# 基于深度强化学习与进化混合设计的视觉群体导航

> 原文链接：https://robotics.hellobike.com/papers/2026-09-22-2610-06400
> 频道：papers | 更新日期：2026-09-22 | 标签：arxiv, cs.RO
> 核心摘要：提出结合多智能体强化学习与神经进化的混合方法，在高保真仿真中合成并验证单目视觉群体导航控制器。

# 基于深度强化学习与进化混合设计的视觉群体导航

::: v-pre
> 原文链接：[Visual swarm navigation via deep reinforcement learning and evolutionary hybrid design](https://arxiv.org/abs/2610.06400)（arXiv 2610.06400 · 发布日期：2026-09-22）

**Visual swarm navigation via deep reinforcement learning and evolutionary hybrid design**

提出结合多智能体强化学习与神经进化的混合方法，在高保真仿真中合成并验证单目视觉群体导航控制器。

## 核心要点

- 面向群体机器人，解决分散式控制器的数据驱动设计难题，以产生自主视觉导航的集体行为。
- 方法将多智能体强化学习与交叉熵方法、协方差矩阵自适应进化策略结合，优化预训练个体导航策略。
- 控制器采用紧凑神经网络，仅依赖单目相机图像，面向低成本、资源受限平台的计算与能耗效率。
- 高保真物理仿真实验显示，交叉熵训练控制器较协方差矩阵自适应进化策略多访问36.20%区域，并平均降耗31.40%。

**验证范围与局限**：验证范围限于高保真物理仿真中的多样室内环境集体探索，摘要未报告真机实验。

## 英文摘要 (Abstract)

Swarm robotics presents a robust and cost-effective paradigm for advanced automation in complex, dynamic environments, such as those encountered in search and rescue or environmental monitoring. A fundamental challenge for this field is the data-driven design of decentralized controllers capable of generating emergent collective behaviors. This paper proposes a novel, AI-driven hybrid methodology for the automatic synthesis of swarm robotic controllers for autonomous visual navigation. This approach synergistically combines multi-agent reinforcement learning with neuro-evolutionary strategies, specifically leveraging implementations of the cross-entropy method and the covariance matrix adaptation evolution strategy to optimize a pre-trained individual navigation policy. The underlying deep architecture is engineered for low-cost, resource-constrained platforms, utilizing a compact neural network that relies exclusively on monocular camera imagery. This vision-based design emphasizes computational and energy efficiency, a critical requirement for practical swarm deployments. Experiments, performed in a high-fidelity physics simulator, demonstrate that the resulting controllers enable robust and scalable collective exploration of diverse indoor environments. The controller trained using our cross-entropy method achieves superior exploration coverage, visiting 36.20% more regions compared to the covariance matrix adaptation evolution strategy. Critically, our best vision-based policy achieves exploration performance statistically comparable to traditional methods relying on more expensive distance sensors, while delivering a significant 31.40% average reduction in energy consumption. These findings validate an effective and economically viable autonomous control system, establishing a path for deploying highly efficient collective intelligence in real-world engineering applications.

作者：Álvaro Díez, Fidel Aznar
:::

## 原始参考来源 (Sources)

- [arXiv 2610.06400：Visual swarm navigation via deep reinforcement learning and evolutionary hybrid design](https://arxiv.org/abs/2610.06400)