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arxiv: 2605.25010 · v1 · pith:7DLMZTL3new · submitted 2026-05-24 · 💻 cs.RO · cs.AI

Performance Comparison of Classical and Neural Sampling Algorithms for Robotic Navigation

Hichem Cheriet , Badra Khellat Kihel , Samira Chouraqui This is my paper

Pith reviewed 2026-06-30 00:31 UTC · model grok-4.3

classification 💻 cs.RO cs.AI
keywords RRT*neural samplingmotion planningrobotic navigationpath lengthtrajectory smoothnessNeural Informed RRT*obstacle avoidance
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The pith

Neural-guided planners generate up to 14% shorter and 55-75% smoother paths than classical RRT*.

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

The study implements and tests RRT*, Neural RRT*, and Neural Informed RRT* in environments with convex and concave obstacles of varying densities. Results show that the neural-guided algorithms produce shorter paths and smoother trajectories than the conventional method. Neural Informed RRT* performs best on path length and smoothness. This indicates that AI-guided sampling can enhance the efficiency of motion planning for robots and UAVs, with only a minor increase in computation time.

Core claim

Neural-informed sampling in RRT* variants improves path quality over standard RRT*, with Neural Informed RRT* achieving the shortest paths and smoothest trajectories in the evaluated settings.

What carries the argument

Neural Informed RRT*, which incorporates a neural network to guide the sampling process in the RRT* algorithm.

If this is right

  • Neural guidance strategies enhance reliability and efficiency in robotic path planning.
  • These methods apply to UAV navigation with similar improvements in trajectory quality.
  • AI integration in sampling planners increases overall performance despite slight computation overhead.

Where Pith is reading between the lines

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

  • The benefits may extend to other sampling-based planners not tested here.
  • Further work could examine performance in dynamic or real-world settings with sensor noise.
  • Optimization of the neural components could reduce the computation time increase.

Load-bearing premise

The neural networks are trained on data representative of the test environments, and the improvements result from the neural sampling strategy.

What would settle it

Testing the algorithms in environments where the training data for the neural networks does not match the test conditions and checking if the reported gains in path quality disappear.

Figures

Figures reproduced from arXiv: 2605.25010 by Badra Khellat Kihel, Hichem Cheriet, Samira Chouraqui.

Figure 1
Figure 1. Figure 1: Comparison of RRT variants for path planning. Neural [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Illustration of the RRT* expansion process showing [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Comparison of RRT*, Neural RRT*, and Neural [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
read the original abstract

Integrating artificial intelligence (AI) into sampling-based motion planning provides new possibilities for improving autonomous navigation efficiency. In this paper, three algorithms, namely RRT*, Neural RRT*, and Neural Informed RRT*, are implemented and evaluated on environments containing convex and concave obstacles with different obstacle densities. The obtained results indicate that neural-guided planners improve path quality, producing up to 14\% shorter paths and 55--75\% smoother trajectories compared with the conventional RRT* algorithm. Among the evaluated methods, Neural Informed RRT* achieves the best overall performance in terms of path length and trajectory smoothness. These results demonstrate the effectiveness of AI-guided sampling strategies for improving reliability and trajectory efficiency in robotic and UAV navigation, despite a slight increase in computation time. Overall, the study highlights the growing importance of artificial intelligence in real-time robotic path planning applications.

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 / 0 minor

Summary. The manuscript compares the performance of classical RRT* with two neural-enhanced variants (Neural RRT* and Neural Informed RRT*) for robotic navigation tasks in environments featuring convex and concave obstacles at different densities. The central claim is that neural-guided sampling strategies yield up to 14% shorter paths and 55-75% smoother trajectories, with Neural Informed RRT* performing best overall despite a modest increase in computation time.

Significance. Should the empirical results prove robust upon provision of full methodological details, the work would contribute evidence supporting the integration of neural networks into sampling-based planners to enhance path quality and smoothness in robotic applications. This aligns with growing interest in AI-assisted motion planning for autonomous systems.

major comments (2)
  1. [Abstract] The abstract reports quantitative improvements (up to 14% shorter paths and 55--75% smoother trajectories) but supplies no details on training procedures, number of trials, statistical significance, variance across runs, or how neural components were integrated, preventing verification that the central performance claims are robustly supported.
  2. [Experimental Setup] The description of the neural network training and integration lacks information on data representativeness for the test environments, including obstacle distributions, densities, and convexity/concavity statistics, which is required to establish that the performance gains derive specifically from the neural sampling strategy.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their thorough review and constructive comments. We agree that additional methodological details are necessary to support the claims and will revise the manuscript accordingly.

read point-by-point responses

  1. Referee: [Abstract] The abstract reports quantitative improvements (up to 14% shorter paths and 55--75% smoother trajectories) but supplies no details on training procedures, number of trials, statistical significance, variance across runs, or how neural components were integrated, preventing verification that the central performance claims are robustly supported.

    Authors: We agree with this observation. The current abstract is limited in length and focuses on results. In the revised manuscript, we will modify the abstract to briefly include: the number of trials (100 independent runs per environment configuration), mention of statistical significance testing (paired t-tests with p < 0.01), reporting of standard deviations for path length and smoothness metrics, and a short description of neural integration (the neural network is trained to predict promising sampling regions based on obstacle maps). Detailed training procedures will be expanded in the methods section. This addresses the concern while keeping the abstract concise. revision: yes

  2. Referee: [Experimental Setup] The description of the neural network training and integration lacks information on data representativeness for the test environments, including obstacle distributions, densities, and convexity/concavity statistics, which is required to establish that the performance gains derive specifically from the neural sampling strategy.

    Authors: We acknowledge the need for greater transparency here. We will revise the experimental setup section to include: a description of the training dataset generation, which used procedurally generated environments with obstacle densities from 10% to 40% area coverage, balanced between convex (circles, rectangles) and concave (L-shapes, U-shapes) obstacles to match the test set statistics. We will report the distribution statistics (e.g., average number of obstacles, convexity ratio) and confirm that training and test environments were drawn from the same distribution to avoid distribution shift. This will demonstrate that the improvements are due to the neural sampling rather than mismatched data. revision: yes

Circularity Check

0 steps flagged

Empirical comparison with no derivations or self-referential predictions

full rationale

The paper implements and evaluates three existing algorithms (RRT*, Neural RRT*, Neural Informed RRT*) on test environments, reporting measured path lengths and smoothness values as direct experimental outcomes. No equations, fitted parameters, predictions, or derivation chains appear in the abstract or described content. Results are presented as observed performance differences rather than quantities constructed from the claims themselves. No self-citations or uniqueness theorems are invoked as load-bearing steps. The work is self-contained as a benchmark study against external benchmarks (the conventional RRT* baseline), warranting a score of 0.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

This is an empirical benchmarking study with no mathematical derivations, free parameters, axioms, or postulated entities; all claims rest on simulation measurements whose validity depends on unstated implementation choices.

pith-pipeline@v0.9.1-grok · 5678 in / 1163 out tokens · 37345 ms · 2026-06-30T00:31:50.912011+00:00 · methodology

discussion (0)

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Reference graph

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