The present disclosure discloses a dispatching method and device, and a non-transitory readable storage medium, and relates to the field of computer technology. The method of the present disclosure includes: obtaining path condition information within a warehouse; calculating pickup time of each candidate robot of a plurality of candidate robots according to a location of the candidate robot and the path condition information; dispatching a target robot to perform a pickup task according to the pickup time of each candidate robot.

Systems, methods and apparatus for automated vehicle wheel removal and replacement are provided. One system includes a computer system with applications for scheduling the replacement of tires for the vehicle. An electronically controlled lift device and robotic apparatus is configured for interaction with the computer system. The lift device mechanically adjusts arms for placement on lift points of vehicles. The robotic apparatus detects positioning of lug nut configuration for a wheel, removes lug nuts, and then removes the wheel from the wheel hub with gripping arms. The wheel and tire are then handed off to a separate tire changing machine. When a new tire is replaced the robotic apparatus then mounts the wheel to the original wheel hub, and then secures the lug nuts to the lug nut bolts.

A robotic system for arranging packages at a destination in a specified arrangement. The robotic system processes incoming packages, stores the packages in a temporary storage area, executes a simulate function to generate or update a simulated stacking plan, determines the occurrence of a palletizing trigger, and places the packages on the pallet according to the simulated stacking plan upon determining the occurrence of the palletizing trigger. The palletizing trigger can be one of a time limit trigger, a uniform layer trigger, a storage capacity trigger, or receiving a placement initiation command.

Conventional tele-presence robots have their own limitations with respect to task execution, information processing and management. Embodiments of the present disclosure provide a tele-presence robot (TPR) that communicates with a master device associated with a user via an edge device for task execution wherein control command from the master device is parsed for determining instructions set and task type for execution. Based on this determination, the TPR queries for information across storage devices until a response is obtained enough to execute task. The task upon execution is validated with the master device and user. Knowledge acquired, during querying, task execution and validation of the executed task, is dynamically partitioned by the TPR across storage devices namely, on-board memory of the tele-present robot, an edge device, a cloud and a web interface respectively depending upon the task type, operating environment of the tele-presence robot, and other performance affecting parameters.

A control device 1C mainly includes an operation sequence generation means 16C and a synchronization management means 17C. The operation sequence generation means 16C is configured to generate, based on an operation prediction result R2a of another working body which performs cooperative work with a robot which executes a task, an operation sequence Sra to be executed by the robot. The synchronization management means 17C is configured to synchronize an operation executed by the robot during execution of the operation sequence Sra and an operation executed by the other working body.

Disclosed herein is a method implemented by a task mining engine. The task mining engine is stored as processor executable code on a memory. The processor executable code is executed by a processor that is communicatively coupled to the memory. The method includes receiving recorded tasks identifying user activity with respect to a computing environment and clustering the recorded user tasks into steps by processing and scoring each recorded user task. The method also includes extracting step sequences that identify similar combinations or repeated combinations of the steps to mimic the user activity.

Methods, systems, and devices for managing robot resources are described. A robot receives from an application a request to reserve a particular set of physical resources of the robot. The robot then determines that each of the physical resources in the set are available to the application and, based on the determination, allocates exclusive use of the particular set of resources to the application by (i) generating a token corresponding to the set of resources, (ii) providing the token to the application, and (iii) updating token data that associates the token with the set of resources. The robot then controls access to the particular set of resources such that, while token data indicates that the token is valid, commands from applications that involve the set of resources are only executed when provided with the token corresponding to the allocation of access to the particular set of resources.

Systems and methods for performing a task in an operation environment of an aerial device with an autonomous or semi-autonomous robot are described. In some embodiments, a robot is disposed at an end of a boom of an aerial device. The robot may comprise cameras, actuators, sensors, processors, and manipulators that work together to perform tasks fully autonomously or semi-autonomously. Furthermore, the robot may comprise tools for performing the tasks and computer-executable instructions for performing the tasks may be based on the various sensory inputs, the tools, and the tasks to be performed.

Existing cognitive robotic applications follow a practice of building specific applications for specific use cases. However, the knowledge of the world and the semantics are common for a robot for multiple tasks. In this disclosure, to enable usage of knowledge across multiple scenarios, a method and system for ontology guided indoor scene understanding for cognitive robotic tasks is described where in scenes are processed based on techniques filtered based on querying ontology with relevant objects in perceived scene to generate a semantically rich scene graph. Herein, an initially manually created ontology is updated and refined in online fashion using external knowledge-base, human robot interaction and perceived information. This knowledge helps in semantic navigation, aids in speech, and text based human robot interactions.

A control system, method and computer program product cooperate to assist control for an autonomous robot. An interface receives recognition information from an autonomous robot, said recognition information including candidate target objects to interact with the autonomous robot. A display control unit causes a display image to be displayed on a display of candidate target objects, wherein at least two of the candidate target objects are displayed with an associated indication of a target object score.