学习微型气球机器人倒置姿态的鲁棒控制策略
Learning Robust Control Policies for Inverted Pose on Miniature Blimp Robots
AI总结：
本文提出了一种新型框架，通过仿真与现实映射层，实现微型气球机器人倒置姿态的鲁棒控制策略学习。
摘要：
实现和维持倒置姿态的能力对于解锁微型气球机器人（MBRs）的全部敏捷性至关重要。然而，为MBRs开发可靠的倒置控制策略仍然具有挑战性，因为它们的动力学复杂且欠驱动。为了解决这一挑战，我们提出了一种新的框架，该框架能够为MBRs的倒置姿态实现鲁棒的控制策略学习。所提出的框架由三个核心阶段组成。首先，使用真实世界MBR运动数据构建并校准了一个高保真的三维（3D）仿真环境。第二，使用改进的双延迟深度确定性策略梯度（TD3）算法结合领域随机化策略，在仿真中训练了一个鲁棒的倒置控制策略。第三，设计了一个映射层，以弥合仿真到现实的差距，并促进所学策略在现实世界中的部署。在仿真环境中的全面评估显示，所学的策略在成功率上比能量塑造控制器更高。此外，实验结果证实，带有映射层的所学策略使MBR能够在现实世界中实现并维持完全倒置姿态。
英文摘要：
The ability to achieve and maintain inverted poses is essential for unlocking the full agility of miniature blimp robots (MBRs). However, developing reliable inverted control strategies for MBRs remains challenging due to their complex and underactuated dynamics. To address this challenge, we propose a novel framework that enables robust control policy learning for inverted pose on MBRs. The proposed framework consists of three core stages. First, a high-fidelity three-dimensional (3D) simulation environment is constructed and calibrated using real-world MBR motion data. Second, a robust inverted control policy is trained in simulation using a modified Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm combined with a domain randomization strategy. Third, a mapping layer is designed to bridge the sim-to-real gap and facilitate real-world deployment of the learned policy. Comprehensive evaluations in the simulation environment demonstrate that the learned policy achieves a higher success rate compared to the energy-shaping controller. Furthermore, experimental results confirm that the learned policy with a mapping layer enables an MBR to achieve and maintain a fully inverted pose in real-world settings.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
