This application provides a fleet control method and apparatus, an electronic device, and a storage medium. The fleet control method is used for controlling a robot fleet and includes: determining a planned path of each robot in the robot fleet, where the planned path of each robot is used to indicate a movement path for the robot to move to a corresponding target storage location within a shelving unit region to execute a task; determining a following road segment in the planned path of each following robot based on the planned path of each robot, where the following road segment includes a road segment located on the ground and/or a road segment extending in a vertical direction; and sending the following road segment to a corresponding following robot.

Aspects of the disclosure relate to a robot. The robot includes a body and a wheel assembly coupled to the body. The wheel assembly includes a central hub and a central gear coupled to the central hub. A plurality of legs are coupled to the central hub. The plurality of legs are operatively coupled to the central gear such that the central gear drives the plurality of legs between a closed position and an open position. A motor is coupled to the body and coupled to the wheel. A suspension system is coupled to the wheel assembly. An autonomous guidance system is coupled to the motor.

The application discloses a method, system, device, and storage medium for determining a cleaning path. The method includes: in response to a cleaning instruction from a cleaning robot, controlling the cleaning robot to rotate a preset angle on the surface to be cleaned based on a target rotation direction; detecting whether the cleaning robot generates a first edge corner trigger signal during its rotation; and determining a working path of the cleaning robot on a surface to be cleaned based on the detecting result of the first edge corner trigger signal. This application automatically determines the working path based on the detecting result of the first edge corner trigger signal, which, compared to existing technologies that rely on manually selecting the working path, ensures the adaptability of the working path to the surface to be cleaned.

Disclosed herein is a method of detecting stalled state of a dynamic pool equipment unit, comprising receiving a plurality of movement features relating to a dynamic pool equipment unit deployed in a water pool which are captured during a predefined sampling window and comprise (1) motion features of the pool equipment unit, and (2) operational features of electric motor(s) of the pool equipment unit, determining a movement pattern of the pool equipment unit using one or more statistical models applied to the plurality of movement features which are trained to estimate a stalled state of the pool equipment unit in which the pool equipment unit is pitched up and unable to advance on a slopped obstacle in the water pool, and causing the pool equipment unit to stop attempted advance in a current direction responsive to determining that the pool equipment unit is in the stalled state.

The present disclosure provides a method and apparatus for return control of a swimming pool cleaning robot, and an electronic device and a computer storage medium thereof. The method includes: in response to a trigger of a return instruction, acquiring a current position of the swimming pool cleaning robot in a map for a swimming pool; and generating a return path according to a reachable block in the map for the swimming pool, a predetermined return position, and the current position, and controlling the swimming pool cleaning robot to return from the current position to the predetermined return position on the basis of the return path. Therefore, the swimming pool cleaning robot is controlled to automatically return to a designated position of the swimming pool, such that use smartness of the swimming pool cleaning robot is improved, and use experience of a user is enhanced.

Disclosed herein is a method of detecting stalled state of a dynamic pool equipment unit, comprising receiving a plurality of movement features relating to a dynamic pool equipment unit deployed in a water pool which are captured during a predefined sampling window and comprise (1) motion features of the pool equipment unit, and (2) operational features of electric motor(s) of the pool equipment unit, determining a movement pattern of the pool equipment unit using one or more statistical models applied to the plurality of movement features which are trained to estimate a stalled state of the pool equipment unit in which the pool equipment unit is pitched up and unable to advance on a slopped obstacle in the water pool, and causing the pool equipment unit to stop attempted advance in a current direction responsive to determining that the pool equipment unit is in the stalled state.

An apparatus and method for controlling driving of a moving object that climbs up or down stairs comprises a tilt sensor configured to sense a slope of the moving object in a pitch direction, and a processor configured to reduce a speed of the moving object in a specific section while the moving object climbs up or down the stairs, based on the sensed slope.

Disclosed is a system for managing loads in a storage facility. The system comprises: a racking structure configured to store loads; mobile robot assembly(ies) (MRA(s)) operable to traverse within a storage facility, wherein MRA(s) comprises a mobile robot and a docking arrangement; and pick and drop robots (PDRs) operable to traverse along a height of storage facility for picking and dropping loads from racking structure, wherein PDRs comprise a latch arrangement and climb arrangement. Herein, MRA is operable to carry a PDR from amongst PDRs, PDR being operatively mounted on docking arrangement of MRA, wherein when MRA is at a first predefined distance from racking structure, PDR engages itself to racking structure via latch arrangement, and climb arrangement is configured to extend or retract vertically along a length of racking structure, for picking and dropping loads from racking structure upon engagement with racking structure.

Methods and systems are provided for controlling wheel-legged quadrupedal robots using pose optimization and force control according to quadratic programming (QP) are disclosed. An example robotic system leverages the whole-body motion and the wheel actuation to roll over high obstacles while keeping the wheel torques to navigate the terrain. Wheel traction and balancing is employed for the robot body. Linear rigid body dynamics with wheels are used for real-time balancing control of wheel-legged robots. Further, an effective pose optimization method is implemented for locomotion over steep ramp and stair terrains. The pose optimization solves for optimal poses to enhance stability and enforce collision-fee constraints for the rolling motion over stair terrain.

A mobile robot with a locomotion mechanism is described which is adapted to traverse vertical obstacles. The mobile robot includes a frame structure and a wheel assembly including at least two back wheels, at least two middle wheels, and at least two front wheels. The mobile robot further includes a first and a second front rocker arms arranged on opposing sides of the frame structure each coupled to the frame structure. Each of the first and second front rocker arms is pivotable around a lever bearing located between respective axial centers of rotation of wheel pairs.