MACNS: A generic graph neural network integrated deep reinforcement learning based multi-agent collaborative navigation system for dynamic trajectory planning

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 propose a generic Multi-Agent Collaborative Navigation System (MACNS) to address the challenges. First, we formalise the solution of the problem and challenges into a Markov Decision Process (MDP), which is further developed as a training environment where a Deep Reinforcement Learning (DRL) agent can learn patterns efficiently. Second, the proposed framework integrates a Graph Neural Network (GNN) into the policy network of Proximal Policy Optimisation for the homogeneous decision-making of each individual agent, showing good generalisation and convergence speed. Finally, we demonstrate how MACNS can be applied and implemented in a real-world use case. Extensive simulations and real-world tests validate the effectiveness of the MACNS-based use case, showcasing its superiority over other state-of-the-art PPO-related methods in terms of planned routes and user experience.