An advanced system of cooperating solar module carrier robots for installing solar panels on a solar tracker is provided. The advanced system can include a computer vision system designed to route the cooperating solar module carrier robots to a solar tracker, a deck sized to fit one or more pallets of solar panels, and a robotic arm with a suction cup tool designed to pick up and hold a solar panel.

An aspect of the present disclosure relates to automated imaging of photovoltaic devices using an aerial vehicle (20). In one aspect, there is a method (440) for automated imaging of a PV array (310) using an aerial vehicle (20), the PV array (310) corresponding to target points (350) for the aerial vehicle (20). The method (440) comprises: positioning the aerial vehicle (20) at one of the target points (350) corresponding to the PV array (310); and controlling the aerial vehicle (20) for automated manoeuvre between the target points (350) to capture visual datasets of the PV array (310). The automated manoeuvre comprises: aligning a field-of-view (225) of a camera (222) of the aerial vehicle (20) to a PV array subsection of the PV array (310); determining a scanning direction (360) for moving the aerial vehicle (20) between the target points (350); and capturing, using the camera (222), the visual datasets of the PV array (310) starting from the PV array subsection as the aerial vehicle (20) moves along the scanning direction (360) between the target points (350).

A system is provided for controlling a grain cart relative to a grain truck. The grain truck includes a side edge extending between a front end and a rear end. The system comprises a ranging device and a controller. The ranging device is configured to determine a position and orientation of the side edge relative to the grain cart. The controller is configured to determine a path line parallel to the side edge, wherein the path line is a predetermined distance from the side edge, identify a goal point based on the path line, where the goal point a second predetermined distance from the front or rear end of the side edge, and plan a path for the grain cart to the goal point.

A system is provided for controlling a grain cart relative to a grain truck. The grain cart includes a grain tank and an unload auger configured to transfer crop material out of the grain tank. The grain truck includes a truck box extending from a first end to a second end. The truck box includes a top edge extending around the top of the truck box. The system comprises a ranging device and a controller. The ranging device is configured to identify a distance to the top edge of the truck box and identify a distance to an area in the truck box. The controller is configured to determine a position of the grain cart relative to the grain truck, and determine whether the grain cart is positioned near the first end of the truck box. If the controller determines that the grain cart is positioned near the first end of the truck box, the controller is configured to determine a fill level in the area based on the distance to the top edge of the truck box and the distance to the area in the truck box, determine whether the fill level exceeds a threshold, and if the controller determines that the fill level does not exceed the threshold, the controller is configured to start the unload auger.

A method includes: receiving, at a mobile robot from a central server, a rescue command including a rescue location corresponding to an incapacitated mobile robot; controlling a locomotive assembly of the mobile robot to travel towards the rescue location; capturing, using a sensor of the mobile robot, sensor data representing the rescue location; at the mobile robot, identifying the incapacitated mobile robot from the sensor data; controlling the locomotive assembly to position the mobile robot in a predetermined pose relative to the incapacitated robot; and controlling a charging interface of the mobile robot to transfer energy from a battery of the mobile robot to a battery of the incapacitated mobile robot.

A robot includes: a plurality of wheels; a plurality of motors; at least one sensor; a memory configured to store first information on a size of the robot; and a processor. The processor is configured to: acquire image data of an escalator from the at least one sensor, acquire second information on a size of a plurality of steps included in the escalator based on the image data, based on the first information and the second information, identify both a boarding position available for the robot to board the escalator among the plurality of steps, and a posture of the robot configured to allow the robot to board at the boarding position, acquire control information for controlling the robot to board at the boarding position in the posture when the boarding position and the posture have been identified, and control the plurality of motors based on the control information.

A method implemented by a processor associated with a movable object includes receiving a first parameter value from a user interface communicatively coupled to the movable object; determining a horizontal acceleration based on the first parameter value, wherein the horizontal acceleration is substantially zero when the first parameter value is zero; and controlling the movable object to accelerate or decelerate in accordance with the determined horizontal acceleration.

Robotic applications are important in both indoor and outdoor environments. Establishing reliable end-to-end communication among robots in such environments are inevitable. Many real-time challenges in robotic communications are mainly due to the dynamic movement of robots, battery constraints, absence of Global Position System (GPS), etc. Systems and methods of the present disclosure provide assisted link prediction (ALP) protocol for communication between robots that resolves real-time challenges link ambiguity, prediction accuracy, improving Packet Reception Ratio (PRR) and reducing energy consumption in-terms of lesser retransmissions by computing link matrix between robots and determining status of a Collaborative Robotic based Link Prediction (CRLP) link prediction based on a comparison of link matrix value with a predefined covariance link matrix threshold. Based on determined status, robots either transmit or receive packet, and the predefined covariance link matrix threshold is dynamically updated. If the link to be predicted is unavailable, the system resolves ambiguity thereby enabling communication between robots.

This specification discloses a marker allocation method and apparatus in an unmanned aerial vehicle airport and an unmanned aerial vehicle landing method and apparatus. According to an airport shape and an airport size of an unmanned aerial vehicle airport and a standard shape and a standard size of a takeoff and landing point, a target layout of an unmanned aerial vehicle airport that includes multiple takeoff and landing points is determined. Further, an initial takeoff and landing point is determined from the multiple takeoff and landing points included in the target layout. Markers respectively allocated to the multiple takeoff and landing points are determined from a predetermined marker set that includes markers of different image content, by using the initial takeoff and landing point as a start point, according to a predetermined search algorithm, and with a constraint that similarity between a marker of any one of the multiple takeoff and landing points and markers of other takeoff and landing points in a specified neighborhood thereof is the lowest. In this method, a position and a correspondence between each takeoff and landing point and each marker in a range of the airport do not need to be manually determined, thereby improving efficiency of allocating a marker to an unmanned aerial vehicle.

A marine vessel management system, comprising: receiving input data comprising at least radar input data indicative of a first field of view and imagery input data indicative of a second field of view being at least partially overlapping with said first field of view. Processing the input data to determine data indicative of reflecting object(s) within an overlapping portion of said first field of view. Determining respective locations(s) within said second field of view, where said reflecting object(s) are identified, and obtaining radar meta-data of said reflecting object(s); processing said input imagery data said respective locations in an overlapping portion of said second field of view. Determining image data piece(s) corresponding with section(s) of said imagery data associated with said reflecting object(s). Using said radar meta-data for generating label data and generating output data comprising said image data section(s) and said label data.