A genetic algorithm-inspired UUV path planner based on dynamic programming

Chi Tsun Cheng, Kia Fallahi, Henry Leung, Chi Kong Tse

Research output: Journal article publicationJournal articleAcademic researchpeer-review

51 Citations (Scopus)


Path planning can be viewed as an optimization process in which an optimum path between two points is to be found under some predefined constraints. Some typical constraints are path length, fuel consumption, and path safety factor. Exact algorithms such as linear programming (LP) and dynamic programming (DP) are widely adopted in vehicle maneuvering systems. However, as the problem domain scales up, exact algorithms suffer from high computational complexity. In contrast, metaheuristic algorithms such as evolutionary algorithms (EA) and genetic algorithms (GA) can provide suboptimum solutions without the full understanding of the problem domain. Metaheuristic algorithms are capable of providing decent solutions within a finite period of time, even for large-scaled problems. In this paper, a GA-inspired unmanned underwater vehicle (UUV) path planner based on DP is proposed. Simulation results show that the proposed algorithm can outperform a GA-based UUV path planner in terms of speed and solution quality.
Original languageEnglish
Article number6135820
Pages (from-to)1128-1134
Number of pages7
JournalIEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews
Issue number6
Publication statusPublished - 24 Jan 2012


  • Dynamic programming (DP)
  • genetic algorithms (GA)
  • optimization methods
  • path planning
  • underwater vehicle control

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Information Systems
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering


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