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Updated: Aug 17, 2025

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A Sampling-Based Algorithm with the Metropolis Acceptance Criterion for Robot Motion Planning.

Yiyang Liu1,2,3,4, Yang Zhao1,2,3,5, Shuaihua Yan1,2,3,6

  • 1Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang 110016, China.

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Summary
This summary is machine-generated.

Metropolis RRT* (M-RRT*) enhances robot motion planning by introducing a two-phase approach. This method improves the convergence rate of the Rapidly exploring Random Tree* (RRT*) algorithm, boosting its practical performance.

Keywords:
Metropolis acceptance criterionRRTasymptotic optimalitymotion planningsampling-based algorithms

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Area of Science:

  • Robotics and Artificial Intelligence
  • Computational Geometry and Algorithms

Background:

  • Rapidly exploring Random Tree* (RRT*) is a key algorithm for motion planning in robotics, valued for its asymptotic optimality.
  • However, RRT*'s performance degrades with increasing path complexity due to slow convergence and high computational cost.
  • This limits its real-world applicability in complex robotic systems.

Purpose of the Study:

  • To address the convergence and performance limitations of the RRT* motion planning algorithm.
  • To introduce a novel, efficient motion planning algorithm that maintains asymptotic optimality while improving speed.
  • To enhance the practical utility of RRT* in complex robotic applications.

Main Methods:

  • Proposes Metropolis RRT* (M-RRT*), a two-phase motion planning algorithm utilizing the Metropolis acceptance criterion.
  • Phase 1: Employs an asymptotic vertex acceptance criterion for efficient initial path estimation.
  • Phase 2: Implements a nonlinear dynamic vertex acceptance criterion to prioritize path-improving vertices, accelerating convergence.

Main Results:

  • M-RRT* demonstrates significantly improved convergence rates compared to existing RRT* variants.
  • Simulations in diverse environments confirm the effectiveness of M-RRT* in motion planning tasks.
  • The algorithm successfully balances optimality with reduced computation time.

Conclusions:

  • M-RRT* offers a substantial improvement over standard RRT* for robotic motion planning.
  • The proposed two-phase approach effectively accelerates convergence without sacrificing asymptotic optimality.
  • This enhanced algorithm shows great promise for real-time robotic applications requiring efficient pathfinding.