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ENTITY automated guided vehicle

automated guided vehicle

PulseAugur coverage of automated guided vehicle — every cluster mentioning automated guided vehicle across labs, papers, and developer communities, ranked by signal.

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RECENT · PAGE 1/1 · 6 TOTAL
  1. SIGNIFICANT · CL_214922 ·

    Galileo Ai launches Galileo X, unifying AGV, off-road, and quadruped robot capabilities

    Galileo Ai has unveiled its new Galileo X, a "terrestrial bipedal mobility system" designed to integrate the capabilities of AGVs, off-road vehicles, and quadruped robots into a single platform. This novel approach aims…

  2. SIGNIFICANT · CL_214212 ·

    Galileo Robotics launches all-domain mobile robot platform Galileo X

    Galileo Robotics has unveiled its new "all-domain locomotion embodiment system" with the Galileo X platform at the 2026 World Robot Conference. This innovative system integrates high-precision indoor transport, long-dis…

  3. SIGNIFICANT · CL_204455 ·

    RoboScience unveils REX G1 embodied robot for real-world productivity

    RoboScience has launched its first general-purpose mobile manipulator robot, the REX G1, designed to act as a "next-generation embodied intelligence productivity partner." This robot integrates mobility and manipulation…

  4. TOOL · CL_193391 ·

    LLM framework enhances simulation optimization for AGV scheduling

    Researchers have developed a new framework for designing heuristics in simulation-based optimization, utilizing Large Language Models (LLMs) to analyze simulation traces and suggest code-level improvements. This method …

  5. RESEARCH · CL_93097 ·

    New distillation method enhances AI for vehicle collision avoidance

    Researchers have developed an instance-aware knowledge distillation framework to improve semi-supervised learning for collision avoidance systems. This method generates pseudo-labels by combining domain priors from a te…

  6. RESEARCH · CL_06962 ·

    AI research analyzes coordination gap in job-shop scheduling training methods

    A new paper analyzes the trade-offs between joint and modular training for multi-agent reinforcement learning in job-shop scheduling with transportation resources. The research quantifies the "coordination gap" between …