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Optimized Dynamic Collision Avoidance Algorithm for USV Path Planning.

Sensors (Basel, Switzerland)·2023
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Risk-Sensitive Markov Decision Processes of USV Trajectory Planning with Time-Limited Budget.

Yi Ding1, Hongyang Zhu2

  • 1Maritime College, Guangdong Ocean University, Zhanjiang 524091, China.

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|September 28, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces the Markov decision process Heuristic Algorithm (MHA) for efficient Unmanned Surface Vehicle (USV) collision avoidance. The MHA optimizes ship navigation by balancing time costs and collision risks.

Keywords:
Risk-Sensitive Markov decision processestrajectory optimizationtrajectory planning strategy

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Area of Science:

  • Maritime Navigation and Robotics
  • Artificial Intelligence and Machine Learning

Background:

  • Safe ship navigation relies on complex trajectory planning.
  • Unmanned Surface Vehicles (USVs) require robust collision avoidance strategies.

Purpose of the Study:

  • To develop a time-optimized collision avoidance algorithm for USVs.
  • To enhance trajectory planning using a Risk-Sensitive Markov decision process model.

Main Methods:

  • Introduced the Markov decision process Heuristic Algorithm (MHA).
  • Utilized a Risk-Sensitive Markov decision process model for state generation.
  • Incorporated enhanced reward and time-dependent cost functions for path planning.

Main Results:

  • The MHA algorithm effectively plans practical motion paths within time constraints.
  • Decision-makers can evaluate the trade-off between budget and goal achievement probability.
  • Local stochastic optimization aids in selecting collision avoidance paths with minimal risk increase.

Conclusions:

  • The MHA provides an effective solution for time-optimized USV collision avoidance.
  • The algorithm supports informed decision-making regarding risk and resource allocation.
  • This approach enhances the safety and efficiency of autonomous maritime navigation.