ASTRIL-MPC: Autonomous Traversal Framework of Articulated Tracked Robots with Language-Guided Neural-Kinematic MPC
ASTRIL-MPC combines learned kinematics, NMPC, and LLM-guided safety-checked retuning for articulated tracked robot traversal in search-and-rescue.
ASTRIL-MPC is a language-guided neural-kinematic model predictive control framework for autonomous traversal of articulated tracked robots in urban search and rescue. A learned kinematics model predicts short-horizon task-state increments, NMPC plans with feasibility constraints, and an LLM proposes bounded, safety-checked updates to weights and bounds. The compiled predictor enables a full control cycle within 100 ms, improving traversal-quality scores by up to 71% over non-adaptive NMPC and 67% over a PPO baseline while eliminating measurable collision impacts.
- LLM proposes bounded, safety-checked updates to MPC weights and bounds
- Compiled predictor enables full control cycle within 100 ms
- Up to 71% improvement over non-adaptive NMPC across traversal tasks
From the original article
1 min read at arxiv.orgIn 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.
By Zhenfeng Gan · arxiv.org
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