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arxiv: 2606.02677 · v1 · pith:S7JBIKSDnew · submitted 2026-06-01 · 💻 cs.RO

Motion Planning in Dynamic Environments: A Survey from Classical to Modern Methods

Zongyuan Shen , Yaming Ou , Shalabh Gupta , Shancheng Zhao , Dehua Zhou , Gao Wang , Zhongqiang Ren , Junfeng Fan
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Long Cheng
This is my paper

Pith reviewed 2026-06-28 14:42 UTC · model grok-4.3

classification 💻 cs.RO
keywords motion planningdynamic environmentssampling-based planningmodel predictive controlreinforcement learningvelocity obstaclesdynamic perceptionrobot navigation
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The pith

Motion planning methods in dynamic environments fall into five categories that address both classical techniques and modern learning approaches.

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

The paper surveys 138 works on motion planning for robots in environments where obstacles move. It groups the methods into sampling-based, graph search, model predictive control, learning, and local planning approaches such as velocity obstacles. The review covers how dynamic perception using sensors helps with moving obstacles and examines challenges like prediction uncertainty and the freezing robot problem. This organization helps readers understand the principles and limitations of different methods. A reader would care because real-world robots must navigate safely among moving people and objects.

Core claim

The paper establishes a structured taxonomy for motion planning in dynamic environments by categorizing methods into five groups—sampling, graph search, model predictive control, learning, and classical local planning—while analyzing their principles, strengths, and limitations in the presence of moving obstacles and related challenges.

What carries the argument

The five-category taxonomy based on core concepts of sampling, graph search, model predictive control, learning, and local planning approaches including velocity obstacles, potential fields and dynamic windows.

If this is right

  • Readers gain a structured understanding of how different methods handle prediction uncertainty and human-robot interaction.
  • The survey identifies the freezing robot problem as a key challenge unique to dynamic settings.
  • Integration of dynamic perception techniques with planning is necessary for effective navigation among moving obstacles.

Where Pith is reading between the lines

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

  • New algorithms could be developed by combining elements from multiple categories to address gaps in handling uncertainty.
  • This taxonomy might serve as a basis for comparing performance across methods in standardized dynamic test environments.

Load-bearing premise

The 138 papers and five-category taxonomy together represent the field comprehensively without major omissions or misclassifications.

What would settle it

A significant motion planning technique published between 2015 and 2025 that cannot be classified into any of the five categories or is missing from the reviewed works would challenge the survey's completeness.

Figures

Figures reproduced from arXiv: 2606.02677 by Dehua Zhou, Gao Wang, Junfeng Fan, Long Cheng, Shalabh Gupta, Shancheng Zhao, Yaming Ou, Zhongqiang Ren, Zongyuan Shen.

Figure 1
Figure 1. Figure 1: Examples of motion planning in dynamic environments: (a) [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 3
Figure 3. Figure 3: Category-wise distribution of motion planning methods in [PITH_FULL_IMAGE:figures/full_fig_p002_3.png] view at source ↗
Figure 2
Figure 2. Figure 2: Taxonomy of motion planning in dynamic environments. [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 4
Figure 4. Figure 4: Timeline of the evolution of sampling-based motion planning methods in dynamic environments. [PITH_FULL_IMAGE:figures/full_fig_p003_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Different safety constraint formulations for dynamic obstacle [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Conformal prediction [82] is used to generate valid predic [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Representative MPC-based methods with high-level guidance. [PITH_FULL_IMAGE:figures/full_fig_p008_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Organization and main topics of learning-based motion planning methods in dynamic environments. [PITH_FULL_IMAGE:figures/full_fig_p009_8.png] view at source ↗
Figure 10
Figure 10. Figure 10: Multi-policy adaptation strategy: sensor information is used [PITH_FULL_IMAGE:figures/full_fig_p011_10.png] view at source ↗
Figure 9
Figure 9. Figure 9: Representative agent-agent interaction modeling methods: (a) [PITH_FULL_IMAGE:figures/full_fig_p011_9.png] view at source ↗
Figure 11
Figure 11. Figure 11: Classical planner strategy: a classical planner provides a [PITH_FULL_IMAGE:figures/full_fig_p012_11.png] view at source ↗
Figure 13
Figure 13. Figure 13: Overview of dynamic perception modalities, including camera-based [163], LiDAR-based [164], and event-based perception [165]. [PITH_FULL_IMAGE:figures/full_fig_p015_13.png] view at source ↗
read the original abstract

Motion planning in dynamic environments requires robots to continuously adapt their paths in response to environmental changes for safe and uninterrupted navigation. While many surveys have reviewed planning in static settings, systematic reviews focused on dynamic environments remain limited. This paper presents a comprehensive survey of 138 works, primarily published between 2015 and 2025, spanning both classical and learning-based approaches. The motion planning methods are grouped into five categories based on the concepts of sampling, graph search, model predictive control, learning, and additional classical local planning approaches, including velocity obstacles, potential fields and dynamic windows. The learning techniques include supervised learning and reinforcement learning. We also discuss the role of dynamic perception in motion planning, covering techniques for detecting and modeling moving obstacles using cameras, LiDAR, and event-based sensors. The survey analyzes the principles, strengths, and limitations of each method, with particular attention to challenges unique to dynamic environments, such as prediction uncertainty, human-robot interaction, and the freezing robot problem. The survey provides researchers with a structured understanding of motion planning methods in dynamic environments.

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

1 major / 0 minor

Summary. The paper presents a survey of 138 works (mostly 2015–2025) on motion planning in dynamic environments. It organizes methods into five categories—sampling-based, graph search, model predictive control, learning (supervised and reinforcement), and classical local planners (velocity obstacles, potential fields, dynamic window approach)—while also addressing dynamic perception with cameras, LiDAR, and event cameras, and analyzing strengths, limitations, and dynamic-specific challenges such as prediction uncertainty, human-robot interaction, and the freezing robot problem.

Significance. If the taxonomy and coverage prove accurate, the survey would be useful for the robotics community by consolidating recent literature on dynamic settings (where static surveys dominate) and explicitly linking perception to planning while highlighting open issues. The explicit treatment of learning methods alongside classical ones and the focus on practical challenges provide a structured entry point for researchers.

major comments (1)
  1. [Abstract and categorization sections] The central claim of a representative, non-overlapping five-category taxonomy is load-bearing for the survey's value. The manuscript (Abstract and categorization sections) presents the groupings as distinct but provides no explicit protocol or discussion for assigning hybrid methods (e.g., RL-augmented sampling planners or learned cost functions inside MPC), which have become common since 2015. Without such handling, overlaps or omissions cannot be ruled out, weakening the assertion of balanced coverage.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback on the taxonomy. We address the major comment below.

read point-by-point responses

  1. Referee: [Abstract and categorization sections] The central claim of a representative, non-overlapping five-category taxonomy is load-bearing for the survey's value. The manuscript (Abstract and categorization sections) presents the groupings as distinct but provides no explicit protocol or discussion for assigning hybrid methods (e.g., RL-augmented sampling planners or learned cost functions inside MPC), which have become common since 2015. Without such handling, overlaps or omissions cannot be ruled out, weakening the assertion of balanced coverage.

    Authors: We agree that the manuscript does not include an explicit protocol for assigning hybrid methods, and that this omission weakens the claim of a non-overlapping taxonomy. In the revised manuscript we will add a new subsection (tentatively 2.6) immediately after the five-category overview that states the classification rule: each method is assigned to the category matching its dominant algorithmic paradigm, with hybrid components noted and cross-referenced to the secondary category. Examples of RL-augmented sampling and learned-cost MPC will be provided, together with a short table listing the 138 surveyed papers that contain hybrid elements and their assigned primary category. This addition will be referenced from the abstract and from the introduction. revision: yes

Circularity Check

0 steps flagged

No circularity: descriptive survey with no derivations or predictions

full rationale

This paper is a literature review that organizes 138 existing works into five categories (sampling, graph search, MPC, learning, classical local). It contains no equations, fitted parameters, predictions, uniqueness theorems, or first-principles derivations. The taxonomy is an organizational framework chosen by the authors, not derived from or equivalent to any input data or self-citation. No load-bearing step reduces to a self-citation chain or by-construction equivalence. The paper is self-contained as a descriptive survey against external benchmarks (the cited literature).

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

As a survey the paper introduces no new free parameters, axioms, or invented entities; all content rests on the cited prior literature.

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Reference graph

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