Paper Citation Record · LEDGER

High-Precision Formation Control for Heterogeneous Multi-Robot Systems via Hierarchical Hybrid Physics-Informed Deep Reinforcement Learning

As of 17 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 0 inbound Pith citation observations for arXiv:2607.03512.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2607.03512 v1

Coverage vector

measured 37 of 37 reference resolution

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Pith citing papers itemized under the disclosed page cap.

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Source: cited_works

Reference resolution

37 of 37 outbound references displayed

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External citation measurements

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Outbound references

Observation 3ddc0491-8d22-4dff-8cee-86907eaaeb75 · outbound

This paper cites Multi-robot environmental coverage with a two-stage coordination strategy via deep reinforcement learning,.

High-Precision Formation Control for Heterogeneous Multi-Robot Systems via Hierarchical Hybrid Physics-Informed Deep Reinforcement Learning Multi-robot environmental coverage with a two-stage coordination strategy via deep reinforcement learning,

Reference 1

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Observation 1c17dd80-e79d-47a0-98e5-32d24c996117 · outbound

This paper cites Development of a search and rescue robot system for the underground building environment,.

High-Precision Formation Control for Heterogeneous Multi-Robot Systems via Hierarchical Hybrid Physics-Informed Deep Reinforcement Learning Development of a search and rescue robot system for the underground building environment,

Reference 2

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Observation 1bc8ace7-d5a6-48ec-97a2-c58928af3cfa · outbound

This paper cites A reinforced neighborhood search method combined with genetic algorithm for multi-objective multi-robot transportation system,.

High-Precision Formation Control for Heterogeneous Multi-Robot Systems via Hierarchical Hybrid Physics-Informed Deep Reinforcement Learning A reinforced neighborhood search method combined with genetic algorithm for multi-objective multi-robot transportation system,

Reference 3

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Observation de0a6448-52f2-4aa2-bc17-a0efe94aec0d · outbound

This paper cites Forma- tion control of nonholonomic multirobot systems over robot coordinate frames and its application to LiDAR-based robots,.

High-Precision Formation Control for Heterogeneous Multi-Robot Systems via Hierarchical Hybrid Physics-Informed Deep Reinforcement Learning Forma- tion control of nonholonomic multirobot systems over robot coordinate frames and its application to LiDAR-based robots,

Reference 4

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Observation 88fd43f5-f9fd-4e32-a959-07169680a71a · outbound

This paper cites Deform: Adaptive formation reconfig- uration of multi-robot systems in confined environments,.

High-Precision Formation Control for Heterogeneous Multi-Robot Systems via Hierarchical Hybrid Physics-Informed Deep Reinforcement Learning Deform: Adaptive formation reconfig- uration of multi-robot systems in confined environments,

Reference 5

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Observation 4a7099fb-a271-45ac-8d10-02867ff81057 · outbound

This paper cites Heterogeneous multi-robot cooperation with asynchronous multi-agent reinforcement learning,.

High-Precision Formation Control for Heterogeneous Multi-Robot Systems via Hierarchical Hybrid Physics-Informed Deep Reinforcement Learning Heterogeneous multi-robot cooperation with asynchronous multi-agent reinforcement learning,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-07-12T01:58:48.241542Z

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Observation baeef2d6-ea3b-49d1-b1b8-6f07767d3245 · outbound

This paper cites Leader-following formation of heterogeneous multi-agent systems with time-varying topology: A virtual neighbor framework,.

High-Precision Formation Control for Heterogeneous Multi-Robot Systems via Hierarchical Hybrid Physics-Informed Deep Reinforcement Learning Leader-following formation of heterogeneous multi-agent systems with time-varying topology: A virtual neighbor framework,

Reference 7

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Observation 4ee2c292-b687-4e64-8bdb-c5ce508296d6 · outbound

This paper cites PD and PI control for the lag consensus of nonlinear multiagent systems with and without external disturbances,.

High-Precision Formation Control for Heterogeneous Multi-Robot Systems via Hierarchical Hybrid Physics-Informed Deep Reinforcement Learning PD and PI control for the lag consensus of nonlinear multiagent systems with and without external disturbances,

Reference 8

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Observation ca43d360-4c87-462c-bd71-289a022ad88f · outbound

This paper cites A heuristic-guided dynamical multi-rover motion planning framework for planetary surface missions,.

High-Precision Formation Control for Heterogeneous Multi-Robot Systems via Hierarchical Hybrid Physics-Informed Deep Reinforcement Learning A heuristic-guided dynamical multi-rover motion planning framework for planetary surface missions,

Reference 9

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Observation 4728bae6-580c-4269-acec-ba3ae4cf0497 · outbound

This paper cites Online trajectory generation with distributed model predictive control for multi-robot motion planning,.

High-Precision Formation Control for Heterogeneous Multi-Robot Systems via Hierarchical Hybrid Physics-Informed Deep Reinforcement Learning Online trajectory generation with distributed model predictive control for multi-robot motion planning,

Reference 10

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Observation a22be99c-5b99-46c1-97b8-d472ba175e4e · outbound

This paper cites Incorporating control barrier functions in distribut- edmodel predictive control for multirobot coordinated control,.

High-Precision Formation Control for Heterogeneous Multi-Robot Systems via Hierarchical Hybrid Physics-Informed Deep Reinforcement Learning Incorporating control barrier functions in distribut- edmodel predictive control for multirobot coordinated control,

Reference 11

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Observation a1db7c98-1bab-40db-9f88-3cfcdc52853e · outbound

This paper cites Distributed deep reinforcement learning for Ackermann multi-robot formation: A weighted multi-objective optimization,.

High-Precision Formation Control for Heterogeneous Multi-Robot Systems via Hierarchical Hybrid Physics-Informed Deep Reinforcement Learning Distributed deep reinforcement learning for Ackermann multi-robot formation: A weighted multi-objective optimization,

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-07-12T02:08:26.103561Z

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Observation d1b5a143-3b2a-4f5d-a0c7-1c15d001e717 · outbound

This paper cites Distributed deep reinforcement learning based on bi-objective framework for multi-robot formation,.

High-Precision Formation Control for Heterogeneous Multi-Robot Systems via Hierarchical Hybrid Physics-Informed Deep Reinforcement Learning Distributed deep reinforcement learning based on bi-objective framework for multi-robot formation,

Reference 13

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Observation 775a04dd-2a61-4912-a6e4-a732004c14d0 · outbound

This paper cites Multi-UA V adaptive cooperative formation trajectory planning based on an improved MATD3 algorithm of deep reinforcement learning,.

High-Precision Formation Control for Heterogeneous Multi-Robot Systems via Hierarchical Hybrid Physics-Informed Deep Reinforcement Learning Multi-UA V adaptive cooperative formation trajectory planning based on an improved MATD3 algorithm of deep reinforcement learning,

Reference 14

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Observation 26b8f46e-692e-4ac0-8ee9-f4a1221ba42f · outbound

This paper cites Safe multi-agent reinforcement learning for behavior-based cooperative navigation,.

High-Precision Formation Control for Heterogeneous Multi-Robot Systems via Hierarchical Hybrid Physics-Informed Deep Reinforcement Learning Safe multi-agent reinforcement learning for behavior-based cooperative navigation,

Reference 15

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Observation c2e669ec-ed46-4455-92d1-f334d8eda8ce · outbound

This paper cites Research on global path planning algorithm for mobile robots based on improvedA ∗,.

High-Precision Formation Control for Heterogeneous Multi-Robot Systems via Hierarchical Hybrid Physics-Informed Deep Reinforcement Learning Research on global path planning algorithm for mobile robots based on improvedA ∗,

Reference 16

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Observation 6d40d64e-2640-4aa0-824b-496b59a4251c · outbound

This paper cites An improvedA ∗ algorithm for the industrial robot path planning with high success rate and short length.

High-Precision Formation Control for Heterogeneous Multi-Robot Systems via Hierarchical Hybrid Physics-Informed Deep Reinforcement Learning An improvedA ∗ algorithm for the industrial robot path planning with high success rate and short length

Reference 17

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Observation 498e3681-014c-4726-9c99-6e9b3f5524f4 · outbound

This paper cites A self-rotating, single-actuated UA V with extended sensor field of view for autonomous navigation,.

High-Precision Formation Control for Heterogeneous Multi-Robot Systems via Hierarchical Hybrid Physics-Informed Deep Reinforcement Learning A self-rotating, single-actuated UA V with extended sensor field of view for autonomous navigation,

Reference 18

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Observation e03e3043-1121-4aa9-bdd5-859595294740 · outbound

This paper cites Au- tonomous navigation for robot-assisted intraluminal and endovascular procedures: A systematic review,.

High-Precision Formation Control for Heterogeneous Multi-Robot Systems via Hierarchical Hybrid Physics-Informed Deep Reinforcement Learning Au- tonomous navigation for robot-assisted intraluminal and endovascular procedures: A systematic review,

Reference 19

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Observation f5bc1bc7-4fd5-475d-92fb-d0c206f72a35 · outbound

This paper cites Hybrid feedback for autonomous navigation in planar environments with convex obstacles,.

High-Precision Formation Control for Heterogeneous Multi-Robot Systems via Hierarchical Hybrid Physics-Informed Deep Reinforcement Learning Hybrid feedback for autonomous navigation in planar environments with convex obstacles,

Reference 20

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Observation 72fd0eee-9a3a-4e70-86d9-2d38a2cb0888 · outbound

This paper cites Improved RRT global path planning algorithm based on bridge test,.

High-Precision Formation Control for Heterogeneous Multi-Robot Systems via Hierarchical Hybrid Physics-Informed Deep Reinforcement Learning Improved RRT global path planning algorithm based on bridge test,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-07-12T01:58:48.241542Z

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Observation 7fdd6322-8aef-49d8-bca4-c5bb705460c9 · outbound

This paper cites Efficient reliability-based path planning of off-road autonomous ground vehicles through the coupling of surrogate modeling and RRT,.

High-Precision Formation Control for Heterogeneous Multi-Robot Systems via Hierarchical Hybrid Physics-Informed Deep Reinforcement Learning Efficient reliability-based path planning of off-road autonomous ground vehicles through the coupling of surrogate modeling and RRT,

Reference 22

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Observation 8efc06f5-7ba5-4748-9d55-fd540cd4eb90 · outbound

This paper cites CDT-Dijkstra: Fast planning of globally optimal paths for all points in 2D continuous space,.

High-Precision Formation Control for Heterogeneous Multi-Robot Systems via Hierarchical Hybrid Physics-Informed Deep Reinforcement Learning CDT-Dijkstra: Fast planning of globally optimal paths for all points in 2D continuous space,

Reference 23

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Observation 835f5f11-20b6-423e-ad9c-9ed3c1b3fe3a · outbound

This paper cites Research on hybrid path planning of underground degraded environment inspection robot based on improvedA ∗ algorithm and DW A algorithm,.

High-Precision Formation Control for Heterogeneous Multi-Robot Systems via Hierarchical Hybrid Physics-Informed Deep Reinforcement Learning Research on hybrid path planning of underground degraded environment inspection robot based on improvedA ∗ algorithm and DW A algorithm,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-07-12T01:58:48.241542Z

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Observation 06b4b64f-b118-4152-b6d5-21204c182982 · outbound

This paper cites Simultaneous learning and planning within sensing range: An approach for local path planning,.

High-Precision Formation Control for Heterogeneous Multi-Robot Systems via Hierarchical Hybrid Physics-Informed Deep Reinforcement Learning Simultaneous learning and planning within sensing range: An approach for local path planning,

Reference 25

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Observation 2d5b057b-1009-477e-a6be-13d351423457 · outbound

This paper cites Supervised learning of human welder behaviors for intelligent robotic welding,.

High-Precision Formation Control for Heterogeneous Multi-Robot Systems via Hierarchical Hybrid Physics-Informed Deep Reinforcement Learning Supervised learning of human welder behaviors for intelligent robotic welding,

Reference 26

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Observation 56485dbe-e328-4235-a4f2-05909c78d7f1 · outbound

This paper cites Badgr: An autonomous self- supervised learning-based navigation system,.

High-Precision Formation Control for Heterogeneous Multi-Robot Systems via Hierarchical Hybrid Physics-Informed Deep Reinforcement Learning Badgr: An autonomous self- supervised learning-based navigation system,

Reference 27

Resolution: unresolved; no resolver link, observed 2026-07-12T01:58:48.241542Z

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Observation 0e76365c-08f6-4e4f-a649-c291c771cd7e · outbound

This paper cites Autonomous navigation of mobile robots in unknown environments using off-policy reinforcement learning with curriculum learning,.

High-Precision Formation Control for Heterogeneous Multi-Robot Systems via Hierarchical Hybrid Physics-Informed Deep Reinforcement Learning Autonomous navigation of mobile robots in unknown environments using off-policy reinforcement learning with curriculum learning,

Reference 28

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Observation ffda7a43-f1f3-4f84-8ddc-4fc385fbfb9c · outbound

This paper cites Distributed formation control for a multirobotic fish system with model-based event-triggered communication mechanism,.

High-Precision Formation Control for Heterogeneous Multi-Robot Systems via Hierarchical Hybrid Physics-Informed Deep Reinforcement Learning Distributed formation control for a multirobotic fish system with model-based event-triggered communication mechanism,

Reference 29

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Observation 185d4fbb-f8ec-4130-ab4f-b4a02b1d7f02 · outbound

This paper cites Formation control of unmanned aerial vehicle swarms: A comprehensive review,.

High-Precision Formation Control for Heterogeneous Multi-Robot Systems via Hierarchical Hybrid Physics-Informed Deep Reinforcement Learning Formation control of unmanned aerial vehicle swarms: A comprehensive review,

Reference 30

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Observation cdd16f28-3180-4af2-abb3-91ddf1a5b3f5 · outbound

This paper cites Unmanned aerial vehicle formation control method based on improved artificial potential field and consensus,.

High-Precision Formation Control for Heterogeneous Multi-Robot Systems via Hierarchical Hybrid Physics-Informed Deep Reinforcement Learning Unmanned aerial vehicle formation control method based on improved artificial potential field and consensus,

Reference 31

Resolution: unresolved; no resolver link, observed 2026-07-12T01:58:48.241542Z

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Observation 605db609-5679-45fa-b900-79409ea6b75d · outbound

This paper cites An improved artificial potential field method for path planning and formation control of the multi-UA V systems,.

High-Precision Formation Control for Heterogeneous Multi-Robot Systems via Hierarchical Hybrid Physics-Informed Deep Reinforcement Learning An improved artificial potential field method for path planning and formation control of the multi-UA V systems,

Reference 32

Resolution: unresolved; no resolver link, observed 2026-07-12T01:58:48.241542Z

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Observation fdb721dd-79ca-4198-a8b4-e983bea4b362 · outbound

This paper cites Bearing-based formation control simultaneously involving several heterogeneous multi-agent systems with nonlinear 13 uncertainties,.

High-Precision Formation Control for Heterogeneous Multi-Robot Systems via Hierarchical Hybrid Physics-Informed Deep Reinforcement Learning Bearing-based formation control simultaneously involving several heterogeneous multi-agent systems with nonlinear 13 uncertainties,

Reference 33

Resolution: unresolved; no resolver link, observed 2026-07-12T01:58:48.241542Z

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Observation e941d3fa-ca23-4487-b62f-0bb03a37fafa · outbound

This paper cites Decentralized behavior-based formation control of multiple robots considering obstacle avoidance,.

High-Precision Formation Control for Heterogeneous Multi-Robot Systems via Hierarchical Hybrid Physics-Informed Deep Reinforcement Learning Decentralized behavior-based formation control of multiple robots considering obstacle avoidance,

Reference 34

Resolution: unresolved; no resolver link, observed 2026-07-12T01:58:48.241542Z

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This paper cites Soft actor-critic: Off- policy maximum entropy deep reinforcement learning with a stochastic actor,.

High-Precision Formation Control for Heterogeneous Multi-Robot Systems via Hierarchical Hybrid Physics-Informed Deep Reinforcement Learning Soft actor-critic: Off- policy maximum entropy deep reinforcement learning with a stochastic actor,

Reference 35

Resolution: unresolved; no resolver link, observed 2026-07-12T01:58:48.241542Z

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Observation 0f220bbd-6724-4ea3-bb7c-f7b51849ea26 · outbound

This paper cites Scalable Safe Multi-Agent Reinforcement Learning for Multi-Agent System.

High-Precision Formation Control for Heterogeneous Multi-Robot Systems via Hierarchical Hybrid Physics-Informed Deep Reinforcement Learning Scalable Safe Multi-Agent Reinforcement Learning for Multi-Agent System

Reference 36

Resolution: unresolved; no resolver link, observed 2026-07-12T01:58:48.241542Z

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Observation b91859d3-848a-4a59-a7e3-00865fbf4d57 · outbound

This paper cites Graph neu- ral network-based multi-agent reinforcement learning for resilient dis- tributed coordination of multi-robot systems,.

High-Precision Formation Control for Heterogeneous Multi-Robot Systems via Hierarchical Hybrid Physics-Informed Deep Reinforcement Learning Graph neu- ral network-based multi-agent reinforcement learning for resilient dis- tributed coordination of multi-robot systems,

Reference 37

Resolution: unresolved; no resolver link, observed 2026-07-12T01:58:48.241542Z

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