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Related Experiment Video

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Operation of the Collaborative Composite Manufacturing CCM System
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High-Frequency Replanning Under Uncertainty Using Parallel Sampling-Based Motion Planning.

Wen Sun1, Sachin Patil2, Ron Alterovitz3

  • 1Robotics Institute, Carnegie Mellon University, Pittsburgh, PA 15213 USA.

IEEE Transactions on Robotics : a Publication of the IEEE Robotics and Automation Society
|August 18, 2015
PubMed
Summary
This summary is machine-generated.

High-frequency replanning (HFR) enables robots to react to real-time uncertainties by executing motion planners in parallel. This approach achieves optimal robot navigation and task completion, even with complex dynamics and noise.

Keywords:
motion and path planningmotion planning under uncertaintysampling-based methods

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Control Theory

Background:

  • Advancements in sampling-based motion planners enable faster re-execution during robot task execution.
  • Robots face uncertainties in motion, obstacles, sensing, and kinematic models.

Purpose of the Study:

  • Investigate and analyze high-frequency replanning (HFR) for robotic systems.
  • Evaluate HFR's effectiveness in reacting to dynamic environmental and system uncertainties.

Main Methods:

  • Developed a framework for HFR where motion planners run in parallel while the robot executes actions.
  • Modeled discrete-time systems with stochastic nonlinear dynamics and noisy observations (Gaussian distributions).
  • Defined objectives: maximizing success probability or minimizing path length with success probability constraints.

Main Results:

  • Demonstrated that HFR offers asymptotic optimality as parallel computation power increases.
  • Showcased HFR's effectiveness for both holonomic and nonholonomic robots.
  • Validated HFR for diverse applications including car-like vehicles and steerable medical needles.

Conclusions:

  • HFR is a robust strategy for enhancing robot performance and adaptability in uncertain environments.
  • The parallel execution of motion planners significantly improves navigation success and efficiency.
  • HFR provides a scalable solution for complex robotic tasks across various platforms.