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REVIEW 4 major objections 7 minor 42 references

Dynamics and Control of Vision-Aided Multi-UAV-tethered Netted System Capturing Non-Cooperative Target

T0 review · 4 major / 7 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read The paper claims that the mySim simulator, built on multibody dynamics with a spring-damper tethered net, monocular visual-inertial navigation, and a multi-agent reinforcement-learning policy, can capture both non-propelled and…

desk verdict A well-scoped integration effort whose headline capture claim is undermined by a basic error in the monocular range model and a lack of quantitative validation. read the letter →

arxiv 2506.03297 v1 pith:5EIBRVR4 submitted 2025-06-03 cs.RO cs.SYeess.SY

Runhan Liu , Hui Ren , Wei Fan This is my paper
classification cs.ROcs.SYeess.SY
keywords MultirotorUAVMultibodydynamicsTetherednetNon-cooperativetargetcaptureReinforcementlearningVisual-inertialodometrysimulationMAPPO
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper argues that a multi-UAV team carrying a tethered net can capture non-cooperative aerial targets, and that the right way to develop and validate such a system is a simulation environment that couples physics, perception, and learning. The authors build that environment, called mySim, on multibody dynamics: the net is a spring-damper mass-spring network, contacts are penalty-based, and the UAVs are full multirotor bodies. Perception is monocular visual-inertial odometry for the UAVs plus a transformer-based detector for the target, and the capture policy is trained with a multi-agent PPO algorithm. The paper's central claim is that simulation results show this integrated pipeline successfully capturing both a non-propelled free-falling target and an actively maneuvering target, making mySim a platform for pre-deployment testing and optimization of UAV capture policies.

What carries the argument

The load-bearing machinery is the multibody-dynamics formulation built around marker technology: system equations $M\dot{v} + C_q^T(q,t)\lambda - F(q,v,t) - f = 0$ with constraints $C(q,t)=0$, where rope modules are lumped-mass spring-damper chains and collisions enter as penalty-based normal and friction forces. On top of this dynamics core sits a rendering post-processor that feeds a monocular visual-inertial estimator (VINS-MONO) and a transformer-based object detector (DETR), and a multi-agent reinforcement-learning policy (MAPPO) that maps estimated states to rotor commands. What carries the argument is that every component—ground-truth dynamics, rendered perception, and learned control—consumes and produces data in the same loop, so the final captures exercise the whole chain rather than any single module.

What would settle it

Re-run the end-to-end capture with the constant in the target-distance proxy $\beta_i \propto 1/\alpha_i$ multiplied by 0.5 and by 2.0 while keeping the trained policy frozen; if capture success collapses for either scale, the result depends on the uncalibrated scale rather than a robust perception-to-control loop.

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Extended reading notes

Core claim

On the paper's own terms, the central discovery is that a full robot-simulation loop—net flexibility, collisions, multirotor dynamics, vision-based state estimation, and learned coordination—can be assembled inside one multibody-dynamics simulator and still produce successful captures. The system is validated in stages: rope-net dynamics match a commercial multibody solver, collision response shows expected oscillatory damping, and UAV trajectories track references closely; then perception and control are integrated, and finally the end-to-end system captures the two target types. The authors present this as evidence that mySim accurately represents the dynamics and control of the multi-UAV-tethered netted system, so the simulator can serve as a testbed for real-world capture policies.

Load-bearing premise

The load-bearing premise is that an uncalibrated monocular area-to-distance proxy, fused across UAVs, gives a target state accurate enough for the learned policy; a scale error or bias in that proxy would invalidate the capture results.

Editorial extensions

If this is right

  • Multi-UAV capture policies could be trained and screened in simulation before any flight hardware is built, lowering the cost and risk of field trials.
  • Because the system's structure is defined programmatically through markers, the same dynamics-and-learning loop could be reprogrammed for other tethered or articulated UAV payloads.
  • A successful capture of an actively maneuvering target implies the learned policy can operate under partial observability and motion uncertainty, not just track a scripted descent.
  • Validation of the rope module against a commercial multibody solver indicates the spring-damper net model is accurate enough for control-level studies while staying cheap enough for reinforcement learning.
  • The staged validation pattern—physics, then perception, then integrated policy—provides a template for testing other complex UAV coordination systems.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Beyond the paper, the uncalibrated monocular range proxy ($\beta_i \propto 1/\alpha_i$) means the simulated captures are only as trustworthy as the consistency between the rendering camera model and the detector; a real deployment would need a calibration or learned depth scale, which the paper leaves open.
  • Since MAPPO training details such as reward curves, random seeds, and hyperparameter sensitivity are not reported, a natural next experiment is to freeze the trained policy and test it on out-of-distribution target trajectories and wind disturbances to separate genuine coordination from scenario overfitting.
  • The perception module is validated on the simulator's own rendered images; adapting the same pipeline to real or photo-realistic footage of a net-capture rig would quantify the sim-to-real gap that the paper does not address.
  • A parameter sensitivity sweep over rope stiffness, damping, and contact coefficients would show which dynamical quantities actually set capture reliability, pointing to the hardware specifications a physical system would need.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 7 minor

Summary. The paper presents mySim, a multibody-dynamics-based simulation environment for a multi-UAV tethered net system designed to capture non-cooperative targets. The simulator integrates a spring-damper rope model, a contact/collision module, UAV dynamics with PID inner-outer loop control, vision-based state estimation (VINS-MONO for UAV pose, DETR for target detection, and a monocular distance estimate for target localization), and a MAPPO-based multi-agent control policy. The authors validate the rope module against the commercial MWorks package, demonstrate qualitative UAV trajectory tracking and target-cone overlap, and report end-to-end simulation results for capturing both a free-falling and a maneuvering target. The central claim is that mySim accurately simulates the dynamics and control of the system and successfully enables capture of non-cooperative targets in simulation.

Significance. If the central claim holds, mySim would be a valuable contribution as a high-precision, integrated simulation platform for testing and optimizing UAV-tethered-net capture policies before deployment. The paper addresses a practical problem (non-cooperative target capture in low-altitude airspace) and ambitiously combines physics simulation, perception, and learning in one framework. A notable strength is the explicit comparison of the rope module against the external commercial tool MWorks, which gives partial independent grounding to the dynamics model. However, the significance is currently undermined by load-bearing issues in the elastic-force equation, the monocular distance scaling, and the absence of quantitative capture metrics. These issues must be resolved before the claims of accurate simulation and successful capture can be accepted.

major comments (4)
  1. [§2.1, Eq. (7)] Equation (7) defines the elastic force of the rope module as f_i^k = k_i(s_i - s_i), which is identically zero by construction. This makes the internal force F_i in Eq. (9) vanish, contradicting the MWorks validation results shown in Fig. 18 and the entire rope-dynamics model. Presumably one of the two s_i terms should denote the current segment length rather than the natural length defined in Eq. (2). Please correct the equation and clarify the notation; as written, this is a load-bearing error in the core dynamics derivation.
  2. [§2.3, Eqs. (29)-(30)] The monocular target-range estimate uses beta_i ∝ 1/alpha_i, where alpha_i is the normalized bounding-box area. Under the pinhole camera model with the intrinsics in Table 6, the projected area of a fixed-size target scales as 1/r^2, so the correct scaling is beta_i ∝ 1/sqrt(alpha_i). The stated proportionality makes the estimated distance grow quadratically with true range, and the C(n,3) fusion in §2.3 then triangulates from inconsistent range measurements. No calibration constant, procedure, or error model is supplied. The validation in §3.2 is only qualitative (overlapping cones in Fig. 26), and no target-state RMSE is reported. Because this estimated target state directly feeds the MAPPO policy (Alg. B4), the scaling error can invalidate the end-to-end capture results. Please correct the formula, provide a calibration method, or present quantitative evidence that the implemented estimator produces metric target positions.
  3. [§3.3, Figs. 27-28] The end-to-end capture results are presented only as trajectory plots and visualizations. There is no quantitative definition of successful capture, no success-rate statistics over multiple runs, no miss-distance or net-containment metric, and no comparison with a baseline (e.g., a policy using ground-truth target states). The abstract's claim that the system 'successfully enables capture' is therefore not supported by measurable evidence in the current manuscript. Please add concrete metrics such as capture success rate, target-state estimation error, and quantitative distance-to-target curves with defined tolerances.
  4. [§3.3, Table 8] The MAPPO reward weights in Table 8 are hand-selected, and together with the observation design they define what counts as success. The paper does not report training curves, reward-weight sensitivity, or an ablation with an oracle perception module. Without such analysis, it is unclear whether the demonstrated captures are robust capabilities or artifacts of the particular reward tuning and of the (possibly miscalibrated) perception scaling in Eqs. (29)-(30). Please add at least a sensitivity study or an oracle-perception baseline to separate perception error from policy behavior.
minor comments (7)
  1. [§2.2, Eq. (26)] The definition of U(0, δ) says 'a neighborhood centered at the origin with radius' but the radius value is missing; please complete the sentence.
  2. [§2.1, Eq. (13)] The step() function arguments and the damping term in the contact force equation are difficult to parse and may contain a typo; please rewrite the formula with clear definitions of the penetration variable and the step() parameters.
  3. [Table 3] The mass values for payload and collision object are written in an angle-bracket notation '<2.00×10 −1,1.00×10 1 >' that is not defined; please state explicitly that these are ranges and how the two cases in Fig. 20 were selected.
  4. [Acknowledgments] The acknowledgments section is incomplete, ending with 'This was was supported in part by...... UNDERCONSTRUCTION'; complete the funding statement and correct the duplicated 'was' before submission.
  5. [Fig. 22 caption] The caption says 'more vivid UA V positions indicating poses rendered at later time steps'; this is vague and should specify the color or time mapping used in the plots.
  6. [§2.3, Alg. B3] Algorithm B3 states that the target's orientation in world coordinates is estimated, but Eq. (30) provides only a radial distance; explain how orientation is obtained or remove this from the algorithm description.
  7. [Eq. (28)] Equation (28) is typeset in a confusing way, appearing as a product of a matrix and its inverse separated by a line break; please reformat it to show the standard control allocation relation.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the central dynamics claim is checked against an external commercial tool, perception and control use external algorithms, and no load-bearing self-citations or definitional reductions were found.

full rationale

The paper's derivation chain is largely self-contained. The rope dynamics module is validated against the commercial MBD tool MWorks in Section 3.1, providing independent grounding for the central dynamics claim. The perception modules use external, established algorithms (VINS-MONO, DETR), and the control learning uses the published MAPPO algorithm, so these are not self-citations. No load-bearing argument reduces, by the paper's own equations, to a fitted parameter or to a self-citation chain. The closest concern is the monocular range approximation in Eqs. (29)-(30), where beta_i is set proportional to 1/alpha_i; under pinhole geometry this scaling is questionable, and the proportionality constant is unspecified. However, this is a correctness/robustness issue, not circularity: the estimated distance is not fitted to the capture outcome, and the capture 'prediction' is not defined as the same quantity that enters the estimator. The capture demonstrations are evaluated in mySim itself, which is a closed-loop RL evaluation rather than an independently measured prediction, but that does not make the derivation circular by construction. No self-citations were found, and no claim is justified solely by a prior work of the same authors. Therefore the paper merits a circularity score of 0.

Assumptions & free parameters 4 free parameters · 7 assumptions · 0 invented entities

The central claim rests on standard multibody dynamics and named perception and RL packages, plus several hand-chosen parameters and modeling assumptions. No new physical entities are introduced; mySim is a software integration rather than a new physical object.

free parameters (4)
  • Monocular distance scale factor = unspecified; Eq. (30) only states beta_i proportional to 1/alpha_i
    The target range from bounding-box area has no calibration constant or error model. Multi-camera fusion feeds this to MAPPO, so the scale directly affects the demonstrated capture.
  • MAPPO reward weights = Table 8: r_distance=1.2, r_alignment=0.6, r_spin=0.8, r_effort=0.1, r_swing=0.8, r_safe=0.5, r_collision=2.8…
    Hand-selected rewards define the behavior that counts as capture; the policy is trained and evaluated on the same reward, so the choice is load-bearing.
  • Collision penalty parameters = Table 2: k=1e8 N/m, d=1e4 N*s/m, p=1e-4 m, n=1, mu_s=0.04, mu_d=0.03
    Contact force model parameters are chosen by hand and validated only qualitatively; collision outcomes in the capture scenarios depend on them.
  • PID controller gains = Table 5 position and attitude gains
    Hand-tuned gains produce the trajectory tracking shown in Fig. 22; no tuning procedure or robustness analysis is given.
assumptions (7)
  • standard math Multibody dynamics DAE with Lagrange multipliers (Eq. 1, Eq. 10) correctly represents the constrained UAV-net system.
    Assumed as background; no derivation of the formulation is given, but it is standard in MBD.
  • domain assumption Spring-damper point-mass discretization of the tether net (Eqs. 2-9) adequately reproduces net deformation and capture dynamics.
    The paper chooses this model for efficiency and validates only a simple payload case against MWorks; no bending or twisting behavior is checked.
  • domain assumption Penalty impact function (Eq. 13) with step damping and friction (Eq. 16) models collisions realistically.
    Used for net-target and UAV-target contact; validated only with qualitative oscillatory behavior in Fig. 20.
  • ad hoc to paper Monocular apparent-size distance formula (Eq. 30) provides metric target positions once fused across cameras.
    The proportionality constant is not specified or calibrated; fusion over C(n,3) triples is asserted without derivation.
  • domain assumption VINS-MONO and DETR perform correctly on Blender-rendered images without domain adaptation.
    Perception results are shown qualitatively; no training data, fine-tuning, or sim-to-real analysis is reported.
  • standard math Multirotor differential flatness permits position/yaw decomposition in Eq. (25).
    Standard property of quadrotor models, used for the inner-outer loop controller.
  • domain assumption The MAPPO policy used in Sect. 3.3 is trained to convergence and its success is not an artifact of one seed.
    No training curves, seeds, or success rates are provided, so the reported capture behavior is assumed to be a converged, representative policy.

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Cite this review

Pith. "Pith review of Dynamics and Control of Vision-Aided Multi-UAV-tethered Netted System Capturing Non-Cooperative Target." pith.science (2026). https://pith.science/paper/5EIBRVR4

@misc{pith2026250603297,
  author       = {Pith},
  title        = {Pith review of: Dynamics and Control of Vision-Aided Multi-UAV-tethered Netted System Capturing Non-Cooperative Target},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/5EIBRVR4}},
  note         = {Machine review of arXiv:2506.03297}
}
read the original abstract

As the number of Unmanned Aerial Vehicles (UAVs) operating in low-altitude airspace continues to increase, non-cooperative targets pose growing challenges to low-altitude operations. To address this issue, this paper proposes a multi-UAV-tethered netted system as a non-lethal solution for capturing non-cooperative targets. To validate the proposed system, we develop mySim, a multibody dynamics-based UAV simulation environment that integrates high-precision physics modeling, vision-based motion tracking, and reinforcement learning-driven control strategies. In mySim, the spring-damper model is employed to simulate the dynamic behavior of the tethered net, while the dynamics of the entire system is modeled using multibody dynamics (MBD) to achieve accurate representations of system interactions. The motion of the UAVs and the target are estimated using VINS-MONO and DETR, and the system autonomously executes the capture strategy through MAPPO. Simulation results demonstrate that mySim accurately simulates dynamics and control of the system, successfully enabling the multi-UAV-tethered netted system to capture both non-propelled and maneuvering non-cooperative targets. By providing a high-precision simulation platform that integrates dynamics modeling with perception and learning-based control, mySim enables efficient testing and optimization of UAV-based control policies before real-world deployment. This approach offers significant advantages for simulating complex UAVs coordination tasks and has the potential to be applied to the design of other UAV-based systems.

Figures

Figures reproduced from arXiv: 2506.03297 by the authors.

Figure 12
Figure 12. Schematic diagram of trajectory, during a complete simulation process, based on the observation variables sn output at each time step from the simulation environment, the controller outputs a new action a m n , continuing until the task is completed. Orientation Angular Velocity Position Linear Velocity UAV Time Linear Acceleration Target … … 256 nodes, ReLU 128 nodes, ReLU … 128 nodes, ReLU (a) Orientation Angular … view at source ↗
Figure 10
Figure 10. Dynamic Computing Blender Rendering Visual-Inertial Odometry IMU Simulation System State Estimation Controller Estimated Position & Attitude Estimated System State IMU Signal Acceleration & Angular Velocity Scene Camera Image Sequence Position & Attitude (a) Dynamic Computing Blender Rendering Target Detection Rotatable Camera Camera Controller Target State Estimation Controller Position & Attitude Target Camera Ima… view at source ↗
Figure 10
Figure 10. Based on the detected position of the non-cooperative target, the UAV updates the camera’s orientation to align [PITH_FULL_IMAGE:figures/full_fig_p012_10.png] view at source ↗
Figures from the paper (1 more)
Figure 10
Figure 10. Figure 10: The tracking and detection process for UAV 1 is illustrated in Fig. 25, where ( [PITH_FULL_IMAGE:figures/full_fig_p018_10.png]

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Reference graph

Works this paper leans on

42 extracted references · 37 canonical work pages

  1. [1]

    Uav-based forest health monitoring: A systematic review.Remote Sensing, 14(13):3205, 2022

    Simon Ecke, Jan Dempewolf, Julian Frey, Andreas Schwaller, Ewald Endres, Hans-Joachim Klemmt, Dirk Tiede, and Thomas Seifert. Uav-based forest health monitoring: A systematic review.Remote Sensing, 14(13):3205, 2022

    work page 2022

  2. [2]

    Uav & satellite synergies for optical remote sensing applications: A literature review.Science of remote sensing, 3:100019, 2021

    Emilien Alvarez-Vanhard, Thomas Corpetti, and Thomas Houet. Uav & satellite synergies for optical remote sensing applications: A literature review.Science of remote sensing, 3:100019, 2021

    work page 2021

  3. [3]

    Applications of drone in disaster management: A scoping review.Science & Justice, 62(1):30–42, 2022

    Sharifah Mastura Syed Mohd Daud, Mohd Yusmiaidil Putera Mohd Yusof, Chong Chin Heo, Lay See Khoo, Mansharan Kaur Chainchel Singh, Mohd Shah Mahmood, and Hapizah Nawawi. Applications of drone in disaster management: A scoping review.Science & Justice, 62(1):30–42, 2022

    work page 2022

  4. [4]

    Area reconnaissance modeling of mod- ular reconnaissance robotic systems.The Journal of Defense Modeling and Simulation, page 15485129231210302, 2023

    Jan Nohel, Petr Stodola, Jan Zezula, Pavel Zahradníˇcek, and Zdenˇek Flasar. Area reconnaissance modeling of mod- ular reconnaissance robotic systems.The Journal of Defense Modeling and Simulation, page 15485129231210302, 2023

    work page 2023

  5. [5]

    Xiwang Dong, Yongzhao Hua, Yan Zhou, Zhang Ren, and Yisheng Zhong. Theory and experiment on formation- containment control of multiple multirotor unmanned aerial vehicle systems.IEEE Transactions on Automation Science and Engineering, 16(1):229–240, 2018

    work page 2018

  6. [6]

    Research of multi-rotor uavs detailed autonomous inspection technology of transmission lines based on route planning.IEEE Access, 7:114955–114965, 2019

    Tong He, Yihui Zeng, and Zhuangli Hu. Research of multi-rotor uavs detailed autonomous inspection technology of transmission lines based on route planning.IEEE Access, 7:114955–114965, 2019

    work page 2019

  7. [7]

    Emerging use of uav’s: secure communication protocol issues and challenges

    Navid Ali Khan, Noor Zaman Jhanjhi, Sarfraz Nawaz Brohi, and Anand Nayyar. Emerging use of uav’s: secure communication protocol issues and challenges. InDrones in smart-cities, pages 37–55. Elsevier, 2020

    work page 2020

  8. [8]

    Collaborative multi-robot search and rescue: Planning, coordination, perception, and active vision.Ieee Access, 8:191617–191643, 2020

    Jorge Pena Queralta, Jussi Taipalmaa, Bilge Can Pullinen, Victor Kathan Sarker, Tuan Nguyen Gia, Hannu Tenhunen, Moncef Gabbouj, Jenni Raitoharju, and Tomi Westerlund. Collaborative multi-robot search and rescue: Planning, coordination, perception, and active vision.Ieee Access, 8:191617–191643, 2020

    work page 2020

Show all 42 references
  1. [9]

    Vision-guided aerial manipulation using a multirotor with a robotic arm.IEEE/ASME Transactions On Mechatronics, 21(4):1912–1923, 2016

    Suseong Kim, Hoseong Seo, Seungwon Choi, and H Jin Kim. Vision-guided aerial manipulation using a multirotor with a robotic arm.IEEE/ASME Transactions On Mechatronics, 21(4):1912–1923, 2016

    1912

  2. [10]

    Cooperative aerial manipulation using multirotors with multi-dof robotic arms.IEEE/ASME Transactions on Mechatronics, 23(2):702–713, 2018

    Suseong Kim, Hoseong Seo, Jongho Shin, and H Jin Kim. Cooperative aerial manipulation using multirotors with multi-dof robotic arms.IEEE/ASME Transactions on Mechatronics, 23(2):702–713, 2018

    2018

  3. [11]

    Recent research progress of unmanned aerial vehicle regulation policies and technologies in urban low altitude.Ieee Access, 8:74175–74194, 2020

    Chenchen Xu, Xiaohan Liao, Junming Tan, Huping Ye, and Haiying Lu. Recent research progress of unmanned aerial vehicle regulation policies and technologies in urban low altitude.Ieee Access, 8:74175–74194, 2020

    2020

  4. [12]

    Classification of uav and bird target in low-altitude airspace with surveillance radar data.The Aeronautical Journal, 123(1260):191–211, 2019

    WS Chen, J Liu, and J Li. Classification of uav and bird target in low-altitude airspace with surveillance radar data.The Aeronautical Journal, 123(1260):191–211, 2019

    2019

  5. [13]

    Springer International Publishing, Cham, 2016

    Fadri Furrer, Michael Burri, Markus Achtelik, and Roland Siegwart.Robot Operating System (ROS): The Complete Reference (Volume 1), chapter RotorS—A Modular Gazebo MA V Simulator Framework, pages 595–625. Springer International Publishing, Cham, 2016

    2016

  6. [14]

    Koenig and A

    N. Koenig and A. Howard. Design and use paradigms for gazebo, an open-source multi-robot simulator. In 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566), volume 3, pages 2149–2154 vol.3, 2004

    2004

  7. [15]

    Silano, E

    G. Silano, E. Aucone, and L. Iannelli. CrazyS: A Software-In-The-Loop Platform for the Crazyflie 2.0 Nano- Quadcopter. In2018 26th Mediterranean Conference on Control and Automation (MED), pages 352–357, 2018

    2018

  8. [16]

    Silano and L

    G. Silano and L. Iannelli.Robot Operating System (ROS): The Complete Reference (Volume 4), chapter CrazyS: a software-in-the-loop simulation platform for the Crazyflie 2.0 nano-quadcopter, pages 81–115. Springer International Publishing, 2020

    2020

  9. [17]

    PX4/PX4-Autopilot: V1.15.2

    Lorenz Meier, Daniel Agar, Beat Küng, Julian Oes, Pavel Kirienko, Thomas Gubler, Matthias Grob, Paul Riseborough, David Sidrane, Anton Babushkin, Mathieu Bresciani, px4dev, Silvan Fuhrer, Mark Charlebois, James Goppert, Roman Bapst, Nuno Marques, Andreas Daniel Antener, PX4 Bu...

    2024

  10. [18]

    Schoellig

    Jacopo Panerati, Hehui Zheng, SiQi Zhou, James Xu, Amanda Prorok, and Angela P. Schoellig. Learning to fly—a gym environment with pybullet physics for reinforcement learning of multi-agent quadcopter control. In 2021 IEEE/RSJ International Conference on Intelligent Robots and ...

    2021

  11. [19]

    Pybullet, a python module for physics simulation for games, robotics and machine learning.http://pybullet.org, 2016–2021

    Erwin Coumans and Yunfei Bai. Pybullet, a python module for physics simulation for games, robotics and machine learning.http://pybullet.org, 2016–2021. 27

    2016

  12. [20]

    Airsim: High-fidelity visual and physical simulation for autonomous vehicles

    Shital Shah, Debadeepta Dey, Chris Lovett, and Ashish Kapoor. Airsim: High-fidelity visual and physical simulation for autonomous vehicles. InField and Service Robotics: Results of the 11th International Conference, pages 621–635. Springer, 2018

    2018

  13. [21]

    Mujoco: A physics engine for model-based control

    Emanuel Todorov, Tom Erez, and Yuval Tassa. Mujoco: A physics engine for model-based control. In2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 5026–5033. IEEE, 2012

    2012

  14. [22]

    Pegasus simulator: An isaac sim framework for multiple aerial vehicles simulation

    Marcelo Jacinto, João Pinto, Jay Patrikar, John Keller, Rita Cunha, Sebastian Scherer, and António Pascoal. Pegasus simulator: An isaac sim framework for multiple aerial vehicles simulation. In2024 International Conference on Unmanned Aircraft Systems (ICUAS), pages 917–922, 2024

    2024

  15. [23]

    Isaac gym: High performance gpu-based physics simulation for robot learning, 2021

    Viktor Makoviychuk, Lukasz Wawrzyniak, Yunrong Guo, Michelle Lu, Kier Storey, Miles Macklin, David Hoeller, Nikita Rudin, Arthur Allshire, Ankur Handa, and Gavriel State. Isaac gym: High performance gpu-based physics simulation for robot learning, 2021

    2021

  16. [24]

    Orbit: A unified simulation framework for interactive robot learning environments.IEEE Robotics and Automation Letters, 8(6):3740–3747, 2023

    Mayank Mittal, Calvin Yu, Qinxi Yu, Jingzhou Liu, Nikita Rudin, David Hoeller, Jia Lin Yuan, Ritvik Singh, Yunrong Guo, Hammad Mazhar, Ajay Mandlekar, Buck Babich, Gavriel State, Marco Hutter, and Animesh Garg. Orbit: A unified simulation framework for interactive robot learni...

    2023

  17. [25]

    Flightmare: A flexible quadrotor simulator

    Yunlong Song, Selim Naji, Elia Kaufmann, Antonio Loquercio, and Davide Scaramuzza. Flightmare: A flexible quadrotor simulator. InConference on Robot Learning, 2020

    2020

  18. [26]

    Flightgoggles: Photorealistic sensor simulation for perception-driven robotics using photogrammetry and virtual reality

    Winter Guerra, Ezra Tal, Varun Murali, Gilhyun Ryou, and Sertac Karaman. Flightgoggles: Photorealistic sensor simulation for perception-driven robotics using photogrammetry and virtual reality. In2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), ...

    2019

  19. [27]

    Position based dynamics.Journal of Visual Communication and Image Representation, 18(2):109–118, 2007

    Matthias Müller, Bruno Heidelberger, Marcus Hennix, and John Ratcliff. Position based dynamics.Journal of Visual Communication and Image Representation, 18(2):109–118, 2007

    2007

  20. [28]

    Particle-based simulation of granular materials

    Nathan Bell, Yizhou Yu, and Peter J Mucha. Particle-based simulation of granular materials. InProceedings of the 2005 ACM SIGGRAPH/Eurographics symposium on Computer animation, pages 77–86, 2005

    2005

  21. [29]

    On the simulation of tether-nets for space debris capture with vortex dynamics.Acta Astronautica, 123:91–102, 2016

    Eleonora M Botta, Inna Sharf, Arun K Misra, and Marek Teichmann. On the simulation of tether-nets for space debris capture with vortex dynamics.Acta Astronautica, 123:91–102, 2016

    2016

  22. [30]

    Contact dynamics modeling and simulation of tether nets for space-debris capture.Journal of Guidance, Control, and Dynamics, 40(1):110–123, 2017

    Eleonora M Botta, Inna Sharf, and Arun K Misra. Contact dynamics modeling and simulation of tether nets for space-debris capture.Journal of Guidance, Control, and Dynamics, 40(1):110–123, 2017

    2017

  23. [31]

    Analysis of thin beams and cables using the absolute nodal co-ordinate formulation.Nonlinear Dynamics, 45:109–130, 2006

    Johannes Gerstmayr and Ahmed A Shabana. Analysis of thin beams and cables using the absolute nodal co-ordinate formulation.Nonlinear Dynamics, 45:109–130, 2006

    2006

  24. [32]

    An analysis of the flexibility modeling of a net for space debris removal.Advances in Space Research, 65(3):1083–1094, 2020

    Minghe Shan, Jian Guo, and Eberhard Gill. An analysis of the flexibility modeling of a net for space debris removal.Advances in Space Research, 65(3):1083–1094, 2020

    2020

  25. [33]

    Chrono: a parallel multi-physics library for rigid-body, flexible-body, and fluid dynamics

    Hammad Mazhar, Toby Heyn, Arman Pazouki, Dan Melanz, Andrew Seidl, Aaron Bartholomew, Alessandro Tasora, and Dan Negrut. Chrono: a parallel multi-physics library for rigid-body, flexible-body, and fluid dynamics. Mechanical Sciences, 4(1):49–64, 2013

    2013

  26. [34]

    Chrono: An open source multi-physics dynamics engine

    Alessandro Tasora, Radu Serban, Hammad Mazhar, Arman Pazouki, Daniel Melanz, Jonathan Fleischmann, Michael Taylor, Hiroyuki Sugiyama, and Dan Negrut. Chrono: An open source multi-physics dynamics engine. InHigh Performance Computing in Science and Engineering: Second Internati...

    2015

  27. [35]

    Openvins: A research platform for visual-inertial estimation

    Patrick Geneva, Kevin Eckenhoff, Woosik Lee, Yulin Yang, and Guoquan Huang. Openvins: A research platform for visual-inertial estimation. In2020 IEEE International Conference on Robotics and Automation (ICRA), pages 4666–4672. IEEE, 2020

    2020

  28. [36]

    Orb-slam3: An accurate open-source library for visual, visual–inertial, and multimap slam.IEEE Transactions on Robotics, 37(6):1874–1890, 2021

    Carlos Campos, Richard Elvira, Juan J Gómez Rodríguez, José MM Montiel, and Juan D Tardós. Orb-slam3: An accurate open-source library for visual, visual–inertial, and multimap slam.IEEE Transactions on Robotics, 37(6):1874–1890, 2021

    2021

  29. [37]

    von Stumberg and D

    L. von Stumberg and D. Cremers. DM-VIO: Delayed marginalization visual-inertial odometry.IEEE Robotics and Automation Letters (RA-L), 7(2):1408–1415, 2022

    2022

  30. [38]

    Stable- baselines3: Reliable reinforcement learning implementations.Journal of Machine Learning Research, 22(268):1–8, 2021

    Antonin Raffin, Ashley Hill, Adam Gleave, Anssi Kanervisto, Maximilian Ernestus, and Noah Dormann. Stable- baselines3: Reliable reinforcement learning implementations.Journal of Machine Learning Research, 22(268):1–8, 2021

    2021

  31. [39]

    The surprising effectiveness of ppo in cooperative multi-agent games.Advances in Neural Information Processing Systems, 35:24611–24624, 2022

    Chao Yu, Akash Velu, Eugene Vinitsky, Jiaxuan Gao, Yu Wang, Alexandre Bayen, and Yi Wu. The surprising effectiveness of ppo in cooperative multi-agent games.Advances in Neural Information Processing Systems, 35:24611–24624, 2022. 28

    2022

  32. [40]

    Vins-mono: A robust and versatile monocular visual-inertial state estimator.IEEE transactions on robotics, 34(4):1004–1020, 2018

    Tong Qin, Peiliang Li, and Shaojie Shen. Vins-mono: A robust and versatile monocular visual-inertial state estimator.IEEE transactions on robotics, 34(4):1004–1020, 2018

    2018

  33. [41]

    End-to-end object detection with transformers

    Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-end object detection with transformers. InEuropean conference on computer vision, pages 213–229. Springer, 2020

    2020

  34. [42]

    Identity mappings in deep residual networks

    Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Identity mappings in deep residual networks. In Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part IV 14, pages 630–645. Springer, 2016. 29

    2016

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.