Abstract: This specification describes trajectory planning for robotic devices. A robotic navigation system can obtain, for each of multiple time steps, data representing an environment of a robot at the time step. The system generates a series of occupancy maps for the multiple time steps, and uses the series of occupancy maps to determine occupancy predictions for one or more future time steps. Each occupancy prediction can identify predicted locations of obstacles in the environment of the robot at a different one of the future time steps. A planned trajectory can be determined for the robot using the occupancy predictions, and the robot initiates travel along the planned trajectory.

Abstract: Disclosed are a method and a system for charging a robot. A method for charging a robot according to an embodiment of the present disclosure includes monitoring a battery level of a first robot which is providing a service, determining a charging robot for charging the first robot, from a plurality of second robots, when a battery level of the first robot falls below a first threshold level, and transmitting an instruction to move to a target position to the determined charging robot, in which determining the charging robot comprises determining the charging robot based at least partly on distances between the first robot and the second robots and battery levels of the second robots. Embodiments of the present disclosure may be implemented by executing an artificial intelligence algorithm and/or a machine learning algorithm in a 5G environment connected for Internet of Things.

Abstract: A method for generating a trajectory of a robot from a first configuration to a second configuration within an environment while steering away from obstacles may include obtaining physical workspace information associated with the environment in which the robot is configured to operate; obtaining, using a first neural network, a set of weights of a second neural network that is configured to generate a set of values associated with a set of configurations of the robot with respect to the second configuration; obtaining, by applying the set of weights to the second neural network, the set of values associated with the set of configurations of the robot with respect to the second configuration; and generating the trajectory of the robot from the first configuration to the second configuration within the environment, based on the set of values.

Abstract: To provide a robot controller and a robot control method that do not need a logic command to be associated with a teaching position for a robot, and that are thus capable of executing the logic command at a desired position and a desired timing. A robot controller includes: an operation command interpretation unit that interprets an operation command program describing a teaching operation and a teaching position for a robot, and that generates an operation command; a logic command interpretation unit that interprets a logic command program describing a logic command instructing a machining process to be performed by the robot and an execution position for the logic command, independently from the teaching operation and the teaching position, and that generates the logic command that includes the execution position; and a command execution unit that executes the operation command and the logic command.

Abstract: Methods and systems for improved positioning accuracy relative to a digital map are disclosed, and which are preferably used for highly and fully automated driving applications, and which may use localisation reference data associated with a digital map. The invention further extends to methods and systems for the generation of localisation reference data associated with a digital map.

Abstract: A robotic surgical system for treating a patient comprises a first robotic arm configured to remotely control a surgical instrument that is positionable within a cavity of the patient; a second robotic arm configured to remotely control a device that is passable through an orifice of the patient; and a control circuit communicatively couplable to the first and second robotic arm. The first and second robotic are each attached to a surgical platform. The control circuit is configured to determine a position of the arms; cause each of the first and second robotic arm to change their respective position and orientation based on an adjustment of a platform position of the surgical platform; and control the first robotic arm and the second robotic arm to cooperatively interact to perform a surgical operation.

Abstract: Systems, apparatuses, and methods for rapid machine learning for floor segmentation for robotic devices are disclosed herein. According to at least one non-limiting exemplary embodiment, a robotic system is disclosed. The robotic system may comprise a neural network embodied therein capable of learning associations between color values of pixels and corresponding classifications of those pixels, wherein neural network is trained initially to identify floor and non-floor pixels within images. A user input may be provided to the neural network to further configure the neural network to be able to identify navigable floors and unnavigable floors unique to an environment without a need for additional annotated training images specific to the environment.

Abstract: In an embodiment, a method during execution of a motion plan by a robotic arm includes determining a voice command from speech of a user said during the execution of the motion plan, determining a modification of the motion plan based on the voice command from the speech of the user, and executing the modification of the motion plan by the robotic arm.

Abstract: A method for characterising the environment of a robot, the robot having a flexible arm having a plurality of joints, a datum carried by the arm, a plurality of drivers arranged to drive the joints to move and a plurality of position sensors for sensing the position of each of the joints, the method comprising: contacting the datum carried by the arm with a first datum on a second robot in the environment of the first robot, wherein the second robot has a flexible arm having a plurality of joints, and a plurality of drivers arranged to drive those joints to move; calculating in dependence on the outputs of the position sensors a distance between a reference location defined in a frame of reference local to the robot and the first datum; and controlling the drivers to reconfigure the first arm in dependence on at least the calculated distance.

Abstract: A human augmented robotics intelligence operation system can include a plurality of robots, each robot having a plurality of sensors; a robot control unit; and one or more articulating joints; a cloud-based robotic intelligence engine having; a communication module; a historical database; and a processor; and a human augmentation platform. The processor can be configured to make a probabilistic determination regarding the likelihood of successfully completing the particular user command. When the probabilistic determination is above a pre-determined threshold, the processor sends necessary executable commands to the robot control unit. Alternatively, when the probabilistic determination is below the predetermined threshold, the processor generates an alert and flags the operation for human review.

Abstract: The present disclosure relates to a user interactive electronic system, the user interactive electronic system comprising a robotic arm and at least an attachment detachably affixed to a distal end of the robotic arm. The present disclosure also relates to a method for operating such a user interactive electronic system and to a computer program product.

Abstract: An autonomous moving apparatus control system including a range sensor, a reflection plate, and a control unit. The range sensor is installed in a cage of an elevator and detects a distance to an object by receiving reflected light of signal light applied to the object. The reflection plate is disposed in an elevator hall of a floor on which the elevator stops, and reflects the signal light. The control unit determines whether or not a mobile robot, which is an autonomous moving apparatus, can get on and off the elevator based on a detected distance, the detected distance being a distance to the reflection plate detected by the range sensor.

Abstract: According to an embodiment, an article posture changing device includes: a first end effector configured to grasp a projected tag of an article by adsorption; an arm unit configured to support the first end effector and move the first end effector along at least a vertical direction; and a controller configured to control the arm unit and the first end effector to grasp the tag, lift the article upward, separate the article from a placement surface, change an angle of the article by weight thereof, move the article downward, place the article on the placement surface, and release the grasp of the tag.

Abstract: A system for performing autonomous agriculture within an agriculture production environment includes one or more agriculture pods, a stationary robot system, and one or more mobile robots. The agriculture pods include one or more plants and one or more sensor modules for monitoring the plants. The stationary robot system collects sensor data from the sensor modules, performs farming operations on the plants according to an operation schedule based on the collected sensor data, and generates a set of instruction for transporting the agriculture pods within the agriculture production environment. The stationary robot system communicates the set of instructions to the agriculture pods. The mobile robots transport the agriculture pods between the stationary robot system and one or more other locations within the agriculture production environment according to the set of instructions.

Abstract: A management system is a management system which includes a remote work system having at least one master unit and a plurality of slave units which can be remotely operated and a management server, in which each of the plurality of slave units includes a gripping unit that is configured to grip a target, grip the target by means of autonomous control, and, when being connected to the master unit by the management server, grip the target by means of a remote operation by the master unit, the master unit is configured to perform the remote operation on the plurality of slave units, and the management server includes an operation management unit that is configured to connect the master unit and the plurality of slave units when a remote operation is required.

Abstract: User input devices (UIDs) for controlling a surgical robotic system are described. A UID can include one or more tracking sensors to generate respective spatial state signals in accordance with a pose of the UID. At least one of the tracking sensors can be a camera. In the case of multiple tracking sensors, the spatial state signals are processed by a sensor fusion algorithm to generate a more robust, single tracking signal and a quality measure. The tracking signal and the quality measure are then used by a digital control system to control motion of a surgical robotic system actuator that is associated with the UID. Other embodiments are also described and claimed.

Abstract: A surgical robotic system including a surgical table, a surgical robotic manipulator coupled to the surgical table and comprising a plurality of links coupled together by a plurality of joints that are operable to move with respect to one another to move the surgical robotic manipulator, at least one of the plurality of links or the plurality of joints having a portion that faces another of the plurality of links or the plurality of joints, a proximity sensing assembly coupled to the portion of the at least one of the plurality of links or the plurality of joints, the proximity sensing assembly operable to detect an object prior to the surgical robotic manipulator colliding with the object and to output a corresponding detection signal, and a processor operable to receive the corresponding detecting signal and cause the manipulator or the object to engage in a collision avoidance operation.

Abstract: A system for eliminating interference of randomly stacked workpieces is disclosed. The system includes a three-dimensional sensing module, a pick-up apparatus and a control module. The control module is coupled to the three-dimensional sensing module and the pick-up apparatus. The control module is configured to control the three-dimensional sensing module to capture a three-dimensional image, analyze the three-dimensional image to obtain an image information, select a target workpiece to be picked up according to the image information, arrange an interference elimination path for the target workpiece, and control the pick-up apparatus to eliminate interference of the target workpiece according to the interference elimination path.

Abstract: Disclosed herein are an apparatus and method for generating a topological map for navigation of a robot. The method for generating a topological map for navigation of a robot, performed by the apparatus for building the topological map for the navigation of the robot, includes calculating the physical size of a single pixel on a metric map of a space in which a mobile robot is to navigate, extracting the physical coordinates of the pixel on the metric map, building node data and edge data for the navigation of the mobile robot using the physical coordinates, and generating a topological map for the navigation of the mobile robot based on the built node data and the built edge data.

Abstract: An infrastructure is provided for improving the safety of autonomous systems. An autonomous vehicle management system (AVMS) controls one or more autonomous functions or operations performed by a vehicle or machine such that the autonomous operations are performed in a safe manner. The AVMS uses various artificial intelligence (AI) based techniques (e.g., neural networks, reinforcement learning (RL) techniques, etc.) and models as part of its processing. For an inferring data point, for which a prediction is made by AVMS using an AI model, the AVMS checks how statistically similar (or dissimilar) the inferring data point is to the distribution of the training dataset. A score (confidence score) is generated indicative of how similar or dissimilar the inferring data point is to the training dataset. The AVMS uses this confidence score to decide how the prediction made by the AI model is to be used.