A method for queuing robots destined for a target location in an environment, includes determining if a first robot occupies the target location and if it is determined that the first robot occupies the target location, determining if a second robot destined for the target location has entered a predefined target zone proximate the target location. If the second robot has entered the predefined target zone, the method further includes navigating the second robot to a first queue location and causing the second robot to wait at the first queue location until the first robot no longer occupies the target location. The method also includes navigating the second robot to the target location after the first robot leaves the target location.

A method for queuing robots destined for a target location in an environment, includes determining if a first robot occupies the target location and if it is determined that the first robot occupies the target location, determining if a second robot destined for the target location has entered a predefined target zone proximate the target location. If the second robot has entered the predefined target zone, the method further includes navigating the second robot to a first queue location and causing the second robot to wait at the first queue location until the first robot no longer occupies the target location. The method also includes navigating the second robot to the target location after the first robot leaves the target location.

A method for determining values influencing movement of a robot is disclosed. The method includes the following steps: a) provision of a task to be performed by the robot and a worker; b) provision of a layout of a workstation; c) provision of tool data; d) determination of respective axial movement patterns of the robot on the basis of steps a) to c); e) provision of a worker workspace; f) determination of critical path points of the robot, where a specified movement speed is exceeded by the robot and/or a specified mass of an element to be moved by the robot is exceeded, on the basis of the axial movement patterns and the workspace; g) simulation of respective collisions at the critical path points by a second robot; and h) determination of permissible operating speeds of the robot for each critical path point on the basis of the simulated collisions.

Disclosed are a collision avoidance method for a moving device, a collision avoidance apparatus for a moving device, and a computer-readable storage medium. This application relates to the field of artificial intelligence technologies. According to the method, a parking direction of a moving device in an avoidance area is adjusted, so that a startup time used by the moving device after avoidance completes may be reduced. The method includes: determining a target path direction of a moving device; determining a first candidate parking direction and a second candidate parking direction; determining, based on the target path direction, a target parking direction of the moving device from the first candidate parking direction and the second candidate parking direction; and controlling, based on the target parking direction, the moving device to be parked in the avoidance area.

A movable range of angle of each of the plurality of joints and a safety region defined within the movable range are set. An angle command value is generated to each of the plurality of joints, based on current angle data and a distal end position command value. A fault avoidance control is carried out to make a change rate of the angle command value small, when the angle command value is generated to either of the plurality of joints, and the angle command value of the joint exceeds the safety region.

A teaching device constructs, in a virtual space, a virtual robot system in which a virtual 3D model of a robot and a virtual 3D model of a peripheral structure of the robot are arranged, and teaches a moving path of the robot. The teaching device includes an acquisition unit configured to acquire information about a geometric error between the virtual 3D models, and a correction unit configured to correct the moving path of the robot in accordance with the information acquired by the acquisition unit.

An offline teaching device for reducing an amount of time required to generate a motion route with which interference could be avoided, the offline teaching device including at least one processor. The processor generates, as a result of a motion program that includes a plurality of teaching points being input, numerous interpolation points on a motion route of a tool distal-end point of a robot, the motion route being formed among the teaching points in accordance with the motion program; and detects whether interference occurs between each of the generated interpolation points and a peripheral device.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for converting between different representations of collision exclusion relationships. One of the methods includes identifying a plurality of cliques in a collision exclusion graph. A bitmask representing collision exclusion relationships between the particular object and other objects in the robotic operating environment is generated using the identified cliques. A simulation of executing a robotic control plan for a robot in the robotic operating environment is performed using the generated bitmasks to detect collisions.

An interference determination device includes a controller that determines interference between a robot and an object. The controller obtains a plurality of vertices of at least one object within a motion range of the robot in a real space and positional information regarding the plurality of vertices. The controller determines interference between the robot and the object in the real space on a basis of the plurality of vertices and the positional information.

A method for generating a dataset of robot motion programs for training a path generation neural network. A large language model is used to configure a task environment and generate code which creates robot simulations. The large language model uses a robot task library and an asset geometry database as inputs. Based on the task and asset inputs and a task instruction, the large language model breaks down the task into steps, then generates code describing robot and object motion to complete the task. The generated code produces robot motions for the task, and a corresponding robot motion program is created and executed in simulation. The simulated robot motion programs are used to generate collision-free robot paths via RRT and/or optimization, and collision-free paths are validated for robot reachability and object placement success. Validated motion programs are added to the dataset and used for training the path generation neural network.