Disclosed is a mobile robot configured to cut lawn in a work area. The mobile robot may include a main body, a weight sensing sensor, an obstacle sensing sensor, a blade, and a processor. The mobile robot may execute an artificial intelligence (AI) algorithm and/or a machine learning algorithm, and perform communication with other electronic devices in a 5G communication environment. As a result, it is possible to enhance user convenience.

A method, apparatus and device for dynamically acquiring the load of a vehicle, and a storage medium are disclosed. The method includes: acquiring deformation data of load-bearing deformation of a wheel hub of the vehicle, and acquiring the load of the vehicle according to the deformation data.

A modular pavement slab comprises a body, a strain sensor array, and a sensor processor. The body includes a top surface, a bottom surface, and four side surfaces. The modular pavement slab is configured to be coupled to at least one other modular pavement slab via connectors along at least one of the side surfaces. The strain sensor array is retained within the body and is configured to detect a plurality of strains on the body resulting from vehicular traffic across the top surface of the body. The sensor processor is in communication with the strain sensor array. The sensor processor is configured to communicate input signals to the strain sensor array, receive output signals from the strain sensor array, and determine a plurality of time-varying strain values, each strain value indicating a strain experienced over time by a successive one of a plurality of regions of the body.

A power distribution and vehicle self-learning-based truck overload identification method, comprising: acquiring load identification data of a vehicle; calculating AOP values and STP values according to the load identification data of the vehicle; according to a plurality of sets of AOP values and STP values in a standard full-load state, constructing a power distribution curve of the vehicle in the standard full-load state; and comparing an AOP value during an actual operation process to a corresponding AOP value in the power distribution curve in the standard full-load state, and according to a comparison result, identifying whether the vehicle is overloaded. The method can show the operating states of overloaded vehicles in the road network in real time to provide convenience for oversize and overloading management. Loads of vehicles operating in the road network can be monitored in real time without additional equipment, thus improving the scope of overload identification.

The invention relates to a method for calculating the weight of a load moving on scales (1). According to the method, a load signal of the scales is determined over a period of time using the speed of the load, and several partial load signals (TL.sub.1, TL.sub.2) are used, the total thereof providing the load signal, a first partial load signal (TL.sub.1) displaying a maximum value as long as the load is fully on the weighing section of the scales (1), and a second partial load signal (TL.sub.2) displaying a minimum value as long as the load is completely removed from the weighing section of the scales (1), and the speed of the movement of the load is determined from said partial load signals (TL.sub.1 and TL.sub.2). The invention also relates to scales for carrying out said method, comprising two weighing units (10, 11) with flexible deformation elements on which deformation sensors (7, 15), which generate the partial load signals (TL.sub.1,TL.sub.2), are arranged.

A towing configuration of a vehicle coupled to a trailer may be evaluated by rolling a tire of the coupled vehicle and trailer over a compressible region of a pressure vessel filled with a hydraulic fluid. A peak pressure of the hydraulic fluid as the tire rolls over the compressible region of the pressure vessel may be determined. The tire load may be determined based upon the peak pressure. Based at least in part on the tire load, a determination may be made whether the towing configuration satisfies a predetermined set of criteria.

The present disclosure relates to a method for computing a vehicle mass, a terminal device and a storage medium. The method includes: collecting engine torque data and electronic horizon data; determining whether two sampling points whose gradient value difference is greater than a gradient value difference threshold exist; determining whether the two sampling points are on a same road; determining whether the road between the two sampling points is a straight road; determining whether a difference between engine torques is greater than a torque difference threshold; calculating the vehicle mass according to the engine torques and gradient values corresponding to the two sampling points.

A method for calibrating a WIM (Weigh in Motion) sensor built into a road during travel of a calibrating vehicle measures the dynamic wheel force on the road and on the WIM sensor directly at the measuring wheel, depending on time or location. These wheel force data are transmitted to an evaluating unit. As the calibrating vehicle passes over, WIM signal data are simultaneously measured at the WIM sensor and transmitted to the evaluating unit. The wheel force data are synchronized with the WIM signal data in the evaluating unit. A calibration function is determined by comparing the dynamic wheel force data with the WIM signal data.

A device for weighing aircraft includes a weighing platform configured to receive a undercarriage leg of the aircraft and to generate weighing signals, a first calculation unit configured to calculate weighing information from the weighing signals generated by the weighing platform, a communication unit configured to transmit to a central device and to receive signals including at least one signal representing the weighing information calculated by the calculation unit, and a ground rolling unit configured to move the weighing platform over a surface.

A modular pavement slab comprises a body, a strain sensor array, and a sensor processor. The body includes a top surface, a bottom surface, and four side surfaces. The modular pavement slab is configured to be coupled to at least one other modular pavement slab via connectors along at least one of the side surfaces. The strain sensor array is retained within the body and is configured to detect a plurality of strains on the body resulting from vehicular traffic across the top surface of the body. The sensor processor is in communication with the strain sensor array. The sensor processor is configured to communicate input signals to the strain sensor array, receive output signals from the strain sensor array, and determine a plurality of time-varying strain values, each strain value indicating a strain experienced over time by a successive one of a plurality of regions of the body.