A system for generating a machine learning system to generate guidance information based on locations of objects is provided. The system accesses training data that includes training time-of-arrival (“TOA”) information of looks and guidance information for each look. The guidance information is based on a training collection of object locations. The TOA of a look represents, for each object location of a training collection of object locations, times between signals transmitted by transmitters and return signals received by receivers. The return signals represent signals reflected from an object at the object location. The system trains a machine learning system using the training data wherein the machine learning system inputs TOA information and outputs guidance information.

Movable elements of a machine are moved by a control device of the machine by controlling drives of the machine. To monitor the movement of the movable elements for collision with each other or with a stationary element, two monitoring devices check, independently from each other, using a computer program, whether there is a risk of collision in the working space. Depending on whether the monitoring devices detect a risk of collision or not, they intervene, independently from each other, in a corrective manner, in the control of the drives or not, and/or independently emit an alarm message or not. The two computer programs are designed in a diverse manner. The two monitoring devices differ from one another. One monitoring device is identical to the control device, while the other monitoring device is embodied as an industrial PC with a data link to the control device.

Systems and methods for collision detection and avoidance are provided. In one aspect, a robotic medical system including a first set of links, a second set of links, a console configured to receive input commanding motion of the first set of links and the second set of links, a processor, and at least one computer-readable memory in communication with the processor. The processor is configured to access the model of the first set of links and the second set of links, control movement of the first set of links and the second set of links based on the input received by the console, determine a distance between the first set of links and the second set of links based on the model, and prevent a collision between the first set of links and the second set of links based on the determined distance.

Systems and methods for collision avoidance using object models are provided. In one aspect, a robotic medical system, includes a platform, one or more robotic arms coupled to the platform, a console configured to receive input commanding motion of the one or more robotic arms, a processor, and at least one computer-readable memory in communication with the processor. The processor is configured to control movement of the one or more robotic arms in a workspace based on the input received by the console, receive an indication of one or more objects are within reach of the one or more robotic arms, and update the model to include a representation of the one or more objects in the workspace.

A robot and operation method is disclosed. The robot according to the present disclosure may include a sensor, a microphone, and a controller. The robot may execute an artificial intelligence (AI) algorithm and/or a machine learning algorithm, and may communicate with other electronic devices in a 5G communication environment. An embodiment may include detecting a movement of the robot to a location; detecting an obstacle within a predetermined range from the robot; estimating an occupation area of the obstacle in space; and identifying a sound signal received from the estimated occupation area of the obstacle from among a plurality of sound signals received by a plurality of microphones of the robot at the location.

Movable elements of a machine are moved by a control device of the machine by controlling drives of the machine. To monitor the movement of the movable elements for collision with each other or with a stationary element, two monitoring devices check, independently from each other, using a computer program, whether there is a risk of collision in the working space. Depending on whether the monitoring devices detect a risk of collision or not, they intervene, independently from each other, in a corrective manner. In the control of the drives or not, and/or independently emit an alarm message or not. The two computer programs are designed in a diverse manner. The two monitoring devices differ from one another. One monitoring device is identical to the control device, while the other monitoring device is embodied as an industrial PC with a data link to the control device.

A robot teaching system includes a teaching unit and a robot including a robotic arm and a robot controller. In the robot teaching system, a workpiece includes an internal space having an opening, and a target object of a work by the end effector exists in the internal space. The robot controller determines a possibility that the arm part interferes with an edge of the opening while the robotic arm is jogging or inching.

A mobile robot is configured for operation in a commercial or industrial setting, such as an office building or retail store. The robot can patrol one or more routes within a building, and can detect violations of security policies by objects, building infrastructure and security systems, or individuals. In response to the detected violations, the robot can perform one or more security operations. The robot can include a removable fabric panel, enabling sensors within the robot body to capture signals that propagate through the fabric. In addition, the robot can scan RFID tags of objects within an area, for instance coupled to store inventory Likewise, the robot can generate or update one or more semantic maps for use by the robot in navigating an area and for measuring compliance with security policies.

Systems and methods for collision detection and avoidance are provided. In one aspect, a robotic medical system including a first set of links, a second set of links, a console configured to receive input commanding motion of the first set of links and the second set of links, a processor, and at least one computer-readable memory in communication with the processor. The processor is configured to access the model of the first set of links and the second set of links, control movement of the first set of links and the second set of links based on the input received by the console, determine a distance between the first set of links and the second set of links based on the model, and prevent a collision between the first set of links and the second set of links based on the determined distance.

Implementations are described herein for offline computation and caching of precalculated joint trajectories. In various implementations, an instruction may be obtained to move an end effector of a robot between start and target positions. A first type of trajectory planning may be performed in real time or online to calculate a first joint trajectory of the robot that moves the end effector from the start to target position. The robot may then implement the first joint trajectory. A second type of trajectory planning may be performed offline, e.g., during downtime of the robot, to precalculate a second joint trajectory of the robot to move the end effector from the start to target position. The second type of trajectory planning may require more resources than were required by the first type of trajectory planning. Data indicative of the precalculated second joint trajectory of the robot may be stored for future use.