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.

Disclosed are a device for planning a path of a mobile robot and a method for planning a path of a mobile robot. The device for planning a path of a mobile robot and the method for planning a path of a mobile robot, according to the present invention, extend a route planning tree by sampling grid-based nodes between a start point and a target point of the mobile robot. Therefore, the present invention can configure a path plan of the mobile robot to be a path optimized within a quick time.

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.

Disclosed techniques for graph generation for online path planning offer multiple advantages, such as providing for high-quality motion during online operation of the robot, while reducing the computational burden of graph generation. Achieving these competing goals involves reducing the dimensionality of the graph generation problem by performing unconstrained sampling that defines partial robot poses that set values for fewer than all configuration parameters of the robot. The remaining configuration parameters for each sample are then determined in dependence on a distance function that relates the partial pose to one or more reference robot poses that are associated with one or more tasks to be performed by the robot and are provided as inputs to the graph generation. Reference robot poses may be determined automatically based on computer analysis of the robot application or may be user-input values.

A traveling robot includes a mobile unit, an arm and an electronic control unit. The electronic control unit is configured to set a target position to which the traveling robot intends to travel. The electronic control unit is configured to calculate a center of gravity of the traveling robot. The electronic control unit is configured to plan a motion of a movable part of the mobile unit and/or arm to the set target position. The electronic control unit is configured to, in the case where the traveling robot is close to the set target position, plan the motion of the movable part of the mobile unit and/or arm such that a limitation on a range of the center of gravity is relaxed as compared to a limitation on the range of the center of gravity in the case where the traveling robot is far from the set target position.

A trajectory generation device includes a storage unit that stores a plurality of trajectories; a trajectory acquisition unit that acquires a trajectory, corresponding to an environment similar to a current environment, from the plurality of trajectories stored in the storage unit; and a trajectory generation unit that calculates a longest trajectory part, which is present in a moving object moving area in the trajectory acquired by the trajectory acquisition unit, and generates a trajectory by connecting both ends of the calculated longest trajectory part to a predetermined start point and a predetermined end point respectively.

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.

Disclosed are a device for planning a path of a mobile robot and a method for planning a path of a mobile robot. The device for planning a path of a mobile robot and the method for planning a path of a mobile robot, according to the present invention, extend a route planning tree by sampling grid-based nodes between a start point and a target point of the mobile robot. Therefore, the present invention can configure a path plan of the mobile robot to be a path optimized within a quick time.

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.

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.