A motion path generation device generates a motion path of a robot that has a plurality of joints and a plurality of shafts. Adjacent two of the plurality of shafts are connected by a corresponding one of the plurality of joints. The motion path generation device generates a via point candidate that is a candidate of a next via point to be connected to a parent via point. The motion path generation device adds the via point candidate as a new via point in response to determining that the via point candidate does not interfere with an obstacle.

A trajectory generating method includes a first generating process of generating a plurality of trajectories between a start teaching point and a target teaching point, an evaluation process of evaluating a motion of the robot arm on each trajectory to calculate an evaluation value of each trajectory, a selection process of selecting one of the plurality of trajectories based on calculated evaluation values, and an update process of updating the trajectory by repeating the processes of generating a plurality of new trajectories by changing a selected trajectory in the selection process, of calculating an evaluation value of a motion of the robot arm on each changed trajectory and of selecting a trajectory based on calculated evaluation values.

To generate a more appropriate path, provided is a robot path generation device including circuitry configured to: hold a track planning module learning data set, in which a plurality of pieces of path data generated based on a motion constraint condition of a robot, and evaluation value data, which corresponds to each of the plurality of pieces path data and is a measure under a predetermined evaluation criterion, are associated with each other; and generate, based on a result of a machine learning process that is based on the track planning module learning data set, a path of the robot between a set start point and a set end point, which are freely set.

A trajectory generating method includes a first generating process of generating a plurality of trajectories between a start teaching point and a target teaching point, an evaluation process of evaluating a motion of the robot arm on each trajectory to calculate an evaluation value of each trajectory, a selection process of selecting one of the plurality of trajectories based on calculated evaluation values, and an update process of updating the trajectory by repeating the processes of generating a plurality of new trajectories by changing a selected trajectory in the selection process, of calculating an evaluation value of a motion of the robot arm on each changed trajectory and of selecting a trajectory based on calculated evaluation values.

Methods and systems to control stacked hexapod platforms for use as tools, which function with both high accuracy and high precision are provided. In some embodiments, the methods and systems include a convergence of modern control theory, and machine learning. Furthermore, some embodiments provide control algorithms to carry out autonomous in-space assembly operations using assemblers. Some embodiments provide methods and systems which combine long-reach low precision manipulators and smaller, high-precision assembler with interchangeable tools.

A robot motion planning technique using an external computer communicating with a robot controller. A camera or sensor system provides input scene information including start and goal points and obstacle data to the computer. The computer plans a robot tool motion based on the start and goal points and the obstacle environment, where the robot motion is planned using either a serial or parallel combination of sampling-based and optimization-based planning algorithms. In the serial combination, the sampling method first finds a feasible path, and the optimization method then improves the path quality. In the parallel combination, both sampling and optimization methods are used, and a path is selected based on computation time, path quality and other factors. The computer converts dense planned waypoints to sparse command points for transfer to the robot controller, and the controller computes robot kinematics and interpolation points and controls the movement of the robot.

An information processing method, includes an acquisition step in which a control device acquires a starting point and an ending point of a motion of a first object model in a virtual space, a trial step in which the control device tries to generate a branch composing a tree structure model, an interference determination step in which the control device determines whether the branch generated in the trial step interferes with a second object model in the virtual space, and a first branch adding step in which the control device moves the first object model in the virtual space based on kinematics conditions related to the first and second object models in a case where an occurrence of the interference has determined in the interference determination step and adds a moving path of the first object model to the tree structure model as a branch in the interference determination step.

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.

To generate a more appropriate path, provided is a robot path generation device including circuitry configured to: hold a track planning module learning data set, in which a plurality of pieces of path data generated based on a motion constraint condition of a robot, and evaluation value data, which corresponds to each of the plurality of pieces path data and is a measure under a predetermined evaluation criterion, are associated with each other; and generate, based on a result of a machine learning process that is based on the track planning module learning data set, a path of the robot between a set start point and a set end point, which are freely set.

Methods and systems to control stacked hexapod platforms for use as tools, which function with both high accuracy and high precision are provided. In some embodiments, the methods and systems include a convergence of modern control theory, and machine learning. Furthermore, some embodiments provide control algorithms to carry out autonomous in-space assembly operations using assemblers. Some embodiments provide methods and systems which combine long-reach low precision manipulators and smaller, high-precision assembler with interchangeable tools.