外观
论文arXiv 2610.064001 个月前
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：Visual swarm navigation via deep reinforcement learning and evolutionary hybrid design（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
来源
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