Multi-Objective Kinodynamic Motion Planning with Asymptotic Pareto Optimality
This paper addresses multi-objective motion planning for kinodynamic systems, proposing a unified framework based on Stable Sparse-RRT (SST). By replacing the single representative node with a set of locally Pareto-optimal nodes, it yields three algorithms: lexSST, coSST, and poSST, offering theoretical guarantees and empirical validation.
-->
[Submitted on 16 Jul 2026]
Title:Multi-Objective Kinodynamic Motion Planning with Asymptotic Pareto Optimality
View a PDF of the paper titled Multi-Objective Kinodynamic Motion Planning with Asymptotic Pareto Optimality, by Yusif Razzaq and 3 other authors
View PDF HTML (experimental)
Abstract:In this paper, we address the challenge of multi-objective motion planning for systems under kinodynamic constraints. We consider three problem classes: (i) lexicographic optimization, in which objectives are minimized according to a strict priority ordering, (ii) constrained optimization, in which a primary objective is minimized subject to bounds on the remaining costs, and (iii) Pareto front optimization, in which the goal is to approximate the full set of optimal trade-offs among competing objectives. We first show that established cost scalarization methods for multi-objective problems cannot be extended to continuous-domain systems with correctness guarantees. Then, we propose a unified algorithmic framework built upon the Stable Sparse-RRT (SST) algorithm, in which the single representative maintained at each witness neighborhood is replaced by a representative set of locally Pareto-optimal nodes. This structure gives rise to three distinct algorithms: lexSST for lexicographic minimization, coSST for constrained optimization, and poSST for Pareto-front approximation. We provide theoretical guarantees for the completeness and optimality of our algorithms and demonstrate their effectiveness through extensive empirical evaluations.
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2607.15508 [cs.RO]
(or arXiv:2607.15508v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2607.15508
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Yusif Razzaq [view email] [v1] Thu, 16 Jul 2026 23:32:59 UTC (5,152 KB)
Full-text links:
Access Paper:
View a PDF of the paper titled Multi-Objective Kinodynamic Motion Planning with Asymptotic Pareto Optimality, by Yusif Razzaq and 3 other authors
View PDF
HTML (experimental)
TeX Source
view license
Current browse context:
cs.RO
new | recent | 2026-07
Change to browse by:
cs
References & Citations
NASA ADS
Google Scholar
Semantic Scholar
Loading...
Data provided by:
Bibliographic Tools
Bibliographic and Citation Tools
Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
Connected Papers Toggle
Connected Papers (What is Connected Papers?)
Litmaps Toggle
Litmaps (What is Litmaps?)
scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
Code, Data, Media
Code, Data and Media Associated with this Article
alphaXiv Toggle
alphaXiv (What is alphaXiv?)
Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub Toggle
DagsHub (What is DagsHub?)
GotitPub Toggle
Gotit.pub (What is GotitPub?)
Huggingface Toggle
Hugging Face (What is Huggingface?)
ScienceCast Toggle
ScienceCast (What is ScienceCast?)
Demos
Demos
Replicate Toggle
Replicate (What is Replicate?)
Spaces Toggle
Hugging Face Spaces (What is Spaces?)
Spaces Toggle
TXYZ.AI (What is TXYZ.AI?)
Related Papers
Recommenders and Search Tools
Link to Influence Flower
Influence Flower (What are Influence Flowers?)
Core recommender toggle
CORE Recommender (What is CORE?)
Author
Venue
Institution
Topic
About arXivLabs
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)