A robotic trolley collection system and methods for automatically collecting baggage/luggage trolleys are provided. The system includes a differential-driven mobile base; a manipulator mounted on the differential-driven mobile base for forking a trolley, having a structure same as a head portion of the trolley; a sensory and measurement assembly for providing sensing and measurement dataflow; and a main processing case for processing the sensing and measurement dataflow provided by the sensory and measurement assembly and for controlling the differential-driven mobile base, the manipulator, and the sensory and measurement assembly. The method includes localizing and mapping the robotic trolley collection system; detecting an idle trolley to be collected and estimating pose of the idle trolley; visually servoing control of the robotic trolley collection system; and issuing motion control commands to the robotic trolley collection system for automatically collecting the idle trolley.

Methods are described that perform a dispatched consumer-to-store return or swap logistics operation for an item being replaced using a modular autonomous bot apparatus assembly and a dispatch server. The method begins with receiving a return operation dispatch command that includes identifier information, transport parameters, and designated pickup information for the item being replaced/returned, along with authentication information related to an authorized supplier of the item being replaced. Modular components of the bot apparatus are verified to be compatible with the dispatched logistics operation. The MAM then autonomously causes the bot apparatus to move to the designated pickup location, notifies the authorized supplier of an approaching pickup, receives supplier authorization input to permissively allow access to a payload area within the bot apparatus, monitors loading as the item being replaced is received along with return documentation, and then autonomously causes movement of the bot apparatus back to the origin location.

Stabilization mechanisms may include at least one gripper mounted to a powerline-crawling robot, which may be configured to grasp a powerline supporting the powerline-crawling robot. At least one controller may be configured to control a lateral position of the at least one gripper. At least one inertial measurement unit may be configured to sense at least one of lateral movement and axial rotation of the powerline-crawling robot. The controller may control the lateral position of the gripper based on data from the inertial measurement unit. Various other related systems, devices, mechanisms, and methods are also disclosed.

An automatic method for autonomous interactions between robots, comprising an action of automatically receiving, by a transport robot, a request for transporting a service robot. The method comprises an action of automatically computing a location of the service robot. The method comprises an action of automatically moving the transport robot to the location of the service robot. The method comprises an action of automatically sending a signal from the service robot to the transport robot using a signal emitter incorporated into a mechanical element attached to the service robot. The method comprises an action of automatically coupling, using the signal, the mechanical element to a carrier element attached to the transport robot.

A robot system includes: a conveying device configured to convey a workpiece; a robot configured to execute an operation on the workpiece; and circuitry configured to: identify a current position of the workpiece and an object area occupied by an object; identify an interlock area that moves with the current position of the workpiece being conveyed by the conveying device; check an overlap between the interlock area and the object area; and control the robot to execute the operation based on the current position of the workpiece in response to determining that the interlock area does not overlap the object area.

A method of operating a robot performing a task when receiving instructions to discontinue the task and perform an additional task. Having performed the additional task, the robot will revert to the position of performing the first task and continue the first task.

A robot control system according to the present embodiment includes a plurality of mobile robots that moves autonomously in a facility and a control device that controls the mobile robots. When the control device detects that two or more of the mobile robots are in a recovery requiring state, the control device notifies that the two or more mobile robots are in the recovery requiring state, together with priorities of the two or more mobile robots.

A mobile robotic device includes a mobile base and a mast fixed relative to the mobile base. The mast includes a carved-out portion. The mobile robotic device further includes a three-dimensional (3D) lidar sensor mounted in the carved-out portion of the mast and fixed relative to the mast such that a vertical field of view of the 3D lidar sensor is angled downward toward an are in front of the mobile robotic device.

A frame body is provided parallel to and proximate with a surface of a structure and extends substantially horizontally from a first side to a second side. A connecting portion is provided to be attached to a cable to provide for vertical movement of the frame body. A robotic arm is affixed proximate to a bottom of the frame body and is able to move horizontally during penetrative imaging of the surface. Moreover, the robotic arm extends to an end proximate with the surface, and a penetrative imaging portion is attached to the robotic arm near the end proximate with the surface. The robotic arm rotates, vertically moving the penetrative imaging portion during penetrative imaging of the surface. In addition, the penetrative imaging portion can be separately rotated about three orthogonal axes of rotation (yaw, pitch, roll) to achieve various angles of approach and orientation to the surface.

A method includes determining, for a robotic device that comprises a perception system, a robot planner state representing at least one future path for the robotic device in an environment. The method also includes determining a perception system trajectory by inputting at least the robot planner state into a machine learning model trained based on training data comprising at least a plurality of robot planner states corresponding to a plurality of operator-directed perception system trajectories. The method further includes controlling, by the robotic device, the perception system to move through the determined perception system trajectory.