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arxiv: 2604.24518 · v1 · submitted 2026-04-27 · 📡 eess.SY · cs.RO· cs.SY· math.OC

Sliding Mode Control for Safe Trajectory Tracking with Moving Obstacles Avoidance: Experimental Validation on Planar Robots

Shubham Sawarkar , P Sangeerth , S Saharsh , Pushpak Jagtap This is my paper

Pith reviewed 2026-05-08 01:46 UTC · model grok-4.3

classification 📡 eess.SY cs.ROcs.SYmath.OC
keywords sliding mode controlcontrol barrier functionstrajectory trackingobstacle avoidancemobile robotsAckermann drivecollision coneasymptotic tracking
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The pith

Sliding mode control with collision cone barriers enables safe asymptotic trajectory tracking for Ackermann and other mobile robots.

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

This paper introduces a control architecture that achieves both precise path following and collision avoidance for a variety of mobile robots. It uses a generalized kinematic transformation to standardize the dynamics of different vehicles into strict feedback form. A sliding mode controller is then applied for robust tracking even when disturbances are present. This is combined with a safety filter using collision cone control barrier functions to prevent hits with moving obstacles. Real experiments on Ackermann-steered vehicles, differential drive robots, and quadrotors demonstrate the method's effectiveness.

Core claim

The authors show that by converting diverse vehicle dynamics via a generalized kinematic transformation into strict feedback form, a sliding mode controller can be designed for asymptotic reference tracking under disturbances, and when augmented with a Collision Cone Control Barrier Function (C3BF) based safety filter, it strictly enforces collision avoidance while maintaining the tracking performance on ground and aerial robots.

What carries the argument

Generalized kinematic transformation to strict feedback form combined with Sliding Mode Control (SMC) and Collision Cone Control Barrier Function (C3BF) safety filter.

If this is right

  • Asymptotic tracking holds in the presence of external disturbances.
  • Collision avoidance constraints are strictly enforced for moving obstacles.
  • The framework applies to Ackermann-steered vehicles for the first time with SMC.
  • Validation covers numerical simulations and real-world tests on three distinct robot platforms.
  • The approach maintains safety and tracking in dynamic environments.

Where Pith is reading between the lines

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

  • The method may generalize to additional robot classes if their dynamics admit the kinematic transformation.
  • Integrating the safety filter could enhance other tracking controllers beyond sliding mode.
  • Real-time performance in experiments suggests applicability to autonomous vehicle navigation tasks.
  • Disturbance rejection properties could be tested under more severe conditions like wind or uneven terrain.

Load-bearing premise

That a generalized kinematic transformation can convert the dynamics of a broad class of mobile robots into strict feedback form.

What would settle it

Demonstrating a robot platform where the kinematic transformation does not yield strict feedback form, or an experiment where the robot either fails to track asymptotically or violates a collision avoidance constraint despite the filter.

Figures

Figures reproduced from arXiv: 2604.24518 by P Sangeerth, Pushpak Jagtap, Shubham Sawarkar, S Saharsh.

Figure 2
Figure 2. Figure 2: Construction of collision cone for a circular obstacle view at source ↗
Figure 3
Figure 3. Figure 3: Robots considered in case study (a) Turtlebot (b) view at source ↗
Figure 4
Figure 4. Figure 4: Ackermann drive robot performance. Reference(blue) view at source ↗
Figure 5
Figure 5. Figure 5: Ackermann drive: Time histories of sliding surface view at source ↗
Figure 8
Figure 8. Figure 8: Drone performance in altitude hold. Curves: Ref view at source ↗
Figure 7
Figure 7. Figure 7: Differential drive: Time histories of sliding surface view at source ↗
read the original abstract

This paper presents a unified control framework for robust trajectory tracking and moving obstacle avoidance applicable to a broad class of mobile robots. By formulating a generalized kinematic transformation, we convert diverse vehicle dynamics into a strict feedback form, facilitating the design of a Sliding Mode Control (SMC) strategy for precise and robust reference tracking. To ensure operational safety in dynamic environments, the tracking controller is integrated with a Collision Cone Control Barrier Function (C3BF) based safety filter. The proposed architecture guarantees asymptotic tracking in the presence of external disturbances while strictly enforcing collision avoidance constraints. The novelty of this work lies in designing a sliding mode controller for ground robots like the Ackermann drive, which has not been done before. The efficacy and versatility of the approach are validated through numerical simulations and extensive real-world experiments on three distinct platforms: an Ackermann-steered vehicle, a differential drive robot, and a quadrotor drone. Video of the experiments are available at https://youtu.be/dWcxwum96vk

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 unified control framework for robust trajectory tracking and moving obstacle avoidance applicable to a broad class of mobile robots. It uses a generalized kinematic transformation to convert diverse vehicle dynamics into strict-feedback form, designs a Sliding Mode Control (SMC) strategy for asymptotic tracking under external disturbances, and integrates this with a Collision Cone Control Barrier Function (C3BF) safety filter (implemented as a QP) to enforce collision avoidance with moving obstacles. The architecture is claimed to simultaneously guarantee asymptotic tracking and strict safety, with validation via simulations and experiments on an Ackermann-steered vehicle, differential-drive robot, and quadrotor drone.

Significance. The multi-platform experimental validation is a clear strength, providing evidence of practical versatility across ground and aerial robots. If the central claims hold, the work would advance safe, robust control for heterogeneous mobile systems in dynamic environments. However, the absence of analysis on how the safety filter interacts with the SMC reaching condition limits the strength of the theoretical contribution.

major comments (2)
  1. [Abstract] Abstract: The claim that the architecture 'guarantees asymptotic tracking in the presence of external disturbances while strictly enforcing collision avoidance constraints' is not supported by any analysis of the filtered closed-loop dynamics. The C3BF safety filter modifies the nominal SMC input via a QP, which can prevent the system from reaching or remaining on the sliding surface and thereby invalidate the standard SMC robustness argument (equivalent control plus reaching law) under bounded disturbances.
  2. [Control Architecture] Control design sections: No theorem or derivation is provided showing that the combined SMC + C3BF system preserves both the asymptotic tracking property and the strict safety constraint when the filter is active. The generalized kinematic transformation is asserted to enable SMC design for the Ackermann and quadrotor cases, but the conditions under which the transformation yields a controllable strict-feedback form are not stated or verified.
minor comments (2)
  1. [Experiments] The experimental results section would benefit from tabulated quantitative metrics (e.g., RMS tracking error, minimum obstacle distance, and activation frequency of the safety filter) rather than relying primarily on the linked video.
  2. Notation for the transformed states and the C3BF parameters could be introduced more clearly with a table summarizing symbols and their physical meanings.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive report. We agree that the theoretical analysis of the interaction between the SMC and the C3BF safety filter is insufficient in the current manuscript and that the claims in the abstract and control sections require stronger support. We will revise the paper to address these points by adding the necessary derivations and theorems while preserving the experimental contributions.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The claim that the architecture 'guarantees asymptotic tracking in the presence of external disturbances while strictly enforcing collision avoidance constraints' is not supported by any analysis of the filtered closed-loop dynamics. The C3BF safety filter modifies the nominal SMC input via a QP, which can prevent the system from reaching or remaining on the sliding surface and thereby invalidate the standard SMC robustness argument (equivalent control plus reaching law) under bounded disturbances.

    Authors: We acknowledge that the abstract claim is not fully supported by analysis of the filtered dynamics. The C3BF filter is designed to be inactive when the system is safe, reducing to the nominal SMC, but when active the QP solution can alter the input and affect the reaching phase. In the revised manuscript we will add a dedicated subsection analyzing the closed-loop behavior under the QP-modified input. We will derive sufficient conditions (e.g., minimum distance margins or bounded disturbance assumptions) under which the reaching law is preserved or show that the tracking error remains ultimately bounded while safety is strictly enforced. The abstract claim will be qualified accordingly. revision: yes

  2. Referee: [Control Architecture] Control design sections: No theorem or derivation is provided showing that the combined SMC + C3BF system preserves both the asymptotic tracking property and the strict safety constraint when the filter is active. The generalized kinematic transformation is asserted to enable SMC design for the Ackermann and quadrotor cases, but the conditions under which the transformation yields a controllable strict-feedback form are not stated or verified.

    Authors: We agree that explicit theorems are missing. We will introduce a new theorem stating that (i) the C3BF-QP filter guarantees forward invariance of the safe set by construction, and (ii) when the safety constraint is inactive the closed-loop system recovers the standard SMC asymptotic tracking result under bounded disturbances. When the filter is active we will provide a boundedness result for the tracking error. For the generalized kinematic transformation we will add a lemma that states the required conditions (invertibility of the decoupling matrix, relative degree, and controllability of the resulting strict-feedback form) and verify them explicitly for the Ackermann, differential-drive, and quadrotor models with the chosen outputs. revision: yes

Circularity Check

0 steps flagged

No significant circularity in derivation chain

full rationale

The paper's core steps consist of formulating a generalized kinematic transformation to place diverse robot dynamics into strict-feedback form, designing an SMC law on that form for asymptotic tracking under disturbances, and integrating a C3BF safety filter to enforce collision avoidance. These rely on standard control-theoretic constructions (kinematic transformations for nonholonomic systems, sliding-mode reaching laws, and control-barrier-function QP filters) whose properties are invoked from the broader literature rather than being defined in terms of the paper's own outputs or fitted parameters. No equations or claims reduce by construction to the inputs (e.g., no fitted parameter renamed as a prediction, no self-citation chain that alone justifies the simultaneous guarantee, and no ansatz smuggled via self-reference). The architecture is therefore self-contained against external benchmarks of SMC and CBF theory.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

Based solely on the abstract, the framework rests on standard control theory assumptions for kinematic transformations and barrier functions; no specific free parameters or new entities with independent evidence are detailed.

axioms (1)
  • domain assumption Diverse vehicle dynamics can be converted into strict feedback form via a generalized kinematic transformation
    This enables the SMC design and is invoked as the starting point for the unified controller.
invented entities (1)
  • Collision Cone Control Barrier Function (C3BF) no independent evidence
    purpose: Safety filter to enforce collision avoidance constraints
    Applied as the mechanism to strictly enforce safety while the SMC handles tracking.

pith-pipeline@v0.9.0 · 5497 in / 1154 out tokens · 61633 ms · 2026-05-08T01:46:12.456014+00:00 · methodology

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Reference graph

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