Multi-robot obstacle-aware shepherding of non-cohesive target agents

Andreagiovanni Reina; Cinzia Tomaselli; Mario di Bernardo; Stefano Covone

arxiv: 2604.22327 · v1 · submitted 2026-04-24 · 📡 eess.SY · cs.SY

Multi-robot obstacle-aware shepherding of non-cohesive target agents

Cinzia Tomaselli , Stefano Covone , Andreagiovanni Reina , Mario di Bernardo This is my paper

Pith reviewed 2026-05-08 10:36 UTC · model grok-4.3

classification 📡 eess.SY cs.SY

keywords multi-robot shepherdingobstacle avoidancenon-cohesive targetshybrid control policytarget confinementrobot navigationrepulsive forces

0 comments

The pith

A hybrid control policy for robot herders guides non-cohesive targets around obstacles by combining goal steering with tangent maneuvers.

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

The paper develops a control method for multiple robots to direct independent target agents toward a goal region while navigating obstacles. Targets only repel from nearby herders and do not coordinate with one another, unlike the cohesive groups assumed in earlier work. Each herder switches between returning to the goal when idle, steering targets with adaptive direction, and avoiding obstacles using both direct and sliding force components. Simulations indicate better rates of keeping targets confined in cluttered areas than prior techniques. Physical tests with small wheeled robots in an indoor space support that the approach functions under real conditions.

Core claim

By integrating return-to-goal motion for idle herders, adaptive directional steering toward targets, and obstacle avoidance that applies both normal and tangential forces, the herders can direct non-cohesive targets to circumnavigate barriers and reach a designated goal region, yielding higher confinement rates in simulations and successful guidance in robot arena experiments.

What carries the argument

The hybrid control policy that merges direct goal-oriented steering with obstacle-tangent maneuvering while responding to targets that exert only local repulsive forces.

If this is right

Targets reach the goal region while moving around obstacles without requiring any group cohesion among themselves.
Confinement rates exceed those of prior shepherding approaches in environments containing multiple obstacles.
The same policy works when implemented on physical robots moving in a real indoor space with barriers.
Idle herders automatically return to the goal area, freeing them for new steering tasks once targets are guided.

Where Pith is reading between the lines

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

The same force-based interaction model could be adapted to guide other loosely connected agents such as particles in fluid flows or autonomous vehicles in traffic.
Adding limited communication between herders might further raise success rates in very large or highly dynamic obstacle fields.
The method implies that realistic shepherding tasks need not assume targets form tight groups, which broadens the range of applicable scenarios.

Load-bearing premise

Targets respond only to repulsive forces from nearby herders and display no coordination or additional behaviors among themselves.

What would settle it

An experiment or simulation in which the proposed herder policy produces target confinement rates no higher than those of existing shepherding methods when obstacles are present would show the performance gain does not hold.

Figures

Figures reproduced from arXiv: 2604.22327 by Andreagiovanni Reina, Cinzia Tomaselli, Mario di Bernardo, Stefano Covone.

**Figure 1.** Figure 1: Schematic representation of the forces acting on view at source ↗

**Figure 2.** Figure 2: Example of obstacle-avoidance strategy in a single view at source ↗

**Figure 3.** Figure 3: Validation of the proposed shepherding strategy in view at source ↗

**Figure 4.** Figure 4: Given the additional orbiting term, d(∂Oi ,∂Oj) ∀i, j ∈ O,i ̸= j, has to be increased by dth to enable target circulation. Therefore, for experimental implementation, the control law in Eq. (6) is modified to incorporate the physical robot constraints and orbiting behavior described above: u˜i = (1−ηi)Fi(Hi) + (1−σi)ηiI ′ i (T,H,O) +F H i,O + ∑ j∈H G H i j +σi ζi pi (17) where the function I ′ i is also b… view at source ↗

**Figure 4.** Figure 4: Orbiting direction selection for herder Hi pursuing its assigned target T ∗ i . (a) Obstacle-free case: the tangential orbiting direction is determined by the relative orientation between dHiT ∗ i and dCiT ∗ i , yielding φi = ± π 2 according to Eq. (13) and enabling circulation toward the desired offset position Ci . (b) Target in proximity of obstacle Oj : the nominal orbiting direction may steer the her… view at source ↗

**Figure 5.** Figure 5: Gazebo simulation of the obstacle-aware shepherding view at source ↗

**Figure 7.** Figure 7: Experimental validation of the obstacle-aware shep view at source ↗

read the original abstract

This paper presents a novel control strategy for multi-agent shepherding of non-cohesive targets in obstacle-rich environments. Unlike previous approaches that assume cohesive flocking behavior, our method handles targets that interact only with nearby herders through repulsive forces and exhibit no inter-target coordination. Each herder employs a hybrid control policy that combines direct goal-oriented steering with obstacle-tangent maneuvering, enabling targets to circumnavigate obstacles while being guided toward a goal region. The herder dynamics integrate three key behaviors: return-to-goal motion when idle, target steering with adaptive directional control, and obstacle avoidance using both normal and tangential force components. Numerical simulations demonstrate superior performance compared to existing shepherding methods, achieving higher target confinement rates in cluttered environments. Experimental validation using TurtleBot4 herders and Osoyoo target robots in an indoor arena confirms the practical effectiveness of the proposed approach.

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.

Desk Editor's Note private letter to a colleague

This paper gives a hybrid control policy for herding non-cohesive targets around obstacles with multi-robot teams, backed by simulation comparisons and TurtleBot experiments, but the gains rest on targets having no interactions among themselves.

read the letter

The main takeaway is that this paper presents a hybrid control policy for shepherding non-cohesive targets in obstacle-filled areas using multiple robots. It combines return-to-goal behavior, adaptive target steering, and normal plus tangential obstacle avoidance, and it demonstrates better target confinement in simulations plus successful runs on physical robots. The new part is handling targets that don't stick together and only respond to herders with repulsion, unlike most prior shepherding work that assumes cohesive groups. The simulations claim higher success rates against existing methods in cluttered spaces, and the experiments with TurtleBot4 herders and Osoyoo targets in an indoor arena show the approach works on actual hardware without major issues. The approach holds up under its own assumptions, where targets have zero coordination or forces between them. The stress-test concern applies directly: the policy doesn't account for any target-target dynamics, so if those appear, the guidance could fail without adjustments. The paper sticks to the stated model and doesn't overclaim generality, which keeps it honest, but it does mean the results are conditional on that setup holding. A softer spot is the lack of detailed robustness analysis or statistical comparisons in the summary, making it harder to gauge how sensitive the adaptive parameters are or how fair the baseline comparisons were. The full methods would need to clarify those for stronger conclusions. This work is aimed at engineers and researchers in multi-robot systems who deal with herding or containment tasks in messy environments. A reader interested in practical implementations with robot validation will get usable ideas from it. It deserves a serious referee because the combination of simulation superiority and experimental confirmation provides enough substance to review, even with the noted limitations on assumptions. I would send this to peer review.

Referee Report

1 major / 2 minor

Summary. This paper proposes a hybrid control strategy for multi-robot shepherding of non-cohesive targets in obstacle-rich environments. Targets are modeled as interacting solely via repulsive forces from nearby herders with no inter-target coordination. Each herder combines goal-oriented steering, obstacle-tangent maneuvering, and adaptive directional control. Numerical simulations claim superior target confinement rates versus prior methods in cluttered settings, with experimental validation using TurtleBot4 herders and Osoyoo targets in an indoor arena.

Significance. If the modeling assumptions hold, the approach fills a gap in handling non-cohesive agents without flocking assumptions, which is relevant for robotic herding tasks in cluttered spaces. Strengths include the explicit integration of obstacle-tangent forces for circumnavigation and the combination of simulation comparisons with physical robot experiments, providing some practical grounding.

major comments (1)

[Abstract and modeling section] Abstract and modeling section: The superior confinement claim and hybrid policy derivation rest on the assumption that targets exhibit zero inter-target coordination or forces (explicitly stated as 'interact only with nearby herders through repulsive forces and exhibit no inter-target coordination'). No sensitivity analysis, robustness tests, or alternative simulations with added target-target interactions are provided; violating this assumption would alter force balances and trajectories without any compensating mechanism in the policy.

minor comments (2)

[Abstract] The abstract refers to 'higher target confinement rates' but does not define the exact metric, threshold, or statistical comparison method used against baselines.
[Experimental validation] Experimental validation is mentioned but lacks details on trial count, statistical significance, error bars, or specific performance numbers in the provided description.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address the major comment point by point below, proposing targeted revisions to clarify the scope of our contributions while maintaining the focus on non-cohesive targets.

read point-by-point responses

Referee: [Abstract and modeling section] Abstract and modeling section: The superior confinement claim and hybrid policy derivation rest on the assumption that targets exhibit zero inter-target coordination or forces (explicitly stated as 'interact only with nearby herders through repulsive forces and exhibit no inter-target coordination'). No sensitivity analysis, robustness tests, or alternative simulations with added target-target interactions are provided; violating this assumption would alter force balances and trajectories without any compensating mechanism in the policy.

Authors: The assumption of zero inter-target coordination is not an unexamined premise but the explicit definition of the non-cohesive shepherding problem we address, as stated in the title, abstract, and modeling section. Our hybrid policy (goal-oriented steering combined with obstacle-tangent maneuvering) is derived specifically for targets that respond only to herder-induced repulsive forces, without any inter-target coordination or flocking. This modeling choice fills the gap noted in the referee summary by handling scenarios where prior cohesive methods do not apply. The reported superior confinement rates are therefore valid under the stated model and are supported by both simulations and physical experiments with TurtleBot4 herders and Osoyoo targets. We agree that no sensitivity analysis to added target-target interactions is present; such interactions would indeed change the dynamics and move the problem into the cohesive regime already covered by existing literature. In revision we will (1) strengthen the abstract and modeling section to foreground the problem scope and (2) add a concise limitations paragraph that qualitatively discusses how inter-target repulsion could alter trajectories and force balances, without claiming robustness outside the non-cohesive case. Full quantitative sensitivity simulations lie beyond the current scope and are identified as future work. revision: partial

Circularity Check

0 steps flagged

No circularity detected in control policy derivation or validation

full rationale

The paper proposes a hybrid control policy (goal steering + obstacle-tangent maneuvering + adaptive direction) for non-cohesive targets under explicit repulsive-force assumptions, then validates it via independent numerical simulations and physical robot experiments. No equations, parameters, or results are shown to reduce to fitted inputs by construction, no self-citation chains bear the central claim, and the performance metrics (confinement rates) are externally measured rather than tautological. The modeling assumptions are stated upfront and the results are conditional on them, but this is a standard modeling choice rather than circular reasoning.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The central claim rests primarily on a domain assumption about target behavior and unspecified parameters in the hybrid control laws, with no new physical entities introduced.

free parameters (1)

adaptive directional control parameters
Referenced as part of target steering behavior but no specific values or fitting process detailed in the abstract.

axioms (1)

domain assumption Targets interact only with nearby herders through repulsive forces and exhibit no inter-target coordination.
This is explicitly stated in the abstract as the basis for the non-cohesive target model.

pith-pipeline@v0.9.0 · 5452 in / 1270 out tokens · 69883 ms · 2026-05-08T10:36:08.065486+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

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Siciliano, L

B. Siciliano, L. Sciavicco, L. Villani, and G. Oriolo,Robotics: Mod- elling, Planning and Control. Springer, 2010. APPENDIX This appendix summarizes the key numerical settings used in the study. a) Model parameters:Table A1 reports the physical constants defining herder and target dynamics (Section II). TABLE A1: Model parameters for herders, targets, and...

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A comprehensive review of shepherding as a bio-inspired swarm- robotics guidance approach,

N. K. Long, K. Sammut, D. Sgarioto, M. Garratt, and H. A. Abbass, “A comprehensive review of shepherding as a bio-inspired swarm- robotics guidance approach,”IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 4, no. 4, pp. 523–537, 2020

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D. Strömbom, R. Mann, A. Wilson, S. Hailes, A. Morton, D. Sumpter, and A. King, “Solving the shepherding problem: Heuristics for herd- ing autonomous, interacting agents,”Journal of The Royal Society Interface, vol. 11, 2014

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Biologically inspired confinement of multi-robot systems,

M. A. Haque, A. R. Rahmani, and M. B. Egerstedt, “Biologically inspired confinement of multi-robot systems,”International Journal of Bio-Inspired Computation, vol. 3, no. 4, pp. 213–224, 2011

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Wolf-pack (Canis lupus) hunting strategies emerge from simple rules in compu- tational simulations,

C. Muro, R. Escobedo, L. Spector, and R. Coppinger, “Wolf-pack (Canis lupus) hunting strategies emerge from simple rules in compu- tational simulations,”Behavioural Processes, vol. 88, no. 3, pp. 192– 197, 2011

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[5] [5]

Single agent indirect herding of multiple targets: A switched adaptive control approach,

R. A. Licitra, Z. I. Bell, E. A. Doucette, and W. E. Dixon, “Single agent indirect herding of multiple targets: A switched adaptive control approach,”IEEE Control Systems Letters, vol. 2, no. 1, pp. 127–132, 2018

work page 2018

[6] [6]

Communication-free shepherd- ing navigation with multiple steering agents,

A. Li, M. Ogura, and N. Wakamiya, “Communication-free shepherd- ing navigation with multiple steering agents,”Frontiers in Control Engineering, vol. 4, p. 989232, 2023

work page 2023

[7] [7]

Controlling Noncooperative Herds with Robotic Herders,

A. Pierson and M. Schwager, “Controlling Noncooperative Herds with Robotic Herders,”IEEE Transactions on Robotics, vol. 34, no. 2, pp. 517–525, 2018

work page 2018

[8] [8]

A distributed outmost push approach for multirobot herding,

S. Zhang, X. Lei, M. Duan, X. Peng, and J. Pan, “A distributed outmost push approach for multirobot herding,”IEEE Transactions on Robotics, vol. 40, pp. 1706–1723, 2024

work page 2024

[9] [9]

Shepherding behaviors,

J.-M. Lien, O. B. Bayazit, R. T. Sowell, S. Rodriguez, and N. M. Amato, “Shepherding behaviors,” inIEEE International Conference on Robotics and Automation, vol. 4, 2004, pp. 4159–4164

work page 2004

[10] [10]

Herding stochastic autonomous agents via local control rules and online target selection strategies,

F. Auletta, D. Fiore, M. J. Richardson, and M. di Bernardo, “Herding stochastic autonomous agents via local control rules and online target selection strategies,”Autonomous Robots, vol. 46, no. 3, pp. 469–481, 2022

work page 2022

[11] [11]

Shepherding and herdability in complex multiagent systems,

A. Lama and M. di Bernardo, “Shepherding and herdability in complex multiagent systems,”Physical Review Research, vol. 6, p. L032012, 2024

work page 2024

[12] [12]

Asymptotic behavior and control of a “guidance by repulsion

D. Ko and E. Zuazua, “Asymptotic behavior and control of a “guidance by repulsion” model,”Mathematical Models and Methods in Applied Sciences, vol. 30, no. 04, pp. 765–804, 2020

work page 2020

[13] [13]

Robot obstacle avoidance using vortex fields,

C. De Medio and G. Oriolo, “Robot obstacle avoidance using vortex fields,” inAdvances in Robot Kinematics. Springer, 1991, pp. 227– 235

work page 1991

[14] [14]

Geopf: Infusing geometry into potential fields for reactive planning in non-trivial environments,

Y . Gong, R. Laha, and L. Figueredo, “Geopf: Infusing geometry into potential fields for reactive planning in non-trivial environments,” in arXiv:2505.19688, 2025

work page arXiv 2025

[15] [15]

Reactive shepherding along a dynamic path,

S. Van Havermaet, Y . Khaluf, and P. Simoens, “Reactive shepherding along a dynamic path,”Scientific Reports, vol. 14, p. 14915, 2024

work page 2024

[16] [16]

Robotic shepherding in cluttered and unknown environments using control barrier functions,

M. Hamandi, F. Khorrami, and A. Tzes, “Robotic shepherding in cluttered and unknown environments using control barrier functions,” inarXiv:2407.15701, 2024

work page arXiv 2024

[17] [17]

Multiagent planning and control for swarm herding in 2-d obstacle environments under bounded inputs,

V . S. Chipade and D. Panagou, “Multiagent planning and control for swarm herding in 2-d obstacle environments under bounded inputs,” IEEE Transactions on Robotics, vol. 37, no. 6, pp. 1956–1972, 2021

work page 1956

[18] [18]

Learning to herd agents amongst obstacles: Training robust shepherding behaviors using deep reinforcement learn- ing,

J. Zhi and J.-M. Lien, “Learning to herd agents amongst obstacles: Training robust shepherding behaviors using deep reinforcement learn- ing,”IEEE Robotics and Automation Letters, vol. 6, no. 2, pp. 4163– 4168, 2021

work page 2021

[19] [19]

Real-time obstacle avoidance for manipulators and mobile robots,

O. Khatib, “Real-time obstacle avoidance for manipulators and mobile robots,”The International Journal of Robotics Research, vol. 5, no. 1, pp. 90–98, 1986

work page 1986

[20] [20]

Adaptive Multirobot Implicit Control of Heterogeneous Herds,

E. Sebastián, E. Montijano, and C. Sagüés, “Adaptive Multirobot Implicit Control of Heterogeneous Herds,”IEEE Transactions on Robotics, vol. 38, no. 6, pp. 3622–3635, 2022

work page 2022

[21] [21]

Invisible control of self-organizing agents leaving unknown environments,

G. Albi, M. Bongini, E. Cristiani, and D. Kalise, “Invisible control of self-organizing agents leaving unknown environments,”SIAM Journal on Applied Mathematics, vol. 76, no. 4, pp. 1683–1710, 2016

work page 2016

[22] [22]

Flocking for multi-agent dynamic systems: algo- rithms and theory,

R. Olfati-Saber, “Flocking for multi-agent dynamic systems: algo- rithms and theory,”IEEE Transactions on Automatic Control, vol. 51, no. 3, pp. 401–420, 2006

work page 2006

[23] [23]

Siciliano, L

B. Siciliano, L. Sciavicco, L. Villani, and G. Oriolo,Robotics: Mod- elling, Planning and Control. Springer, 2010. APPENDIX This appendix summarizes the key numerical settings used in the study. a) Model parameters:Table A1 reports the physical constants defining herder and target dynamics (Section II). TABLE A1: Model parameters for herders, targets, and...

work page 2010