发表机构
The University of Osaka; Kobe University（大阪大学; 神户大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出D3DWA，基于决斗双深度Q网络自适应调整动态窗口法的评估权重和预测时域，在模拟和真实机器人实验中均成功完成导航任务，优于仅调整权重的变体。
AI 中文摘要
动态窗口法（DWA）被广泛用于局部导航，但其性能强烈依赖于通常在导航前固定的参数。特别是，合适的预测时域可能随局部自由空间而变化：较长的时域支持在开阔区域中的高效运动，而较短的时域有助于在狭窄或杂乱区域中保持可行运动。本文提出D3DWA，一种基于决斗双深度Q网络（D3QN）的自适应DWA框架，该框架在每个控制步骤从连续导航状态中联合选择DWA评估权重和预测时域，同时保留DWA的轨迹生成和碰撞检查。在八个模拟环境中，包括未见过的布局，D3DWA到达了每个目标。真实机器人实验进一步表明，D3DWA完成了所有三个测试配置，包括一个仅权重变体超时的受限案例。这些结果证明了联合自适应评估权重和预测时域的益处。附加材料可在该https URL获取。
英文摘要
The Dynamic Window Approach (DWA) is widely used for local navigation, but its performance depends strongly on parameters that are typically fixed before navigation. In particular, the appropriate prediction horizon can vary with local free space: longer horizons support efficient motion in open areas, whereas shorter horizons help preserve feasible motions in narrow or cluttered regions. This paper proposes D3DWA, an adaptive DWA framework based on a Dueling Double Deep Q-Network (D3QN), which jointly selects the DWA evaluation weights and prediction horizon from a continuous navigation state at every control step while retaining DWA's trajectory generation and collision checking. In eight simulated environments, including unseen layouts, D3DWA reached every goal. Real-robot experiments further showed that D3DWA completed all three tested configurations, including a constrained case in which the weights-only variant timed out. These results demonstrate the benefit of jointly adapting the evaluation weights and prediction horizon. Additional material is available at https://mertcookimg.github.io/d3dwa/
