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An Optimized Path Planning Method for Coastal Ships Based on Improved DDPG and DP
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Unmanned surface vehicles: An overview of developments and challenges
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Hybrid Path Planning Algorithm Based on Membrane Pseudo-Bacterial Potential Field for Autonomous Mobile Robots
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Learn to Navigate: Cooperative Path Planning for Unmanned Surface Vehicles Using Deep Reinforcement Learning
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AUV Path Planning Based on Improved Genetic Algorithm
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Cooperative Game-based Multi-Agent Path Planning with Obstacle Avoidance
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An adaptive obstacle avoidance algorithm for unmanned surface vehicle in complicated marine environments
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Long-Distance Path Planning for Unmanned Surface Vehicles in Complex Marine Environment
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Energy-efficient Path Planning and Control Approach of USV Based on Particle Swarm optimization
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Improved Artificial Potential Field Method Applied for AUV Path Planning
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A Decision-Making Method for Ship Collision Avoidance Based on Improved Cultural Particle Swarm
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ALGORITHMS FOR THE REDUCTION OF THE NUMBER OF POINTS REQUIRED TO REPRESENT A DIGITIZED LINE OR ITS CARICATURE
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Path Planning of Coastal Ships Based on Optimized DQN Reward Function
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Decision-Making for the Autonomous Navigation of Maritime Autonomous Surface Ships Based on Scene Division and Deep Reinforcement Learning
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An Autonomous Path Planning Model for Unmanned Ships Based on Deep Reinforcement Learning
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Memory-based control with recurrent neural networks
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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