Kino-PAX$^+$: Near-Optimal Massively Parallel Kinodynamic Sampling-based Motion Planner

2026-02-02

AI Summary

Existing sampling-based motion planners struggle to simultaneously achieve real-time performance, near-optimality, and parallel scalability under complex dynamic constraints. This work proposes a novel parallelized kinodynamic sampling-based planner that decouples the traditionally sequential planning pipeline into three massively parallel subroutines, constructs a sparse trajectory tree, and focuses propagation and optimization on high-potential nodes within local neighborhoods. The method is the first to provide asymptotic δ-robust near-optimality guarantees within a large-scale parallel framework, overcoming the fundamental limitation of conventional parallel SBMP approaches that cannot optimize objective functions. Experimental results demonstrate up to three orders of magnitude faster solution times compared to state-of-the-art serial methods, while also yielding higher-quality solutions than the best existing GPU-based planners.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Intelligent Robots: Motion and Path Planning

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Sampling-based motion planners (SBMPs) are widely used for robot motion planning with complex kinodynamic constraints in high-dimensional spaces, yet they struggle to achieve \emph{real-time} performance due to their serial computation design. Recent efforts to parallelize SBMPs have achieved significant speedups in finding feasible solutions; however, they provide no guarantees of optimizing an objective function. We introduce Kino-PAX$^{+}$, a massively parallel kinodynamic SBMP with asymptotic near-optimal guarantees. Kino-PAX$^{+}$ builds a sparse tree of dynamically feasible trajectories by decomposing traditionally serial operations into three massively parallel subroutines. The algorithm focuses computation on the most promising nodes within local neighborhoods for propagation and refinement, enabling rapid improvement of solution cost. We prove that, while maintaining probabilistic $\delta$-robust completeness, this focus on promising nodes ensures asymptotic $\delta$-robust near-optimality. Our results show that Kino-PAX$^{+}$ finds solutions up to three orders of magnitude faster than existing serial methods and achieves lower solution costs than a state-of-the-art GPU-based planner.

Problem

• kinodynamic motion planning
• sampling-based motion planners
• massively parallel planning
• real-time performance
• optimality guarantees

Innovation

• massively parallel
• kinodynamic planning
• asymptotic near-optimality
• sampling-based motion planning
• GPU acceleration