Abstract: Provided is a globally optimal robot visual positioning method and device based on point-line features. The method comprises the following steps: acquiring a priori three-dimensional map of a current scene constructed in advance; acquiring a current image of the robot and the inertial measurement data; calculating a pitch angle and a roll angle of the current robot pose according to the current inertial sensor data and the inertial sensor data in the priori map; matching the two-dimensional point-line features detected in the current image with three-dimensional point-line features in a priori map; separating the rotation and translation of the pose to be solved according to the matched feature pairs, solving the rotation and then solving the translation so as to complete the dimensionality reduction of the search space.

Abstract: Provided is art capable of recognizing the states of a plurality of target objects arranged in a prescribed space region. This target object recognition device is provided with: a plurality of calculation processing units (21, 22) which each calculate the attitude state of a target object in a prescribed space region using a different technique; a state recognition unit (23) which recognizes the layout state of all of a plurality of target objects arranged in the space region; a method determination unit (24) which, in accordance with the result of the recognition by the state recognition unit (23), determines a method for using the results of the calculation performed by the calculation processing units (21, 22); and a target object recognition unit (25) which recognizes the attitude states of the target objects by means of the determined method for using the results of the calculation.

Abstract: A lateral position of a vehicle is estimated by collating a position of a lane division line in map data with a relative position of the lane division line with respect to the vehicle indicated by a recognition result by a peripheral monitoring sensor. The lateral position of the vehicle is precluded from being specified using the estimated lateral position of the vehicle in response to a lateral deviation between (i) a position of a landmark in the map data and (ii) a translated landmark position being equal to or greater than a first threshold value. The translated landmark position is a position on map deviated from the estimated lateral position of the vehicle assumed to be the lateral position of the vehicle on map by a relative position of the landmark with respect to the vehicle indicated by the recognition result by the peripheral monitoring sensor.

Abstract: A method includes maneuvering a robot in (i) a following mode in which the robot is controlled to travel along a path segment adjacent an obstacle, while recording data indicative of the path segment, and (ii) in a coverage mode in which the robot is controlled to traverse an area. The method includes generating data indicative of a layout of the area, updating data indicative of a calculated robot pose based at least on odometry, and calculating a pose confidence level. The method includes, in response to the confidence level being below a confidence limit, maneuvering the robot to a suspected location of the path segment, based on the calculated robot pose and the data indicative of the layout and, in response to detecting the path segment within a distance from the suspected location, updating the data indicative of the calculated pose and/or the layout.

Abstract: A computer-implemented method for determining a motion trajectory for a mobile robot based on an occupancy prior indicating probabilities of presence of dynamic objects and/or individuals in a map of an environment. Occupancy priors are determined by a reward function defined by reward function parameters. The determining of the reward function parameters includes: providing semantic maps; providing training trajectories for each of semantic maps; computing a gradient as a difference between an expected mean feature count and an empirical mean feature count depending on each of the semantic maps and on each of the training trajectories, the empirical mean feature count is the average number of features accumulated over the provided training trajectories of the semantic maps, wherein the expected mean feature count is the average number of features accumulated by trajectories generated depending on the current reward function parameters; and updating the reward function parameters depending on the gradient.

Abstract: Systems and methods for minimally invasive procedures include a computer-assisted system comprising a manipulator assembly configured to couple to a cannula and a controller coupled to the manipulator assembly. The cannula has a lumen configured to receive a shaft of an instrument. The controller is configured to position a remote center of motion for the manipulator assembly at a first location relative to the cannula, and in response to an indication to reposition the remote center of motion relative to the cannula, reposition the remote center of motion to a second location relative to the cannula while constraining the second location to be located along the cannula. The second location is different from the first location.

Abstract: A robot controlling method includes following operations. A depth image is obtained by the depth camera. A processing circuit receives the depth image and obtains an obstacle parameter of an obstacle and a tool parameter of a tool according to the depth image. The tool is set on the end of a robot. The processing circuit obtains a distance vector between the end and the obstacle parameter. The processing circuit obtains a first endpoint vector and a second endpoint vector between the tool parameter and the obstacle parameter. The processing circuit establishes a virtual torque according to the distance vector, the first endpoint vector, and the second endpoint vector. The processing circuit outputs control signal to the robot according to the tool parameter, the obstacle parameter and the virtual torque to drive the robot to move or rotate the tool to a target.

Abstract: A feature-guided scanning trajectory optimization method for a 3D measurement robot, including: building a 3D digital model of an aircraft surface; obtaining a size of the 3D digital model; extracting features to be measured; classifying the features to be measured; calculating a geometric parameter of each type of features to be measured; generating an initial scanning trajectory of each type of features to be measured; building a constraint model of the 3D measurement robot; optimizing the initial scanning trajectory into a local optimal scanning trajectory; and planning a global optimal scanning trajectory of each type of features to be measured on the aircraft surface by using a modified ant colony optimization algorithm.

Abstract: A vehicle movement assist system includes: an external environment sensor unmovably fixed to a vehicle; and a control device configured to acquire the position of an obstacle relative to the vehicle based on a result of detection performed by the external environment sensor and to execute an automatic moving process to autonomously move the vehicle. During execution of the automatic moving process, if the obstacle is detected in a recognition range set by the control device, the control device performs a notification to the driver and/or executes a process including deceleration or stopping of the vehicle. The control device is configured to change the recognition range in accordance with the travel state of the vehicle.

Abstract: Systems and methods are disclosed comprising a robotic device, an instrument attachable to the robotic device to treat tissue, a vision device attached to the robotic device or instrument, and one or more controllers. The vision device generates vision data sets captured from multiple perspectives of the physical object enabled by the vision device moving in a plurality of degrees of freedom during movement of the robotic device. The controller(s) have at least one processor and are in communication with the vision device. The controller(s) associate a virtual object with the physical object based on one or more features of the physical object identifiable in the vision data sets. The virtual object at least partially defines a virtual boundary defining a constraint on movement of the robotic device relative to the physical object. In some cases, movement of the robotic device is actively constrained by using the virtual boundary.

Abstract: A computer-implemented method, when executed by data processing hardware of a robot having an articulated arm and a base, causes data processing hardware to perform operations. The operations include determining a first location of a workspace of the articulated arm associated with a current base configuration of the base of the robot. The operations also include receiving a task request defining a task for the robot to perform outside of the workspace of the articulated arm at the first location. The operations also include generating base parameters associated with the task request. The operations further include instructing, using the generated base parameters, the base of the robot to move from the current base configuration to an anticipatory base configuration.

Abstract: A method of predicting occupancy of unseen areas in a region of interest (ROI) includes obtaining a depth image of the ROI, the depth image being captured from a first height; generating an occupancy map based on the obtained depth image, the occupancy map comprising an array of cells corresponding to locations in the ROI; and generating an inpainted map by inputting the occupancy map into a trained inpainting network, the inpainted map comprising an array of cells corresponding to the ROI, and wherein the inpainting network is trained by comparing an output of the inpainting network, based on inputting a training depth image taken from the first height, to a ground truth map, the ground truth map being based on a combination of the training depth image and a depth image taken at a height different than the first height.

Abstract: A vehicle system includes: a vehicle platform including a first computer that controls traveling of a vehicle; an automated-driving platform including a second computer that controls automated-driving of the vehicle; a first network connecting the vehicle platform and the automated-driving platform; a second network connecting the vehicle platform and the automated-driving platform; a main power supply that supplies electricity required for communication in the first network; a sub-power supply that supplies electricity required for communication in the second network; and a communication interface that allows communication between the vehicle platform and the automated-driving platform to be performed by using any one of the first network and the second network.

Abstract: Robotic systems can be capable of collision detection and avoidance. A robotic medical system can include a robotic arm, an input device configured to receive one or more user inputs for controlling the robotic arm, and a display configured to provide information related to the robotic medical system. The display can include a first icon that is representative of the robotic arm and includes at least a first state and a second state. The robotic medical system can be configured to control movement of the robotic arm based on the user inputs received at the input device in real time, determine a distance between the robotic arm and a component, and provide information to the user about potential, near, and/or actual collisions between the arm and the component.

Abstract: Systems, methods, and apparatuses for vehicle localization. The vehicle can include a data processing system (“DPS”) including one or more processors and memory. The DPS can receive sensor data from sensors of the vehicle. The DPS can identify a historical road profile of the ground for a first location of the vehicle. The DPS can generate a current road profile of the ground. The DPS can determine a lateral deviation of the vehicle. The DPS can determine a match between the historical road profile and the current road profile at a second location that aligns with the lateral deviation. The DPS can provide an indication of a current location of the vehicle as the second location.

Abstract: Techniques are disclosed to use robotic system simulation to control a robotic system. In various embodiments, a communication indicating an action to be performed by a robotic element is received from a robotic control system. Performance of the action by the robotic element is simulated. A state tracking data is updated to reflect a virtual change to one or more state variables as a result of simulated performance of the action. Successful completion of the action by the robotic element is reported to the robotic control system.

Abstract: A mobile robot may include a traveling unit configured to move a main body; a memory configured to store trajectory information of a moving path corresponding to the movement of the main body; a communication unit configured to communicate with another mobile robot that emits a signal; and a controller configured to recognize the location of the another mobile robot based on the signal, and control the another mobile robot to follow a moving path corresponding to the stored trajectory information based on the recognized location. In addition, the controller may control the moving of the another mobile robot to remove at least part of the stored trajectory information, and allow the another mobile robot to follow a moving path corresponding to the remaining trajectory information in response to whether the moving path corresponding to next trajectory information to be followed by the another mobile robot satisfies a specified condition.

Abstract: A robot control system includes circuitry configured to: determine a necessity of assisting a robot to complete an automated work, based on environment information of the robot; select a remote operator from candidate remote operators based on stored operator data in response to determining that it is necessary to assist the robot to complete the automated work; transmit the environment information to the selected remote operator via a communication network; receive an operation instruction based on the environment information from the selected remote operator via the communication network; and control the robot to complete the automated work based on the operation instruction.

Abstract: A method of transporting an object using a system comprising a plurality of robots. The method comprises one or more robots of the plurality of robots arranging themselves to each exert a respective transporting force on the object. Each of the one or more robots evaluates whether it satisfies a transport criterion while arranged to exert the respective transporting force on the object. If all of the one or more robots satisfy the transport criterion, the one or more robots exert the respective transporting forces on the object to transport the object towards a destination. At least one of the one or more robots evaluates whether it satisfies the transport criterion based on observations of other robots of the plurality of robots within its vicinity.

Abstract: Embodiments of the present disclosure relates to an electronic device, a vision-based operation method and system for a robot. The electronic device includes at least one processing unit; and a memory coupled to the at least one processing unit and storing computer program instructions therein, the instructions, when executed by the at least one processing unit, causing the electronic device to perform acts including: obtaining an image containing a tool of a robot and an object to be operated by the tool, the image being captured by a camera with a parameter, obtaining a command for operating the tool, the command generated based on the image; and controlling the robot based on the command and the parameter. Embodiments of the present disclosure can greatly improve the accuracy, efficiency and safety of an operation of a robot.