A novel framework, LLM-Augmented Semantic Reasoning for Robust AUV Docking (DockLLM), which pioneers the integration of multi-modal large models’ semantic generalization capabilities into the underwater docking task and maintains a robust 89% success rate under simulated heavy occlusion scenarios.
This paper proposes LSTP-Nav, a lightweight, decentralized navigation framework built on LSTP-Net that maps stacked 2D LiDAR observations, goal information, and velocity feedback directly to action and introduces an HS reward to provide smooth, heading-aware safety feedback, and develops PhysReplay-SimLab to improve tr...
Xingrong Diao, Zhi-Qiang Sun, Jian-Wei Peng et al.· IEEE Transactions on Automat...· 0 citations
This work presents an instruction-grounded semantic enhancement module that injects object-level semantics and relative spatial cues into the current observation state, and develops a relevance-aware dynamic temporal aggregation strategy that reweights the full history buffer while converting a few high-relevance frame...
This paper presents an observation-only autonomy framework for Unmanned Underwater Vehicles (UUVs) navigation in dynamic underwater environments that integrates persistent occupancy mapping, global clearance-aware planning, and risk-aware local control. The proposed pipeline constructs occupancy maps solely from onboar...
LAPF is the only evaluated approach that couples every detected hazard to a bounded, metric-neutral corrective action while maintaining near-goal stability, with zero clamp events in both scenarios, whereas CoT prompting increases from 9.7 to 14.0 events.
Yousef Emami, MohammadHossein Homaei, Hao Zhou et al.· 0 citations
This work provides a feasible technical pathway and reproducible evaluation benchmark for the collaborative deployment of lightweight LLM planner, sub-goal guidance, sensor observations, cooperative reward, and reward shaping components and quantifies the indispensability of the LLM planner.
Yuting Cao, Zheng Zhao, Jiekai Wu et al.· Journal of King Saud Univers...· 0 citations
Simulation results demonstrate that the proposed Hierarchical LLM-driven control framework significantly reduces collision rates and improves aggregate system throughput compared to existing baselines.
Zijiang Yan, Hao Zhou, W. Jaafar et al.· arXiv.org· 0 citations
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