Systems and methods for detection of people are disclosed. In some exemplary implementations, a robot can have a plurality of sensor units. Each sensor unit can be configured to generate sensor data indicative of a portion of a moving body at a plurality of times. Based on at least the sensor data, the robot can determine that the moving body is a person by at least detecting the motion of the moving body and determining that the moving body has characteristics of a person. The robot can then perform an action based at least in part on the determination that the moving body is a person.

Embodiments of a learning-based excavation planning method are disclosed for excavating rigid objects in clutter, which is challenging due to high variance of geometric and physical properties of objects, and large resistive force during the excavation. A convolutional neural network is utilized to predict a probability of excavation success. Embodiments of a sampling-based optimization method are disclosed for planning high-quality excavation trajectories by leveraging the learned prediction model. To reduce simulation-to-real gap for excavation learning, voxel-based representations of an excavation scene are used. Excavation experiments were performed in both simulation and real world to evaluate the learning-based excavation planners. Experimental results show that embodiments of the disclosed method may plan high-quality excavations for rigid objects in clutter and outperform baseline methods by large margins.

The invention relates to a sensor arrangement and to a method for safeguarding a monitored zone at a machine. The sensor arrangement comprises a camera continuously generating 3D images, a control and evaluation unit for the position detection of objects in the monitored zone and, in the case of a hazardous position, initiating a safety-directed response of the machine, with a buffer memory unit being provided for storing last recorded images and with a 3D reference map being prepared from the stored images when the safety-directed response was initiated, a voxel identification unit being provided for flagging those voxels in the current 3D image whose coordinates differ by a specified distance from those of the corresponding voxels of the reference map, a movement recognition unit being provided in which the voxels thus identified are examined as to whether they display position changes that are above a fixed threshold in the course of a fixed number of further current images, and independently thereof, a restart signal for the machine being able to be output at an output.

Automatically performing an operation on an object-with a tool-carried by a polyarticulated system-that can be moved in a working environment, the object-and the working environment being open-ended or insufficiently defined to carry out the operation. A method comprises: capturing a scatter plot image of the object-and the working environment-with a 3D sensor, merging this image with the CAD model of the system and the environment into a working image, and defining anti-collision parameters;— defining a path of the tool-on the portion of the working image representing the object and executing a simulation of the corresponding movement of the system-and the tool in the working image so as to ensure that the operation is feasible;— and if the operation is feasible executing the actual movement of the system carrying the tool-according to the path defined for performing the operation on the object.

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.

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.

The present disclosure discloses a method and apparatus for motion planning of a robot, a method and apparatus for path planning of a robot, and a method and apparatus for grasping of a robot. The method includes: when the robot operates on an object to be operated, performing, in combination with a space model of a real scene where the object is located, collision detection on the object and a collision subject in the space model; and determining a motion planning scheme for the robot corresponding to a result of the collision detection based on collision sensitivity of the object and collision sensitivity of the collision subject, the motion planning scheme being formed by the robot operating on the object.

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.

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.

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.