大模型
arxiv.org ·
ASTRIL-MPC：语言引导神经-运动学 MPC 的铰接履带式机器人自主穿越框架
该论文提出 ASTRIL-MPC，面向城市搜救中铰接履带式机器人（ATR）在楼梯间和杂乱室内等接触丰富环境下的自主穿越。框架由三部分组成：学习型运动学模型根据高度序列与近期轨迹预测短时域任务状态增量；NMPC 以多目标代价和严格可行性约束进行规划；LLM 通过带范围裁剪、速率限制和一致性检查的安全接口，对选定权重与边界提出有界更新。编译后的预测器使完整控制周期在 100ms 内完成。在三个穿越任务及多高度泛化设置中，其综合穿越质量分数较非自适应 NMPC 最高提升 71%，较 PPO 基线提升 67%，并在下坡过程中消除了可测量的碰撞冲击。
AI推荐理由 arXiv 预印本，作者与摘要信息完整，方法描述具体（学习运动学模型 + NMPC + LLM 有界调参，控制周期 100ms 内），并给出相对非自适应 NMPC 提升 71%、相对 PPO 提升 67% 的量化结果，属可验证的研究进展。局限在于仅为预印本、未经同行评审，实验规模（三个穿越任务）有限，LLM 调参的实际增益与安全性未独立验证。
72
正文
Computer Science > Robotics
arXiv:2609.13083 (cs)
[Submitted on 11 Sep 2026]
Title:ASTRIL-MPC: Autonomous Traversal Framework of Articulated Tracked Robots with Language-Guided Neural-Kinematic MPC
Authors:Zhenfeng Gan, Yanbo Chen, Lirong Che, Junbo Tan, Xueqian Wang
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Abstract: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 learned kinematics, optimization-based planning, and language-guided retuning yields data-efficient and robust autonomy for articulated tracked robots.
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Subjects:
Robotics (cs.RO); Artificial Intelligence (cs.AI)
Cite as:
arXiv:2609.13083 [cs.RO]
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arXiv:2609.13083v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2609.13083
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From: Zhenfeng Gan [view email]
[v1]
Fri, 11 Sep 2026 17:17:52 UTC (32,242 KB)
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