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Multi-objective path planning for unmanned surface vehicle with currents effects.

Yong Ma1, Mengqi Hu2, Xinping Yan3

  • 1School of Navigation, Wuhan University of Technology, 1178 Heping Road, Wuhan, Hubei, 430063, PR China; Hubei Key Laboratory of Inland Shipping Technology, 1178 Heping Road, Wuhan, Hubei, 430063, PR China.

ISA Transactions
|February 20, 2018
PubMed
Summary
This summary is machine-generated.

This study presents a new method for unmanned surface vehicle (USV) path planning, optimizing for safety, speed, and cost. The dynamic augmented multi-objective particle swarm optimization algorithm finds ideal paths, overcoming obstacles and currents.

Keywords:
CurrentsDynamic augmented multi-objective particle swarm optimizationMulti-objective path planningUnmanned surface vehicle

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Area of Science:

  • Robotics and Autonomous Systems
  • Optimization Algorithms
  • Marine Engineering

Background:

  • Path planning for unmanned surface vehicles (USV) is complex due to environmental factors like obstacles and currents.
  • Existing methods often struggle to balance multiple objectives such as path length, smoothness, economy, and safety.
  • USV navigation requires robust algorithms that respect operational constraints like collision avoidance and velocity limits.

Purpose of the Study:

  • To address the multi-objective path planning challenge for unmanned surface vehicles (USV).
  • To develop an optimization framework that considers shortest, smoothest, most economical, and safest path criteria.
  • To propose a novel algorithm capable of handling complex constraints in USV operations.

Main Methods:

  • Formulating the USV path planning as a multi-objective nonlinear optimization problem with extensive constraints.
  • Developing and applying a dynamic augmented multi-objective particle swarm optimization (DAMO-PSO) algorithm.
  • Utilizing Pareto optimality to identify a set of ideal trade-off paths.

Main Results:

  • The proposed method successfully formulates the complex USV path planning problem.
  • The dynamic augmented multi-objective particle swarm optimization algorithm provides effective solutions.
  • Numerical simulations demonstrate the algorithm's capability in finding optimal paths considering multiple objectives and constraints.

Conclusions:

  • The formulated model and the proposed DAMO-PSO algorithm are effective for USV path planning.
  • USVs can select optimal paths from the Pareto set, balancing competing objectives.
  • This research contributes to the advancement of autonomous marine navigation systems.