arxiv: 2602.22459 · v1 · submitted 2026-02-25 · 💻 cs.RO · cs.SY· eess.SY

Recognition: no theorem link

Hierarchical Trajectory Planning of Floating-Base Multi-Link Robot for Maneuvering in Confined Environments

Yicheng Chen , Jinjie Li , Haokun Liu , Zicheng Luo , Kotaro Kaneko , Moju Zhao

Authors on Pith no claims yet

Pith reviewed 2026-05-15 19:01 UTC · model grok-4.3

classification 💻 cs.RO cs.SYeess.SY

keywords trajectory planningfloating-base robotsmulti-link robotsconfined environmentspoint cloudhierarchical planningaerial robotics

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The pith

A hierarchical planner lets floating-base multi-link robots generate continuous collision-free trajectories directly from raw point-cloud data.

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

The paper develops a trajectory planning method for robots whose body can change shape during flight, such as multi-link aerial vehicles suited to confined spaces. It aims to solve high-dimensional planning by splitting the task into global guidance for the root link treated as a rigid body and parallel local optimization of the joints. A reader would care because the approach produces paths that respect kinematic limits and dynamic feasibility without requiring pre-built obstacle maps. The system processes raw sensor point clouds to enable maneuvers that rigid robots cannot perform.

Core claim

The authors establish that generating global anchor states from the root link decomposes the high-dimensional problem into tractable segments; each segment is then optimized locally in parallel using differentiable objectives and constraints that enforce kinematic feasibility while maintaining dynamic feasibility by avoiding control singularities. This yields continuous, collision-free trajectories demonstrated on a real articulated aerial robot operating directly from point-cloud inputs.

What carries the argument

The decomposition into global anchor states for the root link that segments the space, paired with parallel configuration-aware local trajectory optimization that applies differentiable constraints to enforce limits and avoid singularities.

Load-bearing premise

Global anchor states generated from the root link can always decompose the space into segments where parallel local optimization succeeds while respecting kinematic limits and avoiding singularities.

What would settle it

A physical robot test in a confined space where the generated trajectory causes a collision or control loss despite running the full pipeline would show the claim does not hold.

Figures

Figures reproduced from arXiv: 2602.22459 by Haokun Liu, Jinjie Li, Kotaro Kaneko, Moju Zhao, Yicheng Chen, Zicheng Luo.

**Figure 2.** Figure 2: Kinematic model of the floating-base multi-link robot. The root link [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Figure 3: System overview. The hierarchical trajectory planning framework decomposes the planning problem into independently solvable segments by introducing [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗

**Figure 4.** Figure 4: Illustration of generating the candidate local target set [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗

**Figure 5.** Figure 5: Illustration of candidate target evaluation. Each candidate configuration [PITH_FULL_IMAGE:figures/full_fig_p006_5.png] view at source ↗

**Figure 6.** Figure 6: Overview of the perception, planning, and control pipeline. Coordinate [PITH_FULL_IMAGE:figures/full_fig_p010_6.png] view at source ↗

**Figure 7.** Figure 7: Simulation of a floating-base multi-link robot navigating through a confined environment with narrow passages. (a) Illustration of the global anchor [PITH_FULL_IMAGE:figures/full_fig_p011_7.png] view at source ↗

**Figure 8.** Figure 8: Computation time comparison between our planner and its sequential [PITH_FULL_IMAGE:figures/full_fig_p011_8.png] view at source ↗

**Figure 9.** Figure 9: Comparison of planned motions from different algorithms in one trial. (a) Mission setup with start and goal states. (b) The proposed planner generated [PITH_FULL_IMAGE:figures/full_fig_p012_9.png] view at source ↗

**Figure 10.** Figure 10: Influence of hyperparameters on planning performance. The plots [PITH_FULL_IMAGE:figures/full_fig_p013_10.png] view at source ↗

**Figure 11.** Figure 11: Evaluation with real-world point clouds under environment changes. A handheld Livox Mid-360 LiDAR was used to capture the scene, and FAST-LIO [PITH_FULL_IMAGE:figures/full_fig_p014_11.png] view at source ↗

**Figure 12.** Figure 12: The robot used in the experiments. In its square configuration, [PITH_FULL_IMAGE:figures/full_fig_p014_12.png] view at source ↗

**Figure 13.** Figure 13: Experimental demonstration of the floating-base multi-link robot performing complex maneuvers in confined environments. The first four rows show [PITH_FULL_IMAGE:figures/full_fig_p015_13.png] view at source ↗

read the original abstract

Floating-base multi-link robots can change their shape during flight, making them well-suited for applications in confined environments such as autonomous inspection and search and rescue. However, trajectory planning for such systems remains an open challenge because the problem lies in a high-dimensional, constraint-rich space where collision avoidance must be addressed together with kinematic limits and dynamic feasibility. This work introduces a hierarchical trajectory planning framework that integrates global guidance with configuration-aware local optimization. First, we exploit the dual nature of these robots - the root link as a rigid body for guidance and the articulated joints for flexibility - to generate global anchor states that decompose the planning problem into tractable segments. Second, we design a local trajectory planner that optimizes each segment in parallel with differentiable objectives and constraints, systematically enforcing kinematic feasibility and maintaining dynamic feasibility by avoiding control singularities. Third, we implement a complete system that directly processes point-cloud data, eliminating the need for handcrafted obstacle models. Extensive simulations and real-world experiments confirm that this framework enables an articulated aerial robot to exploit its morphology for maneuvering that rigid robots cannot achieve. To the best of our knowledge, this is the first planning framework for floating-base multi-link robots that has been demonstrated on a real robot to generate continuous, collision-free, and dynamically feasible trajectories directly from raw point-cloud inputs, without relying on handcrafted obstacle models.

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 gets a working hierarchical planner on real hardware for shape-changing aerial robots from raw point clouds, but the global anchor decomposition looks vulnerable to noise and tight constraints.

read the letter

The main thing to know is that this paper shows a hierarchical planner that runs on a real floating-base multi-link robot, taking raw point clouds and producing continuous trajectories that let the robot reshape itself to get through confined spaces. That real-hardware demonstration from direct sensor data is the concrete step forward over prior simulation or rigid-body work. They split the problem by generating global anchor states from the root link to break the high-dimensional space into segments, then optimize each segment in parallel with differentiable constraints that enforce kinematics and avoid control singularities. The system skips handcrafted obstacle models, and the abstract plus experiments claim success in both simulation and on the physical robot. This approach fits the dual rigid-articulated nature of these systems and gives a usable way to exploit morphology for inspection or rescue tasks. The soft spot is the assumption that the global anchors will reliably create segments where the local planner always finds feasible, singularity-free solutions. Raw point-cloud noise or incomplete geometry can shift collision boundaries, and nothing in the description shows how the method recovers when an anchor lands in a region with no good local solution under full dynamic constraints. That decomposition step carries the load, and it may not be as robust as presented in tight environments. This is for robotics researchers building planners for high-DoF aerial systems in cluttered spaces. A reader who needs practical methods with hardware validation would get value from the framework and the point-cloud handling. It deserves a serious referee because the real-robot results and direct sensor integration are concrete enough to review in detail, especially around decomposition robustness and failure modes.

Referee Report

1 major / 2 minor

Summary. The paper introduces a hierarchical trajectory planning framework for floating-base multi-link robots in confined environments. It exploits the robot's dual nature (rigid root link for global guidance, articulated joints for flexibility) to generate anchor states that decompose the high-dimensional problem into segments. Each segment is optimized in parallel via differentiable objectives and constraints to enforce kinematic limits, avoid control singularities for dynamic feasibility, and achieve collision avoidance directly from raw point-cloud inputs without handcrafted models. The approach is validated in simulations and real-robot experiments, with the claim that it is the first such framework demonstrated on hardware to produce continuous, collision-free, dynamically feasible trajectories.

Significance. If the results hold, the work would be a meaningful contribution to motion planning for high-DoF floating-base systems, enabling morphology-aware maneuvering in tight spaces relevant to inspection and search-and-rescue. The direct use of point clouds, parallel local optimization, and real-robot demonstration are strengths that distinguish it from prior methods relying on simplified obstacle representations. The hierarchical decomposition is a pragmatic way to manage dimensionality, though its reliability under sensor noise remains central to the overall impact.

major comments (1)

[Abstract / Global Anchor States] Abstract and global-anchor decomposition section: the central claim that root-link-generated anchor states reliably decompose the problem into segments where parallel local optimization succeeds while enforcing kinematic limits and avoiding singularities is load-bearing, yet the manuscript provides no formal guarantee or robustness analysis showing that reachable configuration manifolds intersect the feasible set under raw point-cloud noise and incomplete geometry; this assumption is presented as always workable but could fail in tight constraints, requiring either a proof sketch or targeted counterexample experiments.

minor comments (2)

[Abstract] The abstract states 'extensive simulations and real-world experiments' but omits quantitative metrics (e.g., success rates, computation times, or specific platform details), which would strengthen the empirical claims.
[Notation / Introduction] Notation for 'global anchor states' and 'differentiable objectives' should be introduced with explicit definitions or references to equations in the main text to improve readability for readers unfamiliar with the dual rigid-articulated formulation.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We appreciate the identification of the load-bearing assumption in the hierarchical decomposition and address it point by point below.

read point-by-point responses

Referee: [Abstract / Global Anchor States] Abstract and global-anchor decomposition section: the central claim that root-link-generated anchor states reliably decompose the problem into segments where parallel local optimization succeeds while enforcing kinematic limits and avoiding singularities is load-bearing, yet the manuscript provides no formal guarantee or robustness analysis showing that reachable configuration manifolds intersect the feasible set under raw point-cloud noise and incomplete geometry; this assumption is presented as always workable but could fail in tight constraints, requiring either a proof sketch or targeted counterexample experiments.

Authors: We agree that the manuscript lacks a formal guarantee or proof sketch for the intersection of reachable configuration manifolds with the feasible set under point-cloud noise. The current validation relies on empirical results from simulations that include noisy point clouds and real-robot experiments in confined spaces. To strengthen the paper, we will add a dedicated subsection in the discussion that analyzes the assumptions of the anchor state generation and includes targeted experiments with increased sensor noise levels and more challenging tight constraints, including near-failure cases to demonstrate the practical limits of the approach. A complete formal proof would require substantial additional theoretical work and is left for future research. revision: yes

Circularity Check

0 steps flagged

No significant circularity; derivation relies on standard hierarchical optimization

full rationale

The paper's core chain—generating global anchor states from the root link to decompose the problem, followed by parallel local differentiable optimization enforcing kinematic and dynamic constraints—does not reduce to self-definition, fitted inputs renamed as predictions, or load-bearing self-citations. The abstract and described framework apply established robotics techniques (rigid-body guidance plus articulated flexibility) to point-cloud inputs without equations that equate outputs to inputs by construction. No uniqueness theorems or ansatzes are smuggled via self-citation in the provided text. This is a normal non-circular engineering contribution.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

Based on abstract only; framework introduces anchor states and differentiable constraints as core elements with no explicit fitted numerical parameters listed.

axioms (1)

domain assumption Floating-base multi-link robots possess a dual nature allowing root link to serve as rigid body for guidance while joints provide flexibility.
Invoked to generate global anchor states that decompose the planning problem.

invented entities (1)

global anchor states no independent evidence
purpose: Decompose high-dimensional planning into tractable segments
New construct introduced to enable hierarchical separation of global guidance and local optimization.

pith-pipeline@v0.9.0 · 5563 in / 1173 out tokens · 30409 ms · 2026-05-15T19:01:31.961456+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

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