Abstract: Methods and systems for testing robotic systems in an environment blending both physical and virtual test environments are presented herein. A realistic, three dimensional physical environment for testing and evaluating a robotic system is augmented with simulated, virtual elements. In this manner, robotic systems, humans, and other machines dynamically interact with both real and virtual elements. In one aspect, a model of a physical test environment and a model of a virtual test environment are combined, and signals indicative of a state of the combined model are employed to control a robotic system. In a further aspect, a mobile robot present in a physical test environment is commanded to emulate movements of a virtual robot under control. In another further aspect, images of the virtual robot under control are projected onto the physical test environment to provide a visual representation of the presence and action taken by the virtual robot.

Abstract: Methods and systems for automatically aligning an overhead hoist transport (OHT) vehicle with a load port of a wafer fabrication tool during operation and at the installation phase of the equipment in a semiconductor fabrication facility are described herein. In one aspect, an alignment frame including a digital image capture device is mounted to a wafer-in-progress (WIP) carrying pod. An image captured by the image capture device is analyzed to determine a positioning error of the OHT vehicle relative to the alignment frame based on the location of the OHT vehicle within the image. In another aspect, an inertial measurement device is coupled to a gripper assembly of an OHT vehicle. Inertial measurement signals are collected when the gripper assembly docks with a WIP carrying pod. The inertial measurement signals are analyzed to determine an initial positioning error of the gripper assembly with respect to the WIP carrying pod.

Abstract: Methods and systems for joint execution of complex tasks by a human and a robotic system are described herein. In one aspect, a collaborative robotic system includes a payload platform having a loading surface configured to carry a payload shared with a human collaborator. The collaborative robotic system navigates a crowded environment, while sharing a payload with the human collaborator. In another aspect, the collaborative robotic system measures forces in a plane parallel to the loading surface of the payload platform to infer navigational cues from the human collaborator. In some instances, the collaborative robotic system overrides the navigational cues of the human collaborator to avoid collisions between an object in the environment and any of the robotic system, the human collaborator, and the shared payload.

Abstract: A system includes a machine tool 10, a robot 25 having a camera 31, and a transfer device 35 having the robot 25 mounted thereon, and an identification figure is arranged in a machining area of the machine tool 10.

Abstract: A system includes a machine tool 10, a robot 25 having a camera 31, and a transportation device 35 having the robot 25 mounted thereon, and an identification figure is arranged in a machining area of the machine tool 10.

Abstract: Methods and systems for collaboration between two robotic vehicle systems to accurately determine a geometric model of the footprint of a loaded robotic vehicle are described herein. A scanning robot is employed to scan a robotic vehicle loaded with a payload. The scanning robot measures the geometric information required to determine a geometric model of the loaded robotic vehicle. The scanning robot traverses a trajectory around the payload robot, while one or more distance sensors repeatedly measure the distance between the scanning robot and the payload robot and one or more image capture devices repeatedly image the payload robot. A geometric model of the footprint of the payload robot is generated based on the collected image and distance information. In some examples, virtual boundaries are defined around the payload robot based on the geometric model to navigate with obstacle avoidance.

Abstract: Methods and systems for testing robotic systems in an environment blending both physical and virtual test environments are presented herein. A realistic, three dimensional physical environment for testing and evaluating a robotic system is augmented with simulated, virtual elements. In this manner, robotic systems, humans, and other machines dynamically interact with both real and virtual elements. In one aspect, a model of a physical test environment and a model of a virtual test environment are combined, and signals indicative of a state of the combined model are employed to control a robotic system. In a further aspect, a mobile robot present in a physical test environment is commanded to emulate movements of a virtual robot under control. In another further aspect, images of the virtual robot under control are projected onto the physical test environment to provide a visual representation of the presence and action taken by the virtual robot.

Abstract: Methods and systems for collaboration between two robotic vehicle systems to accurately determine a geometric model of the footprint of a loaded robotic vehicle are described herein. A scanning robot is employed to scan a robotic vehicle loaded with a payload. The scanning robot measures the geometric information required to determine a geometric model of the loaded robotic vehicle. The scanning robot traverses a trajectory around the payload robot, while one or more distance sensors repeatedly measure the distance between the scanning robot and the payload robot and one or more image capture devices repeatedly image the payload robot. A geometric model of it the footprint of the payload robot is generated based on the collected image and distance information. In some examples, virtual boundaries are defined around the payload robot based on the geometric model to navigate with obstacle avoidance.

Abstract: Methods and systems for joint execution of complex tasks by a human and a robotic system are described herein. In one aspect, a collaborative robotic system includes a payload platform having a loading surface configured to carry a payload shared with a human collaborator. The collaborative robotic system navigates a crowded environment, while sharing a payload with the human collaborator. In another aspect, the collaborative robotic system measures forces in a plane parallel to the loading surface of the payload platform to infer navigational cues from the human collaborator. In some instances, the collaborative robotic system overrides the navigational cues of the human collaborator to avoid collisions between an object in the environment and any of the robotic system, the human collaborator, and the shared payload.

Abstract: Methods and systems for navigating a mobile robot through a crowded pedestrian environment based on a trained navigation model are described herein. The trained navigation model receives measurement data identifying nearby pedestrians and the velocity of each nearby pedestrian relative to the mobile robot, and the current position and desired endpoint position of the mobile robot in the crowded pedestrian environment. Based on this information, the trained navigation model generates command signals that cause the mobile robot to adjust its velocity. By repeatedly sampling the velocities of surrounding pedestrians and current location, the navigation model directs the mobile robot toward the endpoint location with a minimum of disruption to the pedestrian traffic flows. The navigation model is trained to emulate desirable pedestrian walking behaviors in a crowded pedestrian environment by tracking the movement of a behavioral trainer through a crowded pedestrian environment.

Abstract: Methods and systems for navigating a mobile robot through a crowded pedestrian environment based on a trained navigation model are described herein. The trained navigation model receives measurement data identifying nearby pedestrians and the velocity of each nearby pedestrian relative to the mobile robot, and the current position and desired endpoint position of the mobile robot in the crowded pedestrian environment. Based on this information, the trained navigation model generates command signals that cause the mobile robot to adjust its velocity. By repeatedly sampling the velocities of surrounding pedestrians and current location, the navigation model directs the mobile robot toward the endpoint location with a minimum of disruption to the pedestrian traffic flows. The navigation model is trained to emulate desirable pedestrian walking behaviors in a crowded pedestrian environment by tracking the movement of a behavioral trainer through a crowded pedestrian environment.