ManeuverNet: 一种用于双Ackermann转向机器人精确 maneuvering 的软演员-评论框架与优化奖励函数
ManeuverNet: A Soft Actor-Critic Framework for Precise Maneuvering of Double-Ackermann-Steering Robots with Optimized Reward Functions
AI总结：
ManeuverNet通过结合Soft Actor-Critic与CrossQ，提出了一种针对双Ackermann系统的DRL框架，利用优化奖励函数提升机器人精确 maneuvering 的性能和鲁棒性。
摘要：
双Ackermann转向机器人在农业应用中的自主控制至关重要，其中机器人必须在有限空间内执行精确且复杂的 maneuvering。经典方法，如Timed Elastic Band (TEB)规划器，可以解决这个问题，但它们依赖于参数调优，使其对机器人配置或环境的变化高度敏感，并且在没有持续重新校准的情况下难以部署。同时，端到端深度强化学习（DRL）方法往往因不适用于非holonomic约束的奖励函数而失败，导致策略次优且泛化能力差。为了解决这些挑战，本文提出了ManeuverNet，一种专为双Ackermann系统设计的DRL框架，结合了Soft Actor-Critic与CrossQ。此外，ManeuverNet引入了四个专门设计的奖励函数以支持maneuver学习。与以往工作不同，ManeuverNet不依赖专家数据或手工指导。我们广泛评估了ManeuverNet与最先进的DRL基线和TEB规划器。实验结果表明，我们的框架显著提高了 maneuverability 和成功率，相较于DRL基线提高了超过40%。此外，ManeuverNet有效缓解了TEB规划器中观察到的强参数敏感性。在现实世界试验中，ManeuverNet实现了 maneuvering轨迹效率的90%提升，突显了其鲁棒性和实用性。
英文摘要：
Autonomous control of double-Ackermann-steering robots is essential in agricultural applications, where robots must execute precise and complex maneuvers within a limited space. Classical methods, such as the Timed Elastic Band (TEB) planner, can address this problem, but they rely on parameter tuning, making them highly sensitive to changes in robot configuration or environment and impractical to deploy without constant recalibration. At the same time, end-to-end deep reinforcement learning (DRL) methods often fail due to unsuitable reward functions for non-holonomic constraints, resulting in sub-optimal policies and poor generalization. To address these challenges, this paper presents ManeuverNet, a DRL framework tailored for double-Ackermann systems, combining Soft Actor-Critic with CrossQ. Furthermore, ManeuverNet introduces four specifically designed reward functions to support maneuver learning. Unlike prior work, ManeuverNet does not depend on expert data or handcrafted guidance. We extensively evaluate ManeuverNet against both state-of-the-art DRL baselines and the TEB planner. Experimental results demonstrate that our framework substantially improves maneuverability and success rates, achieving more than a 40% gain over DRL baselines. Moreover, ManeuverNet effectively mitigates the strong parameter sensitivity observed in the TEB planner. In real-world trials, ManeuverNet achieved up to a 90% increase in maneuvering trajectory efficiency, highlighting its robustness and practical applicability.
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。
