Abstract: 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. Various aspects of such robot motion planning are implemented in particular systems and methods that facilitate motion planning of the robot for various environments and tasks.

Abstract: A control unit drives a drive source to move the moving body. A detection unit detects external force applied to the moving body. A movement information deriving unit derives, based on the detected external force, a movement direction and movement speed of the moving body. The control unit drives the drive source based on the movement direction and movement speed derived by the movement information deriving unit.

Abstract: A mobile robot is configured for operation in a commercial or industrial setting, such as an office building or retail store. The robot can patrol one or more routes within a building, and can detect violations of security policies by objects, building infrastructure and security systems, or individuals. In response to the detected violations, the robot can perform one or more security operations. The robot can include a removable fabric panel, enabling sensors within the robot body to capture signals that propagate through the fabric. In addition, the robot can scan RFID tags of objects within an area, for instance coupled to store inventory. Likewise, the robot can generate or update one or more semantic maps for use by the robot in navigating an area and for measuring compliance with security policies.

Abstract: A vehicular collision avoidance system includes a forward-viewing camera viewing through the windshield at least forward of the equipped vehicle, a rearward-sensing radar sensor sensing at least rearward of the equipped vehicle, and an electronic control unit. The vehicular collision avoidance system detects vehicles present forward and/or rearward of the equipped vehicle. Responsive to data processing of radar data captured by the rearward-sensing radar sensor, the vehicular collision avoidance system detects another vehicle approaching the equipped vehicle from the rear, determines distance between the equipped vehicle and the other vehicle, and determines speed difference between the equipped vehicle and the other vehicle.

Abstract: A vehicle theft-prevention apparatus can include a locking mechanism with an engagement component. The locking mechanism can be engaged via a first action to apply a force on the vehicle when in an engaged state. The locking mechanism can disengage, via a second action, to withdraw the force from the vehicle when in the engaged state. The locking mechanism can maintain a current state of the force in response to the first action and the second action when in a disengaged state. One or more computing devices can receive a command to enable or disable the locking mechanism. In response to receiving the command, the computing device can cause the engagement component to transition the locking mechanism from the disengaged state to the engaged state or from an engaged state to a disengaged state.

Abstract: A method includes accessing RGB and depth image data representing a scene that includes at least a portion of a robotic limb. Using this data, a computing system may segment the image data to isolate and identify at least a portion of the robotic limb within the scene. The computing system can determine a current pose of the robotic limb within the scene based on the image data, joint data, or a 3D virtual model of the robotic limb. The computing system may then determine a desired goal pose, which may be based on the image data or the 3D virtual model. Based on the determined goal pose, the computing device determines the difference between the current pose and the goal pose of the robotic limb, and using this difference, provides a pose adjustment that for the robotic limb.

Abstract: Systems and methods for collision detection and avoidance are provided. In one aspect, a robotic medical system including a first set of links, a second set of links, a console configured to receive input commanding motion of the first set of links and the second set of links, a processor, and at least one computer-readable memory in communication with the processor. The processor is configured to access the model of the first set of links and the second set of links, control movement of the first set of links and the second set of links based on the input received by the console, determine a distance between the first set of links and the second set of links based on the model, and prevent a collision between the first set of links and the second set of links based on the determined distance.

Abstract: A response system may be provided. The response system may include a security system and an autonomous drone. The security system includes a security sensor and a controller. The drone includes a processor, a memory in communication with the processor, and a drone sensor. The processor may be programmed to receive the deployment request from the security system, navigate to the one or more zones of the coverage area included in the deployment request, collect drone sensor data of the one or more zones of the coverage area using the at least one drone sensor, determine that an incident has occurred, and/or transmit the collected drone sensor data and incident verification to the security system, wherein, in response to receiving the collected drone sensor data and incident verification, the security system is configured to generate a command for responding to the incident.

Abstract: A system is provided for hands-free handling of at least one asset by a user. The system includes a user device configured to be worn by a user and a control system remotely located relative to the user device and configured to exchange asset-related data with the user device, the asset-related data including at least one or more notifications related to the handling of the at least one asset by the user at the asset location. The user device contains a processor configured to determine location data associated with the at least one asset and including a location for the at least one asset, the determination being based, in part, upon the obtained asset identifier data; dynamically generate and display, at the user device, one or more navigational projections configured to guide the user to the asset location; and detect handling of the at least one asset by the user.

Abstract: In at least one embodiment, a system determines a set of possible grasp poses that allow a robot to successfully grasp an object by generating a set of potential grasp poses, and then evaluating the performance of each potential grasp pose. In at least one embodiment, the system performs a refinement operation on the grasp poses, and based on an evaluation of the poses, creates an improved set of possible grasps for the object.

Abstract: A handling device according to an embodiment includes a manipulator, a normal grid generation unit, a hand kernel generation unit, a calculation unit, and a control unit. The normal grid generation unit converts a depth image into a point cloud, generates spatial data including an object to be grasped that is divided into a plurality of grids from the point cloud, and calculates a normal vector of the point cloud included in the grid using spherical coordinates. The hand kernel generation unit generates a hand kernel of each suction pad. The calculation unit calculates ease of grasping the object to be grasped by a plurality of suction pads based on a 3D convolution calculation using a grid including the spatial data and the hand kernel. The control unit controls a grasping operation of the manipulator based on the ease of grasping the object to be grasped by the plurality of suction pads.

Abstract: Provided is a robot that includes: a first sensor having a first output and configured to sense state of a robot or an environment of the robot; a first hardware machine-learning accelerator coupled to the first output of the first sensor and configured to transform information sensed by the first sensor into a first latent-space representation; a second sensor having a second output and configured to sense state of the robot or the environment of the robot; a second hardware machine-learning accelerator configured to transform information sensed by the second sensor into a second latent-space representation; and a processor configured to control the robot based on both the first latent-space representation and the second latent-space representation.

Abstract: A surgical robotic arm includes a first link and a second link, wherein at least one of the first link or second link is movable relative to each other. The surgical robotic arm also includes a sensor assembly coupled to at least one of the first link or the second link. The sensor assembly includes a force sensing resistor assembly configured to measure force and an interface member disposed over the force sensing resistor assembly, the interface member configured to engage the at least one force sensing resistor assembly due to the interface member contacting an obstruction.

Abstract: Vision systems for robotic assemblies for handling cargo, for example, unloading cargo from a trailer, can determine the position of cargo based on shading topography. Shading topography imaging can be performed by using light sources arranged at different positions relative to the image capture device(s).

Abstract: A robot controller that controls a robot by automatically obtaining a controller capable of suitably controlling a wide range of robots. An image is acquired from an image capturing apparatus that photographs an environment including the robot. The robot is driven based on an output result obtained by inputting the image to a neural network. The neural network is updated according to a reward generated in a case where a plurality of virtual images photographed while changing an environmental condition of a virtual environment generated by virtualizing the environment and a state of a virtual robot are input to the neural network, and a policy of the virtual robot, which is output from the neural network, satisfies a predetermined condition.

Abstract: A robotic surgical system includes at least one robotic arm comprising at least one movable joint and an actuator configured to drive the at least one movable joint, and a controller configured to generate a first signal, the first signal comprising a first oscillating waveform having a first frequency and being modulated by a second oscillating waveform having a second frequency, wherein the second frequency is higher than the first frequency. The actuator is configured to drive the at least one movable joint based on the first signal to at least partially compensate for friction in the at least one movable joint.

Abstract: An oil condition estimation apparatus to be applied to a vehicle in which oil is agitated by a rotator includes a storage device and an execution device. The storage device stores mapping data for defining mapping. The mapping includes, as input variables, a speed variable indicating a rotation speed of the rotator, and a pressure variable indicating a pressure of the oil, and includes, as an output variable, an air bubble variable related to air bubbles contained in the oil. The execution device executes an acquisition process for acquiring values of the input variables, and a calculation process for calculating a value of the output variable by inputting, to the mapping, the values of the input variables acquired through the acquisition process.

Abstract: A teleoperated surgical system is provided that includes a surgical instrument that includes an end effector mounted for rotation about a slave pivot axis; a master control input includes a mount member, first and second master grip rotatably secured at the mount member for rotation about a master pivot axis; sensor to produce a sensor signal indicative of a slave grip counter-force about the slave pivot axis; one or more motors to impart a shear force to the mount member, perpendicular to the master pivot axis; one or more processors to convert the sensor signal to motor control signals to cause the motors to impart the feedback shear force to the first and second master grip members.

Abstract: A workpiece picking device includes a sensor that measures the workpieces, a hand that grasps the workpieces, a robot that moves the hand, and a control device thereof. The control device has a position orientation calculation part that calculates position, orientation and the like of the workpieces, a grasping orientation calculation part that calculates a grasping orientation of the workpieces by the hand, a route calculation part that calculates a route through which the hand moves to the grasping orientation, a sensor control part, a hand control part, a robot control part, a situation determination part that determines the situation of the workpieces on the basis of measurement result or the like of the three-dimensional position, and a parameter modification part that modifies at least one of a measurement parameter and various calculation parameters, when the determination result of the situations of the workpieces satisfies a predetermined condition.

Abstract: Embodiments of the present application relate to the technical field of aircrafts and disclose a method and apparatus for yaw fusion and an aircraft. The method for yaw fusion is applicable to an aircraft and includes: acquiring global positioning system (GPS) data, inertial measurement unit (IMU) data, and magnetometer data, wherein the GPS data includes GPS location, velocity, acceleration information, and GPS velocity signal quality, and the IMU data includes IMU acceleration information and IMU angular velocity information; determining a corrected yaw according to the IMU data, the GPS data, and the magnetometer data; determining a magnetometer alignment deviation angle according to the magnetometer data, the GPS data, and the corrected yaw; determining a GPS realignment deviation angle according to the GPS data and the IMU acceleration information; and generating a fused yaw according to the corrected yaw, the magnetometer alignment deviation angle, and the GPS realignment deviation angle.