation. IEEE International Conference on Robotics and Automation (ICRA), 2008. doi:10.1109/ROBOT.2008.4543489
  17. J. van den Berg, S. J. Guy, M. Lin, and D. Manocha. Reciprocal n-Body Collision Avoidance. Robotics Research (ISRR), Springer Tracts in Advanced Robotics, vol. 70, 2011. doi:10.1007/978-3-642-19457-3_1
  18. J. Alonso-Mora, A. Breitenmoser, M. Rufli, P. Beardsley, and R. Siegwart. Optimal Reciprocal Collision Avoidance for Multiple Non-Holonomic Robots. Distributed Autonomous Robotic Systems, Springer Tracts in Advanced Robotics, vol. 83, 2013. doi:10.1007/978-3-642-32723-0_15
  19. A. D. Ames, S. Coogan, M. Egerstedt, G. Notomista, K. Sreenath, and P. Tabuada. Control Barrier Functions: Theory and Applications. arXiv:1903.11199, 2019. arXiv:1903.11199
  20. L. Wang, A. D. Ames, and M. Egerstedt. Safety Barrier Certificates for Collisions-Free Multirobot Systems. IEEE Transactions on Robotics, 33(3), 2017. doi:10.1109/TRO.2017.2659727
  21. S. Shalev-Shwartz, S. Shammah, and A. Shashua. On a Formal Model of Safe and Scalable Self-driving Cars. arXiv:1708.06374, 2017. arXiv:1708.06374
  22. Y. F. Chen, M. Liu, M. Everett, and J. P. How. Decentralized Non-communicating Multiagent Collision Avoidance with Deep Reinforcement Learning. arXiv:1609.07845, 2016. arXiv:1609.07845
  23. P. Long, T. Fan, X. Liao, W. Liu, H. Zhang, and J. Pan. Towards Optimally Decentralized Multi-Robot Collision Avoidance via Deep Reinforcement Learning. arXiv:1709.10082, 2017. arXiv:1709.10082
  24. M. Everett, Y. F. Chen, and J. P. How. Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning. arXiv:1805.01956, 2018. arXiv:1805.01956
  25. X. Zhou, J. Zhu, H. Zhou, C. Xu, and F. Gao. EGO-Swarm: A Fully Autonomous and Decentralized Quadrotor Swarm System in Cluttered Environments. arXiv:2011.04183, 2020. arXiv:2011.04183
  26. J. Tordesillas and J. P. How. MADER: Trajectory Planner in Multi-Agent and Dynamic Environments. arXiv:2010.04466, 2020. arXiv:2010.04466
  27. C. E. Luis and A. P. Schoellig. Trajectory Generation for Multiagent Point-To-Point Transitions via Distributed Model Predictive Control. arXiv:1809.04230, 2018. arXiv:1809.04230
  28. G. Sharon, R. Stern, A. Felner, and N. R. Sturtevant. Conflict-Based Search for Optimal Multi-Agent Pathfinding. Artificial Intelligence, 219, 2015. doi:10.1016/j.artint.2014.11.006
  29. L. E. Parker. ALLIANCE: An Architecture for Fault Tolerant Multirobot Cooperation. IEEE Transactions on Robotics and Automation, 14(2), 1998. doi:10.1109/70.681242
  30. B. P. Gerkey and M. J. Matarić. A Formal Analysis and Taxonomy of Task Allocation in Multi-Robot Systems. The International Journal of Robotics Research, 23(9), 2004. doi:10.1177/0278364904045564
  31. Y. Rizk, M. Awad, and E. W. Tunstel. Cooperative Heterogeneous Multi-Robot Systems: A Survey. ACM Computing Surveys, 52(2), 2019. doi:10.1145/3303848
  32. P. Trautman and A. Krause. Unfreezing the Robot: Navigation in Dense, Interacting Crowds. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2010. doi:10.1109/IROS.2010.5654369
  33. C. Mavrogiannis, F. Baldini, A. Wang, D. Zhao, P. Trautman, A. Steinfeld, and J. Oh. Core Challenges of Social Robot Navigation: A Survey. arXiv:2103.05668, 2021. arXiv:2103.05668
  34. A. Francis et al.. Principles and Guidelines for Evaluating Social Robot Navigation Algorithms. arXiv:2306.16740, 2023. arXiv:2306.16740
  35. M. Hutter et al.. ANYmal - a Highly Mobile and Dynamic Quadrupedal Robot. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2016. doi:10.1109/IROS.2016.7758092
  36. J. Di Carlo, P. M. Wensing, B. Katz, G. Bledt, and S. Kim. Dynamic Locomotion in the MIT Cheetah 3 Through Convex Model-Predictive Control. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2018. doi:10.1109/IROS.2018.8594448
  37. J. Lee, J. Hwangbo, L. Wellhausen, V. Koltun, and M. Hutter. Learning Quadrupedal Locomotion over Challenging Terrain. arXiv:2010.11251, 2020. arXiv:2010.11251
  38. T. Miki, J. Lee, J. Hwangbo, L. Wellhausen, V. Koltun, and M. Hutter. Learning Robust Perceptive Locomotion for Quadrupedal Robots in the Wild. arXiv:2201.08117, 2022. arXiv:2201.08117
  39. N. Rudin, D. Hoeller, P. Reist, and M. Hutter. Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learning. arXiv:2109.11978, 2021. arXiv:2109.11978
  40. V. Makoviychuk et al.. Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning. arXiv:2108.10470, 2021. arXiv:2108.10470
  41. A. Kumar, Z. Fu, D. Pathak, and J. Malik. RMA: Rapid Motor Adaptation for Legged Robots. arXiv:2107.04034, 2021. arXiv:2107.04034
  42. A. Kadian et al.. Sim2Real Predictivity: Does Evaluation in Simulation Predict Real-World Performance? arXiv:1912.06321, 2019. arXiv:1912.06321
Cite this article Hana Sato, Tomas Keller, Mei-Lin Chao, "Shared Configuration Abstractions for Heterogeneous Fleet Navigation," Transactions on Intelligent Robotics and Autonomous Systems, 2026, vol. 1, no. 3, KAGG:2609.00005. DOI: 10.5803/kagg.2609.00005.