用深度强化学习优化多飞行器搬运任务的协作效率
Optimized Coordination Strategy for Multi-Aerospace Systems in Pick-and-Place Tasks By Deep Neural Network
- 基于深度神经网络的强化学习策略，自动分配飞行器任务
- 仿真中任务完成率提升16%，优于传统博弈论方法
- 适合需要高效协同的航天器、无人机编队应用
本文提出一种基于深度神经网络的多智能体航天系统协同控制策略，采用强化学习框架优化对象搬运任务中的自主任务分配。通过在MuJoCo环境中建模航天场景下的拾取-放置任务，使用深度强化学习算法训练基于DNN的策略，以最大化系统任务完成率。目标函数聚焦于提升有效物体转移率，利用神经网络处理高维状态与动作空间。大量仿真表明，所提方法相较基于博弈论的启发式组合策略，任务效率最高提升16%。进一步在多智能体硬件平台上进行实验验证，证明该方法在真实航天场景中的有效性。
原文摘要 · Abstract (English)
In this paper, we present an advanced strategy for the coordinated control of a multi-agent aerospace system, utilizing Deep Neural Networks (DNNs) within a reinforcement learning framework. Our approach centers on optimizing autonomous task assignment to enhance the system's operational efficiency in object relocation tasks, framed as an aerospace-oriented pick-and-place scenario. By modeling this coordination challenge within a MuJoCo environment, we employ a deep reinforcement learning algorithm to train a DNN-based policy to maximize task completion rates across the multi-agent system. The objective function is explicitly designed to maximize effective object transfer rates, leveraging neural network capabilities to handle complex state and action spaces in high-dimensional aerospace environments. Through extensive simulation, we benchmark the proposed method against a heuristic combinatorial approach rooted in game-theoretic principles, demonstrating a marked performance improvement, with the trained policy achieving up to 16\% higher task efficiency. Experimental validation is conducted on a multi-agent hardware setup to substantiate the efficacy of our approach in a real-world aerospace scenario.
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