pith. sign in

arxiv: 2509.10692 · v4 · submitted 2025-09-12 · 💻 cs.RO

STL-Based Motion Planning and Uncertainty-Aware Risk Analysis for Human-Robot Collaboration with a Multi-Rotor Aerial Vehicle

Giuseppe Silano , Amr Afifi , Martin Saska , Antonio Franchi This is my paper

Pith reviewed 2026-05-18 17:05 UTC · model grok-4.3

classification 💻 cs.RO
keywords signal temporal logicmotion planninghuman-robot collaborationmulti-rotor aerial vehiclerisk analysisevent-triggered replanninguncertainty handlingtrajectory optimization
0
0 comments X

The pith

Signal temporal logic specifications let a multi-rotor vehicle plan safe trajectories for human collaboration while quantifying risks from pose uncertainty and replanning on events.

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

The paper sets out to show that complex collaboration goals can be turned into precise, checkable rules using signal temporal logic. These rules cover keeping safe distances, meeting time limits, and matching human ergonomic preferences during tasks like object handover. An optimization routine then finds flight paths that respect the vehicle's actual motion limits and actuator power. The method adds a layer that estimates how likely those rules are to break when the human's exact position is uncertain and switches to a fresh plan whenever safety margins drop. A reader would care because this combination promises reliable drone assistance in shared spaces without needing perfect knowledge of the human or constant manual fixes.

Core claim

The paper claims that mission objectives for human-robot collaboration, including safety, temporal requirements, and human preferences such as ergonomics and comfort, can be encoded as signal temporal logic specifications. These specifications drive an optimization-based planner that produces dynamically feasible trajectories while respecting the multi-rotor vehicle's nonlinear dynamics and actuation constraints. Smooth robustness approximations and gradient-based techniques are used to solve the resulting non-convex problem. An uncertainty-aware risk analysis then quantifies the likelihood of specification violations under human-pose uncertainty, and a robustness-aware event-triggered repla

What carries the argument

Signal temporal logic specifications that encode safety, timing, and ergonomic requirements, used inside an optimization planner with smoothed robustness measures, uncertainty-aware risk quantification, and robustness-triggered replanning.

If this is right

  • The generated trajectories keep required safety distances, satisfy timing windows, and respect ergonomic preferences for the human partner.
  • Risk values give a concrete probability that human position errors will cause a rule violation during execution.
  • Event-triggered replanning restores robustness margins after disturbances without stopping the overall task.
  • The planner accounts for the full nonlinear vehicle dynamics and actuator limits while still running at usable speeds.

Where Pith is reading between the lines

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

  • The same encoding of rules and risk checks could be tried on longer-duration missions or tasks with multiple humans to test whether replanning stays fast enough.
  • Pairing the uncertainty model with onboard cameras for live human tracking might shrink the safety margins the system must keep.
  • The approach could be ported to ground vehicles or robotic arms in similar shared workspaces to see if the core machinery transfers.

Load-bearing premise

Smooth approximations of the temporal logic robustness turn the non-convex planning problem into one that gradient-based solvers can handle reliably in real time without poor local solutions or constraint violations.

What would settle it

In the Gazebo simulation of the object handover, increase the level of human pose noise and measure whether the actual rate of safety or comfort violations exceeds the risk probabilities computed by the analysis or whether replanning fails to restore margins before a violation occurs.

Figures

Figures reproduced from arXiv: 2509.10692 by Amr Afifi, Antonio Franchi, Giuseppe Silano, Martin Saska.

Figure 1
Figure 1. Figure 1: Illustration of an MRAV facilitating tool delivery to a human worker in a power line scenario. [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Schematic depiction of the object handover scenario, highlighting the operator’s preferred handover [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Schematic representation of an GTMR system with its world [PITH_FULL_IMAGE:figures/full_fig_p010_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Illustration of the expected value E(Z), β-Value-at-Risk VaRβ(Z), and Conditional β-Value-at￾Risk CVaRβ(Z) for a specified risk level β ∈ (0, 1). The axes represent the stochastic variable z and its CDF FZ (z). The shaded area corresponds to %β of the total area under FZ (z). VaRβ(Z) represents the value of z at the β-tail of the distribution, while CVaRβ(Z) averages the worst-case values of z in the β-tai… view at source ↗
Figure 5
Figure 5. Figure 5: Object handover scenario with highlighted approaching regions (yellow) representing the er [PITH_FULL_IMAGE:figures/full_fig_p017_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: System Architecture: The STL Motion Planner at the ground station generates optimal trajectories (x ⋆, u ⋆) for the MRAV. These trajectories are then fed into the Tracking Controller, which operates in closed loop to compute the motor forces ξ, ensuring precise flight maneuvers. the MRAV adheres to the mission requirements. For instance, a positive robustness score indicates that the MRAV is within the spe… view at source ↗
Figure 7
Figure 7. Figure 7: Snapshots from Gazebo simulations illustrating a left-handed, top-to-bottom preferred approach [PITH_FULL_IMAGE:figures/full_fig_p024_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Planned trajectory for the MRAV, showing a left-handed, top-to-bottom preferred approach direc [PITH_FULL_IMAGE:figures/full_fig_p026_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Position, orientation, linear and angular velocities, propeller speed, and velocity magnitude for the [PITH_FULL_IMAGE:figures/full_fig_p027_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Planned trajectory for the MRAV, showing a left-handed, top-to-bottom preferred approach direc [PITH_FULL_IMAGE:figures/full_fig_p028_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Comparison of the normalized energy term, MRAV linear and angular velocities, and propeller [PITH_FULL_IMAGE:figures/full_fig_p029_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Event-driven replanner trajectories for two disturbance scenarios, showing the MRAV’s left [PITH_FULL_IMAGE:figures/full_fig_p030_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Histogram of the smooth robustness score [PITH_FULL_IMAGE:figures/full_fig_p031_13.png] view at source ↗
read the original abstract

This paper presents a motion planning and risk analysis framework for enhancing human-robot collaboration with a Multi-Rotor Aerial Vehicle. The proposed method employs Signal Temporal Logic to encode key mission objectives, including safety, temporal requirements, and human preferences, with particular emphasis on ergonomics and comfort. An optimization-based planner generates dynamically feasible trajectories while explicitly accounting for the vehicle's nonlinear dynamics and actuation constraints. To address the resulting non-convex and non-smooth optimization problem, smooth robustness approximations and gradient-based techniques are adopted. In addition, an uncertainty-aware risk analysis is introduced to quantify the likelihood of specification violations under human-pose uncertainty. A robustness-aware event-triggered replanning strategy further enables online recovery from disturbances and unforeseen events by preserving safety margins during execution. The framework is validated through MATLAB and Gazebo simulations on an object handover task inspired by power line maintenance scenarios. Results demonstrate the ability of the proposed method to achieve safe, efficient, and resilient human-robot collaboration under realistic operating conditions.

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

2 major / 2 minor

Summary. The paper presents a motion planning and risk analysis framework for human-robot collaboration using a multi-rotor aerial vehicle. Mission objectives including safety, temporal requirements, ergonomics, and comfort are encoded via Signal Temporal Logic (STL). An optimization-based planner generates dynamically feasible trajectories subject to nonlinear quadrotor dynamics and actuation limits; smooth robustness approximations enable gradient-based solution of the resulting non-convex program. An uncertainty-aware risk analysis quantifies specification-violation likelihood under human-pose uncertainty, and a robustness-aware event-triggered replanning strategy supports online recovery. The approach is validated in MATLAB and Gazebo simulations on an object-handover task inspired by power-line maintenance.

Significance. If the empirical results and optimization reliability hold, the work would offer a concrete pipeline for STL-specified aerial HRC that explicitly handles human uncertainty and provides recovery mechanisms. The emphasis on ergonomics within STL and the combination of risk analysis with event-triggered replanning are constructive contributions to safe aerial robotics. The significance is currently limited by the absence of reported quantitative metrics, success rates, or formal guarantees on the approximations.

major comments (2)
  1. [Optimization paragraph] Optimization paragraph: the central claim that the framework produces safe, dynamically feasible trajectories in real time rests on reliable solution of the non-convex STL-robustness program. No initialization strategy, multi-start procedure, or Monte-Carlo success-rate statistics under varying human poses are reported; without such evidence the assumption that gradient descent on the smoothed problem avoids infeasible local minima remains unverified.
  2. [Validation / Results section] Validation / Results section: the abstract states that simulations demonstrate safe, efficient, and resilient collaboration, yet no quantitative metrics (e.g., STL robustness values, violation probabilities, computation times, or success rates across trials) are supplied. This absence prevents assessment of whether the smooth approximations preserve the original STL semantics or whether the risk-analysis bounds are tight.
minor comments (2)
  1. [Preliminaries] Notation for the STL robustness function and the uncertainty model should be introduced with explicit definitions before their use in the optimization objective.
  2. [Figures] Figure captions for the Gazebo simulation snapshots should include the corresponding STL robustness values and risk estimates at the depicted instants.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback and detailed comments on our manuscript. We address each major comment point by point below, outlining the revisions we intend to make to strengthen the presentation of the optimization reliability and quantitative validation results.

read point-by-point responses

  1. Referee: [Optimization paragraph] Optimization paragraph: the central claim that the framework produces safe, dynamically feasible trajectories in real time rests on reliable solution of the non-convex STL-robustness program. No initialization strategy, multi-start procedure, or Monte-Carlo success-rate statistics under varying human poses are reported; without such evidence the assumption that gradient descent on the smoothed problem avoids infeasible local minima remains unverified.

    Authors: We agree that explicit evidence for solver reliability is needed to support the claims. The current manuscript describes the use of smooth robustness approximations to enable gradient-based optimization but does not detail the initialization procedure or provide statistical success rates. In the revised version we will add a dedicated paragraph on the optimization implementation, specifying the warm-start initialization from the previous planning cycle's solution and reporting Monte-Carlo results (e.g., success rates over 100 trials with randomized human poses) to demonstrate that feasible trajectories are consistently obtained. revision: yes

  2. Referee: [Validation / Results section] Validation / Results section: the abstract states that simulations demonstrate safe, efficient, and resilient collaboration, yet no quantitative metrics (e.g., STL robustness values, violation probabilities, computation times, or success rates across trials) are supplied. This absence prevents assessment of whether the smooth approximations preserve the original STL semantics or whether the risk-analysis bounds are tight.

    Authors: We concur that the absence of quantitative metrics limits the ability to evaluate the approximations and risk analysis. The revised Results section will be expanded to include tables reporting average and minimum STL robustness values, empirical violation probabilities obtained from the uncertainty-aware analysis, mean and worst-case computation times, and success rates across repeated simulation trials under varying conditions. These additions will allow direct assessment of approximation fidelity and bound tightness while preserving the existing qualitative simulation descriptions. revision: yes

Circularity Check

0 steps flagged

No significant circularity; forward engineering pipeline with independent components

full rationale

The paper presents a motion planning framework that encodes objectives via STL, solves a non-convex optimization with smooth approximations and gradient-based methods, adds uncertainty-aware risk quantification, and uses event-triggered replanning. No equations or steps reduce a claimed prediction or result to a fitted parameter or self-citation by construction. The derivation chain consists of standard engineering choices (STL robustness, nonlinear dynamics constraints, Monte-Carlo risk analysis) whose outputs are validated in simulation rather than forced by redefinition of inputs. The central claims remain self-contained against external benchmarks such as simulation outcomes and do not rely on load-bearing self-citations or ansatzes imported from prior author work.

Axiom & Free-Parameter Ledger

1 free parameters · 2 axioms · 0 invented entities

The framework rests on standard assumptions about quadrotor dynamics and actuation limits plus the existence of accurate human-pose uncertainty models. No new physical entities are postulated.

free parameters (1)
  • STL robustness weights and optimization cost coefficients
    Typical in STL planning; chosen to balance safety, comfort, and efficiency but not reported in abstract.
axioms (2)
  • domain assumption The vehicle's nonlinear dynamics and actuation constraints can be incorporated into a non-convex optimization problem that remains tractable with smooth approximations.
    Invoked when describing the planner that accounts for nonlinear dynamics.
  • domain assumption Human pose uncertainty can be modeled sufficiently well to compute meaningful violation probabilities.
    Central to the uncertainty-aware risk analysis step.

pith-pipeline@v0.9.0 · 5710 in / 1504 out tokens · 33249 ms · 2026-05-18T17:05:16.291575+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

64 extracted references · 64 canonical work pages

  1. [1]

    In: 2013 American Control Conference, pp

    Abbas, H., Fainekos, G.: Computing descent direction of MTL robustness for non-linear systems. In: 2013 American Control Conference, pp. 4405–4410 (2013). DOI 10.1109/ACC.2013.6580518. URL https://ieeexplore.ieee.org/document/6580518

    work page doi:10.1109/acc.2013.6580518 2013

  2. [2]

    ACM Transaction on Embedded Computing Systems12(2s) Title Suppressed Due to Excessive Length 35 (2013)

    Abbas, H., Fainekos, G., Sankaranarayanan, S., Ivan ˇci´c, F., Gupta, A.: Probabilistic Temporal Logic Falsification of Cyber-Physical Systems. ACM Transaction on Embedded Computing Systems12(2s) Title Suppressed Due to Excessive Length 35 (2013). DOI doi.org/10.1145/2465787.2465797. URLhttps://dl.acm.org/doi/10.1145/ 2465787.2465797

    work page doi:10.1145/2465787.2465797 2013

  3. [3]

    OPV2V: An open benchmark dataset and fusion pipeline for perception with vehicle-to-vehicle communication,

    Afifi, A., van Holland, M., Franchi, A.: Toward Physical Human-Robot Interaction Control with Aerial Manipulators: Compliance, Redundancy Resolution, and Input Limits. In: 2022 International Conference on Robotics and Automation, pp. 4855–4861 (2022). DOI 10.1109/ICRA46639.2022. 9812451. URLhttps://ieeexplore.ieee.org/document/9812451

    work page doi:10.1109/icra46639.2022 2022

  4. [4]

    Autonomous Robots42(5), 957–975 (2018)

    Ajoudani, A., Zanchettin, A.M., Ivaldi, S., Albu-Schffer, A., Kosuge, K., Khatib, O.: Progress and prospects of the human-robot collaboration. Autonomous Robots42(5), 957–975 (2018). DOI 10.1007/s10514-017-9677-2. URLhttps://link.springer.com/article/10.1007/ s10514-017-9677-2

    work page doi:10.1007/s10514-017-9677-2 2018

  5. [5]

    IEEE Access8, 201300–201316 (2020)

    Alc ´antara, A., Capit ´an, J., Torres-Gonz ´alez, A., Cunha, R., Ollero, A.: Autonomous Execution of Cinematographic Shots With Multiple Drones. IEEE Access8, 201300–201316 (2020). DOI 10.1109/ ACCESS.2020.3036239. URLhttps://ieeexplore.ieee.org/document/9249238

    work page arXiv 2020

  6. [6]

    Information Processing Letters21, 181–185 (1985)

    Alpern, B., Schneider, F.B.: Defining liveness. Information Processing Letters21, 181–185 (1985). DOI 10.1016/0020-0190(85)90056-0. URLhttps://www.sciencedirect.com/ science/article/abs/pii/0020019085900560

    work page doi:10.1016/0020-0190(85)90056-0 1985

  7. [7]

    In: International Conference on Formal Modeling and Anal- ysis of Timed Systems, pp

    Barbosa, F.S., Karlsson, J., Tajvar, P., Tumova, J.: Formal Methods for Robot Motion Planning with Time and Space Constraints. In: International Conference on Formal Modeling and Anal- ysis of Timed Systems, pp. 1–10 (2021). DOI 10.1007/978-3-030-85037-1 1. URLhttps: //link.springer.com/chapter/10.1007/978-3-030-85037-1_1

    work page doi:10.1007/978-3-030-85037-1 2021

  8. [8]

    IEEE Robotics & Automation Magazine 14(1), 61–70 (2007)

    Belta, C., Bicchi, A., Egerstedt, M., Frazzoli, E., Klavins, E., Pappas, G.J.: Symbolic planning and control of robot motion [Grand Challenges of Robotics]. IEEE Robotics & Automation Magazine 14(1), 61–70 (2007). DOI 10.1109/MRA.2007.339624. URLhttps://ieeexplore.ieee. org/document/4141034

    work page doi:10.1109/mra.2007.339624 2007

  9. [9]

    IEEE Robotics and Automation Letters7(4), 9129–9136 (2022)

    Benzi, F., Brunner, M., Tognon, M., Secchi, C., Siegwart, R.: Adaptive Tank-based Control for Aerial Physical Interaction with Uncertain Dynamic Environments Using Energy-Task Estimation. IEEE Robotics and Automation Letters7(4), 9129–9136 (2022). DOI 10.1109/LRA.2022.3190074. URL https://ieeexplore.ieee.org/document/9826901

    work page doi:10.1109/lra.2022.3190074 2022

  10. [10]

    In: 2016 IEEE International Conference on Robotics and Automation, pp

    Berkenkamp, F., Schoellig, A.P., Krause, A.: Safe controller optimization for quadrotors with Gaus- sian processes. In: 2016 IEEE International Conference on Robotics and Automation, pp. 491– 496 (2016). DOI 10.1109/ICRA.2016.7487170. URLhttps://ieeexplore.ieee.org/ document/7487170

    work page doi:10.1109/icra.2016.7487170 2016

  11. [11]

    Athena Scientific (2012)

    Bertsekas, D.: Dynamic programming and optimal control. Athena Scientific (2012). URLhttp: //www.athenasc.com/dpbook.html

    work page 2012

  12. [12]

    Au- tonomous Robots10, 185–202 (2001)

    Butler, J., Agah, A.: Psychological effects of behavior patterns of a mobile personal robot. Au- tonomous Robots10, 185–202 (2001). DOI 10.1023/A:1008986004181. URLhttps://link. springer.com/article/10.1023/A:1008986004181

    work page doi:10.1023/a:1008986004181 2001

  13. [13]

    IEEE Access11, 81361–81377 (2023)

    Caballero, A., Silano, G.: A Signal Temporal Logic Motion Planner for Bird Diverter Installation Tasks with Multi-Robot Aerial Systems. IEEE Access11, 81361–81377 (2023). DOI 10.1109/ ACCESS.2023.3300240. URLhttps://ieeexplore.ieee.org/document/10197369

    work page arXiv 2023

  14. [14]

    In: Companion of the 2024 ACM/IEEE In- ternational Conference on Human-Robot Interaction, pp

    Cauchard, J.R., Dutau, C., Corsini, G., Cognetti, M., Sidobre, D., Lacroix, S., Brock, A.M.: Con- siderations for Handover and Co-working with Drones. In: Companion of the 2024 ACM/IEEE In- ternational Conference on Human-Robot Interaction, pp. 302–306 (2024). DOI 10.1145/3610978. 3640590. URLhttps://dl.acm.org/doi/10.1145/3610978.3640590

    work page doi:10.1145/3610978 2024

  15. [15]

    In: Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp

    Cauchard, J.R., Kevin, J.L.E., Zhai, Y ., Landay, J.A.: Drone & me: an exploration into natural human- drone interaction. In: Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp. 361–365 (2015). DOI 10.1145/2750858.2805823. URLhttps: //dl.acm.org/doi/abs/10.1145/2750858.2805823

    work page doi:10.1145/2750858.2805823 2015

  16. [16]

    In: 2022 IEEE International Conference on Intelligent Robots and Systems, pp

    Corsini, G., Jacquet, M., Das, H., Afifi, A., Sidobre, D., Franchi, A.: Nonlinear Model Predictive Control for Human-Robot Handover with Application to the Aerial Case. In: 2022 IEEE International Conference on Intelligent Robots and Systems, pp. 7597–7604 (2022). DOI 10.1109/IROS47612. 2022.9981045. URLhttps://ieeexplore.ieee.org/document/9981045

    work page doi:10.1109/iros47612 2022

  17. [17]

    2021 , url =

    Cortez, W.S., Verginis, C.K., Dimarogonas, D.V .: Safe, Passive Control for Mechanical Systems with Application to Physical Human-Robot Interactions. In: 2021 IEEE International Conference on Robotics and Automation, pp. 3836–3842 (2021). DOI 10.1109/ICRA48506.2021.9561981. URL https://ieeexplore.ieee.org/document/9561981

    work page doi:10.1109/icra48506.2021.9561981 2021

  18. [18]

    36 Giuseppe Silano 1,2⋆ et al

    Dautenhahn, K., Walters, M., Woods, S., Koay, K.L., Nehaniv, C.L., Sisbot, A., Alami, R., Simeon, T.: How may i serve you? a robot companion approaching a seated person in a helping context. 36 Giuseppe Silano 1,2⋆ et al. In: Proceedings of the 1st ACM SIGCHI/SIGART conference on Humanrobot interaction, pp. 172– 179 (2006). DOI 10.1145/1121241.1121272. UR...

    work page doi:10.1145/1121241.1121272 2006

  19. [19]

    IEEE Robotics & Automation Magazine32(2), 153–166 (2025)

    Dimmig, C.A., Silano, G., McGuire, K., Gabellieri, C., H ¨onig, W., Moore, J., Kobilarov, M.: Survey of Simulators for Aerial Robots: An Overview and In-Depth Systematic Comparisons [Survey]. IEEE Robotics & Automation Magazine32(2), 153–166 (2025). DOI 10.1109/MRA.2024.3433171. URL https://ieeexplore.ieee.org/document/10665978

    work page doi:10.1109/mra.2024.3433171 2025

  20. [20]

    In: International Conference on Formal Modeling and Analysis of Timed Systems, pp

    Donz ´e, A., Maler, O.: Robust satisfaction of temporal logic over real-valued signals. In: International Conference on Formal Modeling and Analysis of Timed Systems, pp. 92–106. Springer (2010). DOI 10.1007/978-3-642-15297-9 9. URLhttps://link.springer.com/chapter/10.1007/ 978-3-642-15297-9_9

    work page doi:10.1007/978-3-642-15297-9 2010

  21. [21]

    International Journal on Software Tools for Technology Transfer9, 1–4 (2007)

    D’Souza, D., Prabhakar, P.: On the expressiveness of MTL in the pointwise and continuous se- mantics. International Journal on Software Tools for Technology Transfer9, 1–4 (2007). DOI 10.1007/s10009-005-0214-9. URLhttps://link.springer.com/article/10.1007/ s10009-005-0214-9

    work page doi:10.1007/s10009-005-0214-9 2007

  22. [23]

    Durrett, R.: Probability: theory and examples, vol. 49. Cambridge university press (2019). URLhttps://www.amazon.it/Probability-Theory-Examples-Rick-Durrett/ dp/0521765390

    work page arXiv 2019

  23. [24]

    Theoretical Computer Science410(42), 4262–4291 (2009)

    Fainekos, G.E., Pappas, G.J.: Robustness of temporal logic specifications for continuous-time signals. Theoretical Computer Science410(42), 4262–4291 (2009). DOI 10.1016/j.tcs.2009.06.021. URL https://www.sciencedirect.com/science/article/pii/S0304397509004149

    work page doi:10.1016/j.tcs.2009.06.021 2009

  24. [25]

    and Jackson, Brian E

    Garrell, A., Coll, C., Alqu ´ezar, R., Sanfeliu, A.: Teaching a Drone to Accompany a Person from Demonstrations using Non-Linear ASFM. In: 2019 IEEE International Conference on Intelligent Robots and Systems, pp. 1985–1991 (2019). DOI 10.1109/IROS40897.2019.8967675. URLhttps: //ieeexplore.ieee.org/document/8967675

    work page doi:10.1109/iros40897.2019.8967675 2019

  25. [26]

    In: 2017 IEEE International Conference on Intelligent Robots and Systems, pp

    Garrell, A., Garza-Elizondo, L., Villamizar, M., Herrero, F., Sanfeliu, A.: Aerial social force model: A new framework to accompany people using autonomous flying robots. In: 2017 IEEE International Conference on Intelligent Robots and Systems, pp. 7011–7017 (2017). DOI 10.1109/IROS.2017. 8206627. URLhttps://ieeexplore.ieee.org/document/8206627

    work page doi:10.1109/iros.2017 2017

  26. [27]

    Distributionally robust sampling-based motion planning under uncertainty

    Gienger, M., Ruiken, D., Bates, T., Regaieg, M., MeiBner, M., Kober, J., Seiwald, P., Hildebrandt, A.C.: Human-Robot Cooperative Object Manipulation with Contact Changes. In: 2018 IEEE Inter- national Conference on Intelligent Robots and Systems, pp. 1354–1360 (2018). DOI 10.1109/IROS. 2018.8594140. URLhttps://ieeexplore.ieee.org/document/8594140

    work page doi:10.1109/iros 2018

  27. [28]

    IEEE Control Systems Letters 5, 241–246

    Gilpin, Y ., Kurtz, V ., Lin, H.: A Smooth Robustness Measure of Signal Temporal Logic for Symbolic Control. IEEE Control Systems Letters5(1), 241–246 (2021). DOI 10.1109/LCSYS.2020.3001875. URLhttps://ieeexplore.ieee.org/document/9114883

    work page doi:10.1109/lcsys.2020.3001875 2021

  28. [29]

    Haddadin, S., Suppa, M., Fuchs, S., Bodenm ¨uller, T., Albu-Sch¨affer, A., Hirzinger, G.: Towards the robotic co-worker. In: C. Pradalier, R. Siegwart, G. Hirzinger (eds.) Robotics Research, pp. 261–282. Springer Berlin Heidelberg (2011). DOI 10.1007/978-3-642-19457-3 16. URLhttps://link. springer.com/chapter/10.1007/978-3-642-19457-3_16

    work page doi:10.1007/978-3-642-19457-3 2011

  29. [30]

    The International Journal of Robotics Research40(8-9), 1015–1044 (2021)

    Hamandi, M., Usai, F., Sabl ´e, Q., Staub, N., Tognon, M., Franchi, A.: Design of multirotor aerial vehi- cles: A taxonomy based on input allocation. The International Journal of Robotics Research40(8-9), 1015–1044 (2021). DOI 10.1177/02783649211025998. URLhttps://journals.sagepub. com/doi/full/10.1177/02783649211025998

    work page doi:10.1177/02783649211025998 2021

  30. [31]

    Herdel, V ., Lee, J.Y ., Cauchard, J.R.: Above and Beyond: A Scoping Review of Domains and Ap- plications for Human-Drone Interaction. pp. 1–22 (2022). DOI 10.1145/3491102.3501881. URL https://dl.acm.org/doi/10.1145/3491102.3501881

    work page doi:10.1145/3491102.3501881 2022

  31. [32]

    IEEE Robotics and Automation Letters6(4), 7580–7587 (2021)

    Kratky, V ., Alcantara, A., Capitan, J., Stepan, P., Saska, M., Ollero, A.: Autonomous Aerial Film- ing With Distributed Lighting by a Team of Unmanned Aerial Vehicles. IEEE Robotics and Automation Letters6(4), 7580–7587 (2021). DOI 10.1109/LRA.2021.3098811. URLhttps: //ieeexplore.ieee.org/document/9495218

    work page doi:10.1109/lra.2021.3098811 2021

  32. [33]

    Robotics and Autonomous Systems61(12), 1726–1743 (2013)

    Kruse, T., Pandey, A.K., Alami, R., Kirsch, A.: Human-aware robot navigation: A survey. Robotics and Autonomous Systems61(12), 1726–1743 (2013). DOI 10.1016/j.robot.2013.05.007. URL https://www.sciencedirect.com/science/article/pii/S0921889013001048 Title Suppressed Due to Excessive Length 37

    work page doi:10.1016/j.robot.2013.05.007 2013

  33. [34]

    and Jackson, Brian E

    Kshirsagar, A., Kress-Gazit, H., Hoffman, G.: Specifying and Synthesizing Human-Robot Han- dovers. In: 2019 IEEE International Conference on Intelligent Robots and Systems, pp. 5930–5936 (2019). DOI 10.1109/IROS40897.2019.8967709. URLhttps://ieeexplore.ieee.org/ document/8967709

    work page doi:10.1109/iros40897.2019.8967709 2019

  34. [35]

    In: International Conference on Tools and Algorithms for the Construction and Analysis of Systems, pp

    Li, W., Sadigh, D., Sastry, S.S., Seshia, S.A.: Synthesis for human-in-the-loop control systems. In: International Conference on Tools and Algorithms for the Construction and Analysis of Systems, pp. 470–484 (2014). DOI 10.1007/978-3-642-54862-8 40. URLhttps://link.springer.com/ chapter/10.1007/978-3-642-54862-8_40

    work page doi:10.1007/978-3-642-54862-8 2014

  35. [36]

    IEEE Communications Magazine63(5), 94–100 (2025)

    Licea, D.B., Silano, G., Hammouti, H.E., Ghogho, M., Saska, M.: Reshaping UA V-Enabled Commu- nications with Omnidirectional Multi-Rotor Aerial Vehicles. IEEE Communications Magazine63(5), 94–100 (2025). DOI 10.1109/MCOM.001.2400421. URLhttps://ieeexplore.ieee.org/ document/10829762

    work page doi:10.1109/mcom.001.2400421 2025

  36. [38]

    ACM Transactions on Embedded Computing Systems22(3), 1–31 (2023)

    Lindemann, L., Jiang, L., Matni, N., Pappas, G.J.: Risk of Stochastic Systems for Temporal Logic Specifications. ACM Transactions on Embedded Computing Systems22(3), 1–31 (2023). DOI 10.1145/3580490. URLhttps://dl.acm.org/doi/full/10.1145/3580490

    work page doi:10.1145/3580490 2023

  37. [39]

    Agafonov, P

    Lindemann, L., Matni, N., Pappas, G.J.: STL Robustness Risk over Discrete-Time Stochastic Pro- cesses. In: 60th IEEE Conference on Decision and Control, pp. 1329–1335 (2021). DOI 10.1109/ CDC45484.2021.9683305. URLhttps://ieeexplore.ieee.org/document/9683305

    work page arXiv 2021

  38. [40]

    In: Pro- ceedings of the 25th ACM International Conference on Hybrid Systems: Computation and Control, pp

    Lindemann, L., Rodionova, A., Pappas, G.J.: Temporal Robustness of Stochastic Signals. In: Pro- ceedings of the 25th ACM International Conference on Hybrid Systems: Computation and Control, pp. 1–11 (2022). DOI 10.1145/3501710.3519504. URLhttps://dl.acm.org/doi/abs/10. 1145/3501710.3519504

    work page doi:10.1145/3501710.3519504 2022

  39. [41]

    The International Journal of Robotics Research37(11), 1341–1358 (2018)

    Loianno, G., Mulgaonkar, Y ., Brunner, C., Ahuja, D., Ramanandan, A., Chari, M., Diaz, S., Ku- mar, V .: Autonomous flight and cooperative control for reconstruction using aerial robots pow- ered by smartphones. The International Journal of Robotics Research37(11), 1341–1358 (2018). DOI 10.1177/0278364918774136. URLhttps://journals.sagepub.com/doi/abs/10....

    work page doi:10.1177/0278364918774136 2018

  40. [42]

    Robotics Research pp

    Majumdar, A., Pavone, M.: How should a robot assess risk? Towards an axiomatic theory of risk in robotics. Robotics Research pp. 75–84 (2020). DOI 10.1007/978-3-030-28619-4 10. URLhttps: //link.springer.com/chapter/10.1007/978-3-030-28619-4_10

    work page doi:10.1007/978-3-030-28619-4 2020

  41. [43]

    In: Formal Tech- niques, Modelling and Analysis of Timed and Fault-Tolerant Systems, pp

    Maler, O., Nickovic, D.: Monitoring temporal properties of continuous signals. In: Formal Tech- niques, Modelling and Analysis of Timed and Fault-Tolerant Systems, pp. 152–166. Springer, Berlin, Heidelberg (2004). DOI 10.1007/978-3-540-30206-3 12. URLhttps://link.springer. com/chapter/10.1007/978-3-540-30206-3_12

    work page doi:10.1007/978-3-540-30206-3 2004

  42. [44]

    In: IEEE International Conference on Robotics and Automation, pp

    Mallet, A., Pasteur, C., Herrb, M., Lemaignan, S., Ingrand, F.: GenoM3: Building middleware- independent robotic components. In: IEEE International Conference on Robotics and Automation, pp. 4627–4632 (2010). DOI 10.1109/ROBOT.2010.5509539. URLhttps://ieeexplore.ieee. org/document/5509539

    work page doi:10.1109/robot.2010.5509539 2010

  43. [45]

    ACM Transactions on Graphics36(4), 1–10 (2017)

    Nageli, T., Meier, L., Domahidi, A., Alonso-Mora, J., Hilliges, O.: Real-time planning for automated multi-view drone cinematography. ACM Transactions on Graphics36(4), 1–10 (2017). DOI 10.1145/ 3072959.3073712. URLhttps://dl.acm.org/doi/10.1145/3072959.3073712

    work page doi:10.1145/3072959.3073712 2017

  44. [47]

    IEEE Transactions on Robotics37(6), 1855–1873 (2021)

    Ortenzi, V ., Cosgun, A., Pardi, T., Chan, W.P., Croft, E., Kuli ´c, D.: Object Handovers: A Review for Robotics. IEEE Transactions on Robotics37(6), 1855–1873 (2021). DOI 10.1109/TRO.2021. 3075365. URLhttps://ieeexplore.ieee.org/document/9444288

    work page doi:10.1109/tro.2021 2021

  45. [48]

    In: 2017 IEEE Conference on Control Technology and Applications, pp

    Pant, Y .V ., Abbas, H., Mangharam, R.: Smooth operator: Control using the smooth robustness of temporal logic. In: 2017 IEEE Conference on Control Technology and Applications, pp. 1235– 1240 (2017). DOI 10.1109/CCTA.2017.8062628. URLhttps://ieeexplore.ieee.org/ document/8062628

    work page doi:10.1109/ccta.2017.8062628 2017

  46. [49]

    In: 2017 IEEE 17th International Conference on Humanoid Robotics, pp

    Peternel, L., Kim, W., Babi ˇc, J., Ajoudani, A.: Towards ergonomic control of human-robot co- manipulation and handover. In: 2017 IEEE 17th International Conference on Humanoid Robotics, pp. 55–60 (2017). DOI 10.1109/HUMANOIDS.2017.8239537. URLhttps://ieeexplore. ieee.org/document/8239537 38 Giuseppe Silano 1,2⋆ et al

    work page doi:10.1109/humanoids.2017.8239537 2017

  47. [50]

    IEEE Robotics and Automation Letters6(4), 7596–7603 (2021)

    Petracek, P., Kratky, V ., Petrlik, M., Baca, T., Kratochvil, R., Saska, M.: Large-Scale Exploration of Cave Environments by Unmanned Aerial Vehicles. IEEE Robotics and Automation Letters6(4), 7596–7603 (2021). DOI 10.1109/LRA.2021.3098304. URLhttps://ieeexplore.ieee. org/document/9492802

    work page doi:10.1109/lra.2021.3098304 2021

  48. [51]

    In: 53rd IEEE Conference on Decision and Control, pp

    Raman, V ., Donz ´e, A., Maasoumy, M., Murray, R.M., Sangiovanni-Vincentelli, A., Seshia, S.A.: Model predictive control with signal temporal logic specifications. In: 53rd IEEE Conference on Decision and Control, pp. 81–87 (2014). DOI 10.1109/CDC.2014.7039363. URLhttps: //ieeexplore.ieee.org/document/7039363

    work page doi:10.1109/cdc.2014.7039363 2014

  49. [52]

    International Journal of Social Robotics7, 137–153 (2015)

    Rios-Martinez, J., Spalanzani, A., Laugier, C.: From proxemics theory to socially-aware nav- igation: A survey. International Journal of Social Robotics7, 137–153 (2015). DOI 10.1007/s12369-014-0251-1. URLhttps://link.springer.com/article/10.1007/ s12369-014-0251-1

    work page doi:10.1007/s12369-014-0251-1 2015

  50. [53]

    Robotics and Autonomous Systems151, 1–22 (2022)

    Rubagotti, M., Tusseyeva, I., Baltabayeva, S., Summers, D., Sandygulova, A.: Perceived safety in physical human–robot interaction — A survey. Robotics and Autonomous Systems151, 1–22 (2022). DOI 10.1016/j.robot.2022.104047. URLhttps://www.sciencedirect.com/science/ article/pii/S0921889022000173

    work page doi:10.1016/j.robot.2022.104047 2022

  51. [54]

    A survey on tensor techniques and applications in machine learning

    Shakhatreh, H., Sawalmeh, A.H., Al-Fuqaha, A., Dou, Z., Almaita, E., Khalil, I., Othman, N.S., Khreishah, A., Guizani, M.: Unmanned Aerial Vehicles (UA Vs): A Survey on Civil Applications and Key Research Challenges. IEEE Access7, 48572–48634 (2019). DOI 10.1109/ACCESS.2019. 2909530. URLhttps://ieeexplore.ieee.org/document/8682048

    work page doi:10.1109/access.2019 2019

  52. [55]

    In: 2023 International Conference on Unmanned Aircraft Systems, pp

    Silano, G., Afifi, A., Saska, M., Franchi, A.: A Signal Temporal Logic Planner for Ergonomic Hu- man–Robot Collaboration. In: 2023 International Conference on Unmanned Aircraft Systems, pp. 328–335 (2023). DOI 10.1109/ICUAS57906.2023.10156559. URLhttps://ieeexplore. ieee.org/document/10156559

    work page doi:10.1109/icuas57906.2023.10156559 2023

  53. [56]

    IEEE Robotics and Automation Let- ters6(2), 4169–4176 (2021)

    Silano, G., Baca, T., Penicka, R., Liuzza, D., Saska, M.: Power Line Inspection Tasks With Multi- Aerial Robot Systems Via Signal Temporal Logic Specifications. IEEE Robotics and Automation Let- ters6(2), 4169–4176 (2021). DOI 10.1109/LRA.2021.3068114. URLhttps://ieeexplore. ieee.org/document/9384182

    work page doi:10.1109/lra.2021.3068114 2021

  54. [57]

    NeuroImage124, 1155–1167 (2016) https://doi.org/10.1016/j

    Silano, G., Cabellero, A., Liuzza, D., Iannelli, L., Bogdan, S., Saska, M.: A Signal Temporal Logic Approach for Task-Based Coordination of Multi-Aerial Systems: a Wind Turbine Inspec- tion Case Study. Robotics and Autonomous Systems186(4), 1–16 (2025). DOI 10.1016/j. robot.2024.104905. URLhttps://www.sciencedirect.com/science/article/pii/ S0921889024002896

    work page doi:10.1016/j 2025

  55. [58]

    In: 2019 IEEE International Conference on Systems, Man and Cy- bernetics, pp

    Silano, G., Oppido, P., Iannelli, L.: Software-in-the-loop simulation for improving flight control sys- tem design: a quadrotor case study. In: 2019 IEEE International Conference on Systems, Man and Cy- bernetics, pp. 466–471 (2019). DOI 10.1109/SMC.2019.8914154. URLhttps://ieeexplore. ieee.org/document/8914154

    work page doi:10.1109/smc.2019.8914154 2019

  56. [59]

    IEEE Transactions on Robotics 28(5), 1045–1057 (2012)

    Sisbot, E.A., Alami, R.: A Human-Aware Manipulation Planner. IEEE Transactions on Robotics 28(5), 1045–1057 (2012). DOI 10.1109/TRO.2012.2196303. URLhttps://ieeexplore. ieee.org/document/6197743

    work page doi:10.1109/tro.2012.2196303 2012

  57. [60]

    In: 2007 IEEE International Conference on Intelligent Robots and Systems, pp

    Sisbot, E.A., Marin, L.F., Alami, R.: Spatial reasoning for human robot interaction. In: 2007 IEEE International Conference on Intelligent Robots and Systems, pp. 2281–2287 (2007). DOI 10.1109/ IROS.2007.4399486. URLhttps://ieeexplore.ieee.org/document/4399486

    work page arXiv 2007

  58. [61]

    IEEE Transactions on Robotics23(5), 874–883 (2007)

    Sisbot, E.A., Marin-Urias, L.F., Alami, R., Simeon, T.: A Human Aware Mobile Robot Motion Plan- ner. IEEE Transactions on Robotics23(5), 874–883 (2007). DOI 10.1109/TRO.2007.904911. URL https://ieeexplore.ieee.org/document/4339546

    work page doi:10.1109/tro.2007.904911 2007

  59. [62]

    IEEE Robotics & Automation Magazine25(4), 66–75 (2018)

    Staub, N., Mohammadi, M., Bicego, D., Delamare, Q., Yang, H., Prattichizzo, D., Robuffo Gior- dano, P., Lee, D., Franchi, A.: The Tele-MAGMaS: An Aerial-Ground Comanipulator System. IEEE Robotics & Automation Magazine25(4), 66–75 (2018). DOI 10.1109/MRA.2018.2871344. URL https://ieeexplore.ieee.org/abstract/document/8511046

    work page doi:10.1109/mra.2018.2871344 2018

  60. [63]

    IEEE Transactions on Robotics37(3), 723–734 (2021)

    Tognon, M., Alami, R., Siciliano, B.: Physical Human-Robot Interaction With a Tethered Aerial Ve- hicle: Application to a Force-Based Human Guiding Problem. IEEE Transactions on Robotics37(3), 723–734 (2021). DOI 10.1109/TRO.2020.3038700. URLhttps://ieeexplore.ieee.org/ document/9353246

    work page doi:10.1109/tro.2020.3038700 2021

  61. [64]

    IEEE Robotics and Automation Letters4(2), 1846– 1851 (2019)

    Tognon, M., Ch ´avez, H.A.T., Gasparin, E., Sabl ´e, Q., Bicego, D., Mallet, A., Lany, M., Santi, G., Revaz, B., Cort´es, J., Franchi, A.: A Truly-Redundant Aerial Manipulator System With Application to Push-and-Slide Inspection in Industrial Plants. IEEE Robotics and Automation Letters4(2), 1846– 1851 (2019). DOI 10.1109/LRA.2019.2895880. URLhttps://ieee...

    work page doi:10.1109/lra.2019.2895880 2019

  62. [65]

    OPV2V: An open benchmark dataset and fusion pipeline for perception with vehicle-to-vehicle communication,

    Truc, J., Singamaneni, P.T., Sidobre, D., Ivaldi, S., Alami, R.: KHAOS: a Kinematic Human Aware Optimization-based System for Reactive Planning of Flying-Coworker. In: 2022 International Confer- ence on Robotics and Automation, pp. 4764–4770 (2022). DOI 10.1109/ICRA46639.2022.9811803. URLhttps://ieeexplore.ieee.org/document/9811803

    work page doi:10.1109/icra46639.2022.9811803 2022

  63. [66]

    The International Jour- nal of Robotics Research39(1), 73–99 (2020)

    Webster, M., Western, D., Araiza-Illan, D., Dixon, C., Eder, K., Fisher, M., Pipe, A.G.: A cor- roborative approach to verification and validation of human–robot teams. The International Jour- nal of Robotics Research39(1), 73–99 (2020). DOI 10.1177/027836491988333. URLhttps: //journals.sagepub.com/doi/full/10.1177/0278364919883338

    work page doi:10.1177/027836491988333 2020

  64. [67]

    In: 2019 14th IEEE International Conference on Human-Robot Interaction, pp

    Wojciechowska, A., Frey, J., Sass, S., Shafir, R., Cauchard, J.R.: Collocated Human-Drone Inter- action: Methodology and Approach Strategy. In: 2019 14th IEEE International Conference on Human-Robot Interaction, pp. 172–181 (2019). DOI 10.1109/HRI.2019.8673127. URLhttps: //ieeexplore.ieee.org/document/8673127

    work page doi:10.1109/hri.2019.8673127 2019