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Recent advances in Rapidly-exploring random tree: A review.

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This review summarizes recent advancements in Rapidly-exploring Random Tree (RRT) algorithms for robot path planning. While improved RRTs show promise in complex environments, challenges in hyper-parameter tuning and hardware reliability persist.

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Branching strategy improvementModel drivenPost-processingRapidly-exploring random treeRobotSampling strategy improvement

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Area of Science:

  • Robotics and Artificial Intelligence
  • Path Planning Algorithms
  • Autonomous Systems

Background:

  • Path planning is a critical area in robotics, essential for autonomous navigation.
  • The Rapidly-exploring Random Tree (RRT) algorithm offers unique search and sampling properties for generating high-quality paths.
  • Recent research (2021-2023) has focused on enhancing RRT algorithms both theoretically and in practical applications.

Purpose of the Study:

  • To provide a comprehensive review of RRT-based improved algorithms from 2021 to 2023.
  • To highlight theoretical advancements and diverse application implementations of RRT.
  • To identify current challenges and future research directions for RRT path planning.

Main Methods:

  • Review of theoretical improvements including branching, sampling, and post-processing strategies, as well as model-driven RRT.
  • Detailed examination of RRT applications in various robots: welding, assembly, search and rescue, surgical, space, and inspection.
  • Analysis of challenges in hyper-parameter design, generalization, and hardware reliability.

Main Results:

  • RRT-based algorithms demonstrate advantages in large-scale, real-time, and uncertain environments.
  • Model-driven RRT facilitates the design of strategies difficult to quantify.
  • Significant challenges remain in hyper-parameter tuning, generalization, and hardware integration for long-term stability.

Conclusions:

  • Despite advancements, RRT path planning faces hurdles in hyper-parameter optimization and generalization.
  • Hardware reliability, robot localization, scene modeling, and multi-robot collaboration are key factors influencing RRT performance.
  • Future trends include multi-robot and human-robot collaboration, real-time planning, self-tuning algorithms, and dynamic environment adaptation.