Marine Robotics Inspired Adaptive Deep-Sea Exploration Optimization Article Swipe
Marine robots and autonomous underwater vehicles (AUVs) have wide applications in deep-sea exploration, environmental monitoring, resource exploration, and seabed engineering. However, traditional optimization algorithms struggle to fully simulate the exploration behavior of underwater robots in complex environments, due to highly uncertain currents, limited energy, and the multi-objective nature of deep-sea exploration missions. Consequently, they are prone to getting trapped in local optima or experiencing slow convergence in complex environments. This paper proposes a novel optimization algorithm—the Marine Robot-Inspired Adaptive Deep-Sea Exploration Optimization Algorithm (MRA-DSEO). This algorithm combines the characteristics of underwater AUVs and proposes a current-aware weighted (FAW) mechanism, a depth-layered exploration strategy (DLE), an energy-driven dynamic strategy switching mechanism, and a population distribution equilibrium mechanism. Through the synergy of global exploration and local fine-grained search, it achieves efficient search of complex optimization spaces. The paper provides an in-depth analysis of the algorithm's principles, mathematical formulas, and innovative mechanisms, offering theoretical references for underwater exploration optimization problems.
Related Topics
Concepts
Underwater
Convergence (economics)
Robot
Artificial intelligence
Population
Computer science
Optimization problem
Engineering
Robotics
Resource (disambiguation)
Mathematical optimization
Local optimum
Adaptive optimization
Control engineering
Global optimization
Optimization algorithm
Remotely operated underwater vehicle
Adaptive strategies
Local search (optimization)
Mobile robot
Genetic algorithm
Robust optimization
Adaptive system
Resource management (computing)
Metadata
- Type
- preprint
- Landing Page
- https://doi.org/10.5281/zenodo.17853132
- OA Status
- green
- OpenAlex ID
- https://openalex.org/W7111130804
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W7111130804Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.5281/zenodo.17853132Digital Object Identifier
- Title
-
Marine Robotics Inspired Adaptive Deep-Sea Exploration OptimizationWork title
- Type
-
preprintOpenAlex work type
- Publication year
-
2025Year of publication
- Publication date
-
2025-12-08Full publication date if available
- Authors
-
Zhang JinchengList of authors in order
- Landing page
-
https://doi.org/10.5281/zenodo.17853132Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.5281/zenodo.17853132Direct OA link when available
- Concepts
-
Underwater, Convergence (economics), Robot, Artificial intelligence, Population, Computer science, Optimization problem, Engineering, Robotics, Resource (disambiguation), Mathematical optimization, Local optimum, Adaptive optimization, Control engineering, Global optimization, Optimization algorithm, Remotely operated underwater vehicle, Adaptive strategies, Local search (optimization), Mobile robot, Genetic algorithm, Robust optimization, Adaptive system, Resource management (computing)Top concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
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