Skylla Technologies, Inc.

Dmg Mori Company Limited

Movensys Corporation

East Japan Railway Company

Boston Incubator Center, LLC

Kota Weaver

Haruhiko Harry Asada

Hideki Nagasue

Yuta Oba

Masaaki Nakagawa

Tsutomu Sakurai

Anand Parwal

Sheng Liu

Hideaki Tanaka

Jun Maruyama

Baldin Lloren-Bonilla

A system includes a machine tool, a robothaving a camera, and a transportation devicehaving the robotmounted thereon, and an identification figure is arranged in a machining area of the machine tool

Image processing method, image processing apparatus, robot-mounted transfer device, and system

A system includes a machine tool, a robothaving a camera, and a transfer devicehaving the robotmounted thereon, and an identification figure is arranged in a machining area of the machine tool

Robot-mounted moving device, system, and machine tool

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.

Methods and systems for testing robotic systems in an integrated physical and simulated environment

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.

Collaborative task execution with humans and robotic vehicles

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.

Collaborative determination of a load footprint of a robotic vehicle

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.

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Allston, MA, United States of America

Kota Weaver