Deep Reinforcement Learning based Multi-Agent Collaborative Navigation System for Dynamic Trajectory Planning
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Multi-agent collaborative navigation is prevalent in modern transportation systems, including delivery logistics, warehouse automation, and personalised tourism, where multiple agents must converge at a common destination from different starting points. However, the challenges lie in optimising routes for multiple agents while dynamically adjusting the common destination in response to changing traffic conditions. Therefore, we would like to propose a generic Multi-Agent Collaborative Navigation System (MACNS) to address the challenges.