Whole-body motion planning and safety-critical control for aerial manipulation

Domenico Campolo; H. Jin Kim; Jeonghyun Byun; Jinwoo Lee; Lin Yang

arxiv: 2511.02342 · v3 · pith:V6VNDSSTnew · submitted 2025-11-04 · 💻 cs.RO

Whole-body motion planning and safety-critical control for aerial manipulation

Lin Yang , Jinwoo Lee , Domenico Campolo , H. Jin Kim , Jeonghyun Byun This is my paper

Pith reviewed 2026-05-21 20:04 UTC · model grok-4.3

classification 💻 cs.RO

keywords aerial manipulationmotion planningsuperquadricscontrol barrier functionswhole-body planningsafety-critical controlcluttered environments

0 comments

The pith

Superquadric models of the full aerial manipulator and obstacles produce faster, safer, and smoother trajectories than sampling-based or ellipsoid methods.

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

The paper introduces a motion planning and safety-critical control method for aerial manipulators that models both the robot and nearby obstacles as superquadrics. This representation stays differentiable and captures the actual geometry more closely than bounding boxes or ellipsoids. A maximum-clearance planner combines Voronoi diagrams with an equilibrium-manifold approach to create collision-aware paths, while a controller uses high-order control barrier functions to enforce thrust limits and avoidance at the same time. If the method holds, aerial robots with arms could operate reliably in cluttered spaces where current geometric simplifications force overly cautious or unsafe moves. Simulation results show gains in speed, safety, and smoothness, and hardware tests on a physical platform confirm the trajectories work in practice.

Core claim

An SQ-plus-proxy representation models the aerial manipulator and obstacles with differentiable, geometry-accurate surfaces. This supports a maximum-clearance planner that fuses Voronoi diagrams with an equilibrium-manifold formulation to generate smooth, collision-aware trajectories, together with a safety-critical controller that jointly enforces thrust limits and collision avoidance through high-order control barrier functions. The resulting trajectories are faster, safer, and smoother than those from sampling-based planners and exceed ellipsoid baselines in geometric fidelity, with feasibility and robustness demonstrated on a physical aerial-manipulation platform.

What carries the argument

The superquadric-plus-proxy representation, which supplies differentiable surfaces for both the vehicle and obstacles to support accurate whole-body collision avoidance in planning and control.

If this is right

Trajectories generated are faster, safer, and smoother than sampling-based planners in cluttered settings.
Geometric fidelity exceeds that of ellipsoid-based baselines while maintaining dynamic feasibility.
The same framework works consistently from simulation to hardware without major retuning.
Whole-body collision avoidance is achieved without relying on conservative bounding shapes.

Where Pith is reading between the lines

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

The differentiable surfaces could support online replanning when obstacles move or when new sensor data arrives.
Task-level performance such as payload delivery time in warehouses might improve because paths waste less clearance margin.
The approach could combine with learned dynamics models to handle uncertainty in arm joint friction or wind gusts.

Load-bearing premise

The superquadric-plus-proxy shapes stay differentiable and accurate enough for the vehicle, obstacles, real flight dynamics, and sensor noise without extra tuning that would erase the geometric fidelity gains.

What would settle it

Compare planned trajectories and collision rates in a cluttered indoor test environment with added sensor noise against an ellipsoid-based planner; if the superquadric method produces more collisions or requires manual tuning to stay safe, the geometric advantage does not hold.

Figures

Figures reproduced from arXiv: 2511.02342 by Domenico Campolo, H. Jin Kim, Jeonghyun Byun, Jinwoo Lee, Lin Yang.

**Figure 3.** Figure 3: Overall diagram of planning and control of aerial [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗

**Figure 4.** Figure 4: System representation using SQs and proxies. The [PITH_FULL_IMAGE:figures/full_fig_p004_4.png] view at source ↗

**Figure 5.** Figure 5: Simulation results from the whole-body motion [PITH_FULL_IMAGE:figures/full_fig_p006_5.png] view at source ↗

**Figure 6.** Figure 6: Comparison of end-effector trajectories by different [PITH_FULL_IMAGE:figures/full_fig_p006_6.png] view at source ↗

**Figure 7.** Figure 7: Flight experiment setup: (a) 3D side view in the [PITH_FULL_IMAGE:figures/full_fig_p007_7.png] view at source ↗

**Figure 8.** Figure 8: Histories of the multirotor’s measured and target displacements from initial position ([∆q1; ∆q2; ∆q3] ≜ [q1 −q1(t0); q2 −q2(t0); q3 −q3(t0)] and [∆qt,1; ∆qt,2; ∆qt,3] ≜ [qt,1 − qt,1(t0); qt,2 − qt,2(t0); qt,3 − qt,3(t0)]), multirotor’s measured and target Euler angles ([q4; q5; q6] and [qt,4; qt,5; qt,6]), robot arm’s measured and target joint angles (θ and θt), collision avoidance CBF, hco, and thrust va… view at source ↗

**Figure 9.** Figure 9: 2D top view trajectories of the multirotor and the [PITH_FULL_IMAGE:figures/full_fig_p007_9.png] view at source ↗

read the original abstract

Aerial manipulation combines the maneuverability of multirotors with the dexterity of robotic arms to perform complex tasks in cluttered spaces. Yet planning safe, dynamically feasible trajectories remains difficult due to whole-body collision avoidance and the conservativeness of common geometric abstractions such as bounding boxes or ellipsoids. We present a whole-body motion planning and safety-critical control framework for aerial manipulators built on superquadrics (SQs). Using an SQ-plus-proxy representation, we model both the vehicle and obstacles with differentiable, geometry-accurate surfaces. Leveraging this representation, we introduce a maximum-clearance planner that fuses Voronoi diagrams with an equilibrium-manifold formulation to generate smooth, collision-aware trajectories. We further design a safety-critical controller that jointly enforces thrust limits and collision avoidance via high-order control barrier functions. In simulation, our approach outperforms sampling-based planners in cluttered environments, producing faster, safer, and smoother trajectories and exceeding ellipsoid-based baselines in geometric fidelity. Actual experiments on a physical aerial-manipulation platform confirm feasibility and robustness, demonstrating consistent performance across simulation and hardware settings. The video can be found at https://youtu.be/hQYKwrWf1Ak.

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

The paper fuses superquadric modeling with Voronoi max-clearance planning and high-order CBFs for aerial manipulators, delivering a workable integrated framework but thin quantitative backing for the hardware claims.

read the letter

The core contribution is an integrated pipeline that models the full aerial manipulator and obstacles with superquadrics plus proxies for better geometric fidelity than ellipsoids, then generates trajectories via a maximum-clearance Voronoi planner on an equilibrium manifold and enforces safety with high-order control barrier functions that handle both thrust limits and collisions at once. This combination is not just a routine add-on; it ties the differentiable surface representation directly to both the planner and the controller in a way that targets cluttered 3-D flight tasks. Simulation results indicate faster, smoother, and safer paths than sampling-based methods and ellipsoid baselines, and the hardware tests on a physical platform show the system can run without obvious failures. That practical thread from model to controller is the part worth noting. The approach stays grounded in existing Voronoi and CBF tools rather than inventing new primitives, which keeps the claims traceable. The soft spots sit mainly in the validation. The abstract asserts outperformance and robustness across sim and hardware, yet the provided details lack specific metrics, error bars, or clear exclusion criteria for the trials. If the full manuscript supplies those numbers and shows how the superquadric fits hold under IMU noise and arm flexibility, the safety guarantees look more solid. The stress-test concern about C^2 differentiability or proxy error under real dynamics is reasonable to raise; nothing in the abstract rules it out, so the paper needs to demonstrate that the model stays accurate enough along closed-loop trajectories without heavy post-hoc tuning. Readers working on aerial robotics, whole-body planning, or safety-critical control in 3-D spaces would find the specific fusion useful. It is the kind of work that belongs in a reading group for the geometric-control link, even if the quantitative edge needs more data. The paper deserves peer review because the framework is coherent and the problem is relevant, though referees should press for the missing performance numbers and sensitivity checks on the representation.

Referee Report

2 major / 1 minor

Summary. The paper proposes a whole-body motion planning and safety-critical control framework for aerial manipulators that models both the vehicle and obstacles using a superquadric-plus-proxy representation. It combines a maximum-clearance planner based on Voronoi diagrams and an equilibrium-manifold formulation with a controller that enforces thrust limits and collision avoidance through high-order control barrier functions. The central claims are that this yields faster, safer, and smoother trajectories than sampling-based planners in cluttered simulation environments, exceeds ellipsoid baselines in geometric fidelity, and demonstrates feasible, robust performance on a physical aerial-manipulation platform with consistent sim-to-hardware transfer.

Significance. If the differentiability and accuracy of the superquadric representation hold under realistic conditions, the work offers a meaningful advance for aerial manipulation by reducing conservatism in geometric abstractions while preserving the mathematical structure required for high-order CBF safety guarantees. The fusion of Voronoi-based planning with equilibrium manifolds and the joint enforcement of actuator limits and avoidance constraints are technically coherent strengths. However, the absence of quantitative metrics, error bars, or statistical comparisons in the reported results limits assessment of the practical magnitude of the claimed improvements.

major comments (2)

[Abstract] Abstract: The performance claims (faster, safer, smoother trajectories; outperformance over sampling-based and ellipsoid baselines) are stated without any quantitative metrics, error bars, statistical tests, or exclusion criteria for the simulation trials. This makes the central empirical claim difficult to evaluate and is load-bearing for the assertion of consistent sim-to-hardware transfer.
[Modeling and controller sections] Modeling and controller sections (referenced via the SQ-plus-proxy representation and high-order CBF design): The safety guarantees rest on the signed-distance function and its first and second derivatives remaining well-defined and accurate along closed-loop trajectories. The manuscript provides no analysis or experiments demonstrating that the superquadric-plus-proxy surfaces retain C² differentiability and geometric fidelity under IMU/Vicon noise, flexible arm dynamics, or partial observability; if these properties degrade, the high-order CBF constraints no longer guarantee collision avoidance as claimed.

minor comments (1)

[Abstract] The abstract mentions a video link but does not specify which figures or supplementary material contain the quantitative trajectory comparisons or hardware logs that would support the outperformance statements.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the thoughtful review and constructive suggestions. We have revised the manuscript to address the concerns about quantitative support in the abstract and the robustness of the safety-critical controller. Below we provide detailed responses to each major comment.

read point-by-point responses

Referee: [Abstract] The performance claims (faster, safer, smoother trajectories; outperformance over sampling-based and ellipsoid baselines) are stated without any quantitative metrics, error bars, statistical tests, or exclusion criteria for the simulation trials. This makes the central empirical claim difficult to evaluate and is load-bearing for the assertion of consistent sim-to-hardware transfer.

Authors: We concur that the abstract would benefit from quantitative backing to strengthen the empirical claims. While the body of the manuscript contains detailed quantitative comparisons in the simulation results, including trajectory durations, safety margins, and smoothness measures with baseline methods, we have updated the abstract to highlight key metrics such as average planning time reductions and clearance improvements. Additionally, we have incorporated error bars and statistical information in the experimental section to better support the sim-to-hardware transfer claims. revision: yes
Referee: [Modeling and controller sections] The safety guarantees rest on the signed-distance function and its first and second derivatives remaining well-defined and accurate along closed-loop trajectories. The manuscript provides no analysis or experiments demonstrating that the superquadric-plus-proxy surfaces retain C² differentiability and geometric fidelity under IMU/Vicon noise, flexible arm dynamics, or partial observability; if these properties degrade, the high-order CBF constraints no longer guarantee collision avoidance as claimed.

Authors: We appreciate this observation on the conditions for the safety guarantees. The superquadric representation ensures C² differentiability in the nominal case, and the proxy is introduced precisely to facilitate accurate and differentiable modeling. While we do not provide a dedicated noise sensitivity analysis in the current manuscript, the successful hardware experiments on a physical platform using real IMU and Vicon data provide evidence that the controller performs robustly under realistic sensing conditions. We have added a paragraph in the controller section clarifying the assumptions and the practical validation through hardware trials. A more extensive study on partial observability would require additional sensing modalities and is left for future work. revision: partial

Circularity Check

0 steps flagged

No circularity: standard planning and control methods applied to new geometric representation

full rationale

The paper models the aerial manipulator and obstacles via an SQ-plus-proxy representation, then applies maximum-clearance planning that fuses Voronoi diagrams with an equilibrium-manifold formulation and designs a safety-critical controller using high-order control barrier functions. These are established techniques from the literature applied to the new differentiable surfaces; no claimed prediction or result reduces by the paper's own equations to a fitted parameter, self-citation chain, or imported uniqueness theorem. Simulation and hardware comparisons to sampling-based planners and ellipsoid baselines remain independent external benchmarks. The derivation chain is therefore self-contained.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The framework rests on the assumption that superquadrics provide a differentiable, geometry-accurate model for both the vehicle and obstacles; a small number of tuning parameters for the planner and barrier functions are expected but not enumerated in the abstract.

free parameters (1)

planner and controller tuning parameters
Weights, margins, and gains in the maximum-clearance optimization and high-order CBFs are typically fitted or chosen by hand in such control papers.

axioms (1)

domain assumption Superquadric surfaces plus proxy representation remain differentiable and faithful to vehicle and obstacle geometry under flight dynamics
Invoked in the abstract when introducing the SQ-plus-proxy model as the foundation for both planning and control.

pith-pipeline@v0.9.0 · 5737 in / 1417 out tokens · 53878 ms · 2026-05-21T20:04:27.022648+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

3 extracted references · 3 canonical work pages

[1]

and Koditschek, D.E

Arslan, O. and Koditschek, D.E. (2019). Sensor-based reactive navigation in unknown convex sphere worlds. The International Journal of Robotics Research, 38(2- 3), 196–223. Back, J. and Shim, H. (2009). An inner-loop controller guaranteeing robust transient performance for uncertain mimo nonlinear systems.IEEE Transactions on Auto- matic Control, 54(7), 1...

work page 2019
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(2000).Segmenta- tion and recovery of superquadrics, volume

Jaklic, A., Leonardis, A., and Solina, F. (2000).Segmenta- tion and recovery of superquadrics, volume

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Kana, S., Tee, K.P., and Campolo, D

Springer Science & Business Media. Kana, S., Tee, K.P., and Campolo, D. (2021). Human– robot co-manipulation during surface tooling: A general framework based on impedance control, haptic render- ing and discrete geometry.Robotics and Computer- Integrated Manufacturing, 67, 102033. Karaman, S. and Frazzoli, E. (2011). Sampling-based algo- rithms for optim...

work page arXiv 2021

[1] [1]

and Koditschek, D.E

Arslan, O. and Koditschek, D.E. (2019). Sensor-based reactive navigation in unknown convex sphere worlds. The International Journal of Robotics Research, 38(2- 3), 196–223. Back, J. and Shim, H. (2009). An inner-loop controller guaranteeing robust transient performance for uncertain mimo nonlinear systems.IEEE Transactions on Auto- matic Control, 54(7), 1...

work page 2019

[2] [2]

(2000).Segmenta- tion and recovery of superquadrics, volume

Jaklic, A., Leonardis, A., and Solina, F. (2000).Segmenta- tion and recovery of superquadrics, volume

work page 2000

[3] [3]

Kana, S., Tee, K.P., and Campolo, D

Springer Science & Business Media. Kana, S., Tee, K.P., and Campolo, D. (2021). Human– robot co-manipulation during surface tooling: A general framework based on impedance control, haptic render- ing and discrete geometry.Robotics and Computer- Integrated Manufacturing, 67, 102033. Karaman, S. and Frazzoli, E. (2011). Sampling-based algo- rithms for optim...

work page arXiv 2021