An automatic guided vehicle assembly includes a non-powered cart having a shelf unit configured to receive parts or tools, and an automated guided vehicle configured to attach and detach from the non-powered cart and configured to perform autonomous driving, wherein the automated guided vehicle includes a fastening pin configured to move upwardly and downwardly to attach to or detach the automated guided vehicle from the non-powered cart, wherein the non-powered cart includes a fastening unit configured to attach to and detach from the automated guided vehicle, and wherein the fastening unit includes a fastening member having a fastening guide configured to guide coupling of the fastening pinto the fastening unit.

The control device acquires information regarding an event held within a certain area, and based on the acquired information, the power supply has the function of supplying power to electronic devices used by the user from among multiple locations within the area. The control unit includes a control unit that determines a location to dispatch the mobility and performs processing to dispatch the power supply mobility to the determined location.

The control device acquires information regarding an event held within a certain area, and based on the acquired information, the power supply has the function of supplying power to electronic devices used by the user from among multiple locations within the area. The control unit includes a control unit that determines a location to dispatch the mobility and performs processing to dispatch the power supply mobility to the determined location.

The present disclosure discloses a robot system and method for monitoring farmland nitrogen leaching. The robot system includes a cloud platform, a monitoring robot, and a leachate collection module. The cloud platform is configured to send information about a to-be-measured site and a to-be-measured depth to the monitoring robot and receive measurement information of the monitoring robot; and the monitoring robot is configured to move to the to-be-measured site to be connected to the leachate collection module at the to-be-measured depth of the to-be-measured site, extract a leachate of the leachate collection module, perform leachate nitrogen detection on the leachate, and send a leachate nitrogen detection result to the cloud platform.

The present disclosure discloses a robot system and method for monitoring farmland nitrogen leaching. The robot system includes a cloud platform, a monitoring robot, and a leachate collection module. The cloud platform is configured to send information about a to-be-measured site and a to-be-measured depth to the monitoring robot and receive measurement information of the monitoring robot; and the monitoring robot is configured to move to the to-be-measured site to be connected to the leachate collection module at the to-be-measured depth of the to-be-measured site, extract a leachate of the leachate collection module, perform leachate nitrogen detection on the leachate, and send a leachate nitrogen detection result to the cloud platform.

A robot for clearing snow has a main body; a traction arrangement enabling motion of the robot on snow lying on a roof; a snow-removal device; and at least one depth sensor. A processing and control unit controls at least one motor to direct the robot to follow a snow-clearing path; receives a depth signal from the depth sensor(s); and, as a function of the depth signal, actively controls a snow-clearing depth by actuating the snow-removal device and, as the robot follows the snow-clearing path, adjusting a vertical snow-clearing distance so as to leave a layer of snow of a pre-determined depth on the roof after clearing and to prevent contact by the snow-removal device with a surface of the roof. During snow removal, no part of the snow-removal device contacts the roof surface other than at most the traction arrangement.

A robot for clearing snow has a main body; a traction arrangement enabling motion of the robot on snow lying on a roof; a snow-removal device; and at least one depth sensor. A processing and control unit controls at least one motor to direct the robot to follow a snow-clearing path; receives a depth signal from the depth sensor(s); and, as a function of the depth signal, actively controls a snow-clearing depth by actuating the snow-removal device and, as the robot follows the snow-clearing path, adjusting a vertical snow-clearing distance so as to leave a layer of snow of a pre-determined depth on the roof after clearing and to prevent contact by the snow-removal device with a surface of the roof. During snow removal, no part of the snow-removal device contacts the roof surface other than at most the traction arrangement.

The embodiments of present disclosure herein address unresolved problem of cognitive navigation strategies for a telepresence robotic system. This includes giving instruction remotely over network to go to a point in an indoor space, to go an area, to go to an object. Also, human robot interaction to give and understand interaction is not integrated in a common telepresence framework. The embodiments herein provide a telepresence robotic system empowered with a smart navigation which is based on in situ intelligent visual semantic mapping of the live scene captured by a robot. It further presents an edge-centric software architecture of a teledrive comprising a speech recognition based HRI, a navigation module and a real-time WebRTC based communication framework that holds the entire telepresence robotic system together. Additionally, the disclosure provides a robot independent API calls via device driver ROS, making the offering hardware independent and capable of running in any robot.

In accordance with an embodiment, a method of operating a vehicle within a vehicle swarm includes: receiving a sortie specification, the sortie specification specifying a desired behavior the vehicle swarm is to perform; obtaining a position identification within the vehicle swarm; and calculating a set of waypoints based on the received sortie specification and the position identification.

A monitoring and emergency control system for vector psyllid of citrus huanglongbing (HLB) is disclosed. The system includes a video acquisition module, an image recognition module, a control console, an early warning module and an unmanned aerial vehicle (UAV)-based flight prevention module. The video acquisition module acquires images by using 360-degree dead-angle-free cameras, the cameras are arranged at a plurality of points at a periphery and interior of an orchard, and each camera is numbered. The video acquisition module acquires real-time images, and transmits the real-time images to the image recognition module in real time for recognition and determination. The control console determines whether to send out warning information to an orchard manager and a flight prevention instruction according to a feedback result. The UAV-based flight prevention module receives the instruction, and then carries a pesticide box to take off to a region to kill the psyllid by applying pesticides.