arXiv AI
PAANI : On Device Visual Evidence Fusion and Explainable Guidance for River Robot Simulation
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arXiv AI
Sep 23
RiverVLN: Phase-Grounded Temporal Vision--Language Navigation for Unmanned Surface Vehicles
RiverVLN introduces the first benchmark for long‑horizon vision‑language navigation (VLN) of unmanned surface vehicles (USVs) in continuous riverine motion. The PGT‑NAV framework converts navigation instructions into an ordered sequence of visually verifiable semantic phases, maintaining an active phase online through grounded visual and motion evidence. This phase‑grounded approach reduces recursive position and heading drift, achieving a 0.79 success rate in Unity‑ROS closed‑loop tests and demonstrating transfer to real‑world USV deployment.
arXiv AI
Sep 24
Safe Multi-Robot Coordination via VLM-LLM Reasoning and Reachability Analysis
arXiv:2609.27816v1 Announce Type: cross Abstract: Safe coordination in heterogeneous machine-to-machine (M2M) robotic systems is challenging when robots differ in sensing capabilities, environmental...
arXiv AI
Jul 24
VoLN: Vision-Only Long-Horizon Navigation---Paradigm, Benchmark, and Method
arXiv:2607. 21400v1 Announce Type: cross Abstract: Vision-and-Language Navigation (VLN) enables embodied agents to follow natural-language instructions.
arXiv AI
Jul 10
Time-to-Collision Based Dynamic Obstacle Avoidance Using Pretrained Vision Models for Robots in Unstructured Environments
arXiv:2607. 07885v1 Announce Type: cross Abstract: Dynamic obstacle avoidance in unstructured outdoor environments remains a critical challenge for autonomous mobile robots, particularly when large-scale robot-specific training data and simulation-based policies are impractical.
arXiv AI
Aug 25
RACO: Reliability-Aware Coarse-Goal Optimization for Inspection-Oriented UAV Vision-Language Navigation
The paper introduces RACO, a reliability‑aware adaptive coarse‑to‑fine navigation framework for inspection‑oriented UAV vision‑language navigation. It treats the coarse goal as a runtime hypothesis, using object‑level anchors to correct localization before and at the transition to the fine stage, and applies scale‑adaptive terminal refinement for near‑miss cases. RACO is evaluated on the new LG‑UVI inspection setting and outperforms the HETT baseline by 9.53 and 7.98 percentage points on validation‑unseen and test‑unseen, respectively, while improving inspection‑region arrival and reducing false verification risk.
arXiv AI
Jun 18
EffiNav: Fusing Depth and Vision-Language for Efficient Object Goal Navigation
arXiv:2606. 18634v1 Announce Type: cross Abstract: To locate a target object while exploring the unknown environment is a fundamental capability for autonomous agents, with applications ranging from search-and-rescue to field robots.