Process evolution for robotic process automation (RPA) and RPA workflow micro-optimization are disclosed. Initially, an RPA implementation may be scientifically planned, potentially using artificial intelligence (AI). Embedded analytics may be used to measure, report, and align RPA operations with strategic business outcomes. RPA may then be implemented by deploying AI skills (e.g., in the form of machine learning (ML) models) through an AI fabric that seamlessly applies, scales, manages AI for RPA workflows of robots. This cycle of planning, measuring, and reporting may be repeated, potentially guided by more and more AI, to iteratively improve the effectiveness of RPA for a business. RPA implementations may also be identified and implemented based on their estimated return on investment (ROI).

A robot system includes at least one robot configured to interact with a plurality of operators in a warehouse. The robot has a proximity detector configured to detect the presence of an operator of the plurality of operators when they are within a predetermined distance of the at least one robot. There is a processor configured to retrieve from a memory a set of operator interaction preferences for the operator detected. And, there is a display device configured to allow the at least one robot to interact with the detected operator based on the set of operator interaction preferences of the detected operator.

A communication system according to an embodiment of the present invention includes a robot control device, a programmable logic controller for establishing communication with the robot control device, and a communication setting device that is loaded with a configuration file to define communication parameters used in the communication. The communication setting device sets the communication parameters to the programmable logic controller. The robot control device includes a file output unit for outputting the configuration file depending on an internal state of the robot control device.

Process evolution for robotic process automation (RPA) and RPA workflow micro-optimization are disclosed. Initially, an RPA implementation may be scientifically planned, potentially using artificial intelligence (AI). Embedded analytics may be used to measure, report, and align RPA operations with strategic business outcomes. RPA may then be implemented by deploying AI skills (e.g., in the form of machine learning (ML) models) through an AI fabric that seamlessly applies, scales, manages AI for RPA workflows of robots. This cycle of planning, measuring, and reporting may be repeated, potentially guided by more and more AI, to iteratively improve the effectiveness of RPA for a business. RPA implementations may also be identified and implemented based on their estimated return on investment (ROI).

A robot includes a shoulder, an arm connected to the shoulder, an imaging device that is connected to the shoulder via a support, and an image receiver that receives a captured image captured by the imaging device, and a robot controller that controls the arm based on the captured image, and in which the imaging device is two sets of stereo cameras having different depths of field.

A method for process parameter optimization in a robotic manufacturing process includes identifying, in two or more successive iterations, a system model for the robotic manufacturing process. Manufacturing process parameters are optimized based on the model identified. The process may be a robotic assembly process.

A system for identifying and tracking performance of operators in a warehouse. The system comprises at least one robot configured to interact with the operators in the warehouse. The at least one robot includes a first transceiver, a proximity detector, and a memory. The first transceiver defines a zone surrounding the robot and the proximity detector is coupled to the first transceiver. The proximity detector is configured to detect entry, into the zone, of an operator and to detect exit of the operator from the zone. The memory contains information identifying said operators who have entered and exited the zone.

Disclosed is a method for controlling cleaning at least a portion of a building facade (104a). A multi-dimensional map of a facade (104a) is cleaned from an elevator platform (200) of an elevator system is received. According to the map, an ordered sequence of instructions is determined. The instructions comprise robotic arm instructions and elevator platform instructions that are temporally intertwined to execute a cleaning pattern covering at least part of facade (104a). The robotic arm instructions are forwarded to control one or more robotic arms (206) and the elevator platform instructions are forwarded to control the elevation of the elevator platform (200). A corresponding device (221, 213) and non-transient memory (223), and a system having the device, are also disclosed.

A robot control method, a device, and a remote control system. A mobile terminal establishes connection to a robot by scanning a two-dimensional code and by a local area network. A remote monitoring and control center connects to the mobile terminal by a wide area network, and when the mobile terminal and the remote monitoring and control center send information to each other, the mobile terminal breaks off connection with the robot, thereby ensuring information transmission security. Operation instruction information from a user on an operation interface is acquired, the operation instruction information comprising a control parameter and an action execution parameter, and based on the set control parameter and the set action execution parameter, first control instruction information is generated, and sent to the robot by a wireless communications network, in order to cause the robot to execute a corresponding action based on the first control instruction information.

An access management robot facilitation system facilitates a robot to execute access management tasks on a target system.