AI 中文总结
针对铰接履带机器人在复杂环境中的自主穿越难题，提出结合学习运动学模型、NMPC和LLM调参的ASTRIL-MPC框架，显著提升穿越质量并消除碰撞。
AI 中文摘要
在城市搜索与救援中，铰接履带机器人（ATRs）必须穿越结构化但接触丰富的环境，如楼梯间和杂乱的建筑内部。可靠的自主性仍然具有挑战性，因为机器人-地形交互（RTI）是混合且不连续的，且有效的履带-摆臂协调难以用解析方法建模。我们提出了ASTRIL-MPC，一种用于自主穿越的语言引导的神经运动学模型预测控制（MPC）框架。学习到的运动学模型从高度序列和近期轨迹预测短时域任务状态增量；NMPC采用多目标代价和严格可行性约束进行规划；大型语言模型（LLM）通过带范围裁剪、速率限制和一致性检查的安全检查接口，提出对选定权重和界限的有界更新。编译后的预测器使完整控制周期在100毫秒内完成。在三个穿越任务和一个多高度泛化设置中，ASTRIL-MPC将聚合穿越质量分数比非自适应NMPC提高了最多71%，比PPO基线提高了67%，同时在下坡过程中消除了可测量的碰撞冲击。这些结果表明，将学习到的运动学、基于优化的规划和语言引导的重新调整相结合，可为铰接履带机器人带来数据高效且稳健的自主性。
英文摘要
In urban search and rescue, articulated tracked robots (ATRs) must traverse structured but contact-rich environments such as stairwells and cluttered building interiors. Reliable autonomy remains challenging because robot-terrain interaction (RTI) is hybrid and discontinuous, and effective flipper-track coordination is difficult to model analytically. We present ASTRIL-MPC, a language-guided neural kinematics model predictive control (MPC) framework for autonomous traversal. A learned kinematics model predicts short-horizon task-state increments from a height sequence and recent trajectories; NMPC plans with multi-objective costs and strict feasibility constraints; and a large language model (LLM) proposes bounded updates to selected weights and bounds through a safety-checked interface with range clipping, rate limiting, and consistency checks. The compiled predictor enables a full control cycle within 100 ms. Across three traversal tasks and a multi-height generalization setting, ASTRIL-MPC improves an aggregate traversal-quality score by up to 71% over a non-adaptive NMPC and by 67% over a PPO baseline, while eliminating measurable collision impacts during descent. These results indicate that combining terrain-conditioned neural kinematics, optimization-based planning, and language-guided adaptation yields data-efficient and robust autonomy for articulated tracked robots. Real-robot trials over four indoor obstacles further demonstrate transfer to contact-rich physical traversal.
