Example embodiments may relate to methods and systems for selecting a grasp point on an object. In particular, a robotic manipulator may identify characteristics of a physical object within a physical environment. Based on the identified characteristics, the robotic manipulator may determine potential grasp points on the physical object corresponding to points at which a gripper attached to the robotic manipulator is operable to grip the physical object. Subsequently, the robotic manipulator may determine a motion path for the gripper to follow in order to move the physical object to a drop-off location for the physical object and then select a grasp point, from the potential grasp points, based on the determined motion path. After selecting the grasp point, the robotic manipulator may grip the physical object at the selected grasp point with the gripper and move the physical object through the determined motion path to the drop-off location.

Example embodiments may relate to methods and systems for selecting a grasp point on an object. In particular, a robotic manipulator may identify characteristics of a physical object within a physical environment. Based on the identified characteristics, the robotic manipulator may determine potential grasp points on the physical object corresponding to points at which a gripper attached to the robotic manipulator is operable to grip the physical object. Subsequently, the robotic manipulator may determine a motion path for the gripper to follow in order to move the physical object to a drop-off location for the physical object and then select a grasp point, from the potential grasp points, based on the determined motion path. After selecting the grasp point, the robotic manipulator may grip the physical object at the selected grasp point with the gripper and move the physical object through the determined motion path to the drop-off location.

A path generator includes an object setter that sets an object model; a device setter that sets a device model as a model of a robot arm; a path generator that generates a path of the robot arm stepwise; and an interference determiner that performs interference determination on the object model and the device model after having moved along the path, based on a distance between the object model and the device model. If it is determined that there is a possibility of interference between the object model and the device model, at least one of the object setter or the device setter increases a density of point groups of the point group model, and performs interference determination by using the point group model with the increased density of the point groups. If determined that that there is no possibility of interference, the path generator generates a next path.

A system and method is provided for determining grasping positions for two-handed grasps of industrial objects. The system may include a processor configured to determine a three dimensional (3D) voxel grid for a 3D model of a target object. In addition, the processor may be configured to determine at least one pair of spaced apart grasping positions on the target object at which the target object is capable of being grasped with two hands at the same time based on processing the 3D voxel grid for the target object with a neural network trained to determine grasping positions for two-handed grasps of target objects using training data. Such training data may include 3D voxel grids of a plurality of 3D models of training objects and grasping data including corresponding pairs of spaced-apart grasping positions for two-handed grasps of the training objects. Also, the processor may be configured to provide output data that specifies the determined grasping positions on the target object for two-handed grasps.

Robotic carton loader or unloader incorporates three-dimensional (3D) and two-dimensional (2D) sensors to detect respectively a 3D point cloud and a 2D image of a carton pile within transportation carrier such as a truck trailer or shipping container. Edge detection is performed using the 3D point cloud, discarding segments that are two small to be part of a product such as a carton. Segments that are too large to correspond to a carton are 2D image processed to detect additional edges. Results from 3D and 2D edge detection are converted in a calibrated 3D space of the material carton loader or unloader to perform one of loading or unloading of the transportation carrier. Image processing can also detect jamming of products sequence from individually controllable zones of a conveyor of the robotic carton loader or unloader for singulated unloading.

A robot interference checking motion planning technique using point sets. The technique uses CAD models of robot arms and obstacles and converts the CAD models to 3D point sets. The 3D point set coordinates are updated at each time step based on robot and obstacle motion. The 3D points are then converted to 3D grid space indices indicating space occupied by any point on any part. The 3D grid space indices are converted to 1D indices and the 1D indices are stored as sets per object and per time step. Interference checking is performed by computing an intersection of the 1D index sets for a given time step. Swept volumes are created by computing a union of the 1D index sets across multiple time steps. The 1D indices are converted back to 3D coordinates to define the 3D shapes of the swept volumes and the 3D locations of any interferences.

Methods, systems, and platforms for automatic setup planning for a robot. The method includes sampling multiple poses in multiple dimensions within a robotic workspace. The method includes generating one or more candidate configurations based on the multiple poses. The method includes determining a score for each candidate configuration of the one or more candidate configurations. The score represents area coverage of a region of interest and at least one of an amount of setup time of the candidate configuration or an amount of energy used. The method includes determining a set of candidate configurations that has an overall area coverage that covers the region of interest based on the score for each candidate configuration. The method includes controlling a position and an orientation of the object based on the set of candidate configurations.

Methods, systems, and apparatus for automatically moving a tool attached to a robotic manipulator from a start position to a goal position. The method includes determining, using a processor, a plurality of next possible positions from the start position. The method includes selecting a second position from the plurality of next possible positions based on respective costs associated with moving the tool from the start position to each of the possible positions in the plurality of next possible positions. The method includes moving, using a plurality of actuators, the tool to the second position. The method includes determining an updated plurality of next possible positions, selecting a next position, and moving the tool to the next position until the goal position is reached.

A control apparatus includes a processor that executes a first point cloud generation process including a first imaging process of acquiring a first image according to a first depth measuring method and a first analysis process of generating a first point cloud and a second point cloud generation process including a second imaging process of acquiring a second image according to a second depth measuring method and a second analysis process of generating a second point cloud, and detects the object using the first point cloud or the second point cloud. The first point cloud generation process completes in a shorter time than the second point cloud generation process, and the processor starts the second point cloud generation process after the first imaging process and discontinues the second point cloud generation process if the first point cloud satisfies a predetermined condition of success.

A robot control system determines which of a number of discretizations to use to generate discretized representations of robot swept volumes and to generate discretized representations of the environment in which the robot will operate. Obstacle voxels (or boxes) representing the environment and obstacles therein are streamed into the processor and stored in on-chip environment memory. At runtime, the robot control system may dynamically switch between multiple motion planning graphs stored in off-chip or on-chip memory. The dynamically switching between multiple motion planning graphs at runtime enables the robot to perform motion planning at a relatively low cost as characteristics of the robot itself change.