A flight control system for a rotary-wing aircraft includes a shape sensor and a controller. The shape sensor is configured to measure a shape of a rotor blade during movement of the rotor blade. The controller is communicably coupled to the shape sensor and is configured to (i) receive, from the shape sensor, a first signal indicative of a first blade shape; (ii) receive a blade characteristic regarding the rotor blade; and (iii) determine at least one of a moment or force associated with the rotor blade based on the first signal and the blade characteristic.

An obstacle positioning method, device and terminal are provided. The method includes determining installation positions of at least two detectors on a vehicle, and respective detection areas of the detectors, determining an overlapping area of the detection areas of the detectors, and if determining that an obstacle is located in the overlapping area, determining a position of the obstacle according to the installation positions of the detectors forming the overlapping area. By changing the number and positions of detectors installed on an unmanned vehicle, a plurality of overlapping areas of the detection areas of the detectors are obtained, the distribution of obstacles around the vehicle are optimally identified, so that the unmanned vehicle makes reasonable driving plans based on an accurate surrounding obstacle environment.

Technologies for managing a world model of a monitored area includes a roadway server configured to receive LIDAR sensing data from a LIDAR sensing system positioned to monitor the monitored area and generate a world map of the monitored area based on the LIDAR sensing data. The world model includes data that identifies objects located in the monitored area. The roadway server may distribute the world model to automated vehicles traveling through the monitored area via a stream or in response to directed requests. The roadway server may also receive sensor data from the automated vehicles, which may be used to generate the world model. The roadway server may distribute the world model to other interested devices located in or near the monitored area.

Technologies for managing a world model of a monitored area includes a roadway server configured to receive LIDAR sensing data from a LIDAR sensing system positioned to monitor the monitored area and generate a world map of the monitored area based on the LIDAR sensing data. The world model includes data that identifies objects located in the monitored area. The roadway server may distribute the world model to automated vehicles traveling through the monitored area via a stream or in response to directed requests. The roadway server may also receive sensor data from the automated vehicles, which may be used to generate the world model. The roadway server may distribute the world model to other interested devices located in or near the monitored area.

Systems and methods for imaging may include: receiving a first point cloud from a first LiDAR sensor mounted at a first location behind a vehicle grille, the first point cloud representing the scene in front of the vehicle, wherein as a result of the grille the scene represented by the first point cloud data is partially occluded with a first pattern of occlusion; receiving a second point cloud from a second LiDAR sensor mounted at a second location behind the vehicle grille; combining the first and second point clouds to generate a composite point cloud data set, wherein the first LiDAR sensor is located relative to the second LiDAR sensor such that when a point cloud data for the first optical sensor and the second optical sensor are combined, the first pattern of occlusion is at least partially compensated; and processing the combined point cloud data set.

A process for sensing a scene. The process includes receiving sensor data from a plurality of sensor modalities, where each sensor modality observes at least a portion of the scene containing at least one of the objects of interest and generates sensor data conveying information on the scene and of the object of interest. The process further includes processing the sensor data from each sensor modality to detect objects of interest and produce a plurality of primary detection results, each detection result being associated with a respective sensor modality. The process also includes fusing sensor data from a first sensor modality with sensor data from a second sensor modality to generate a fused 3D map of the scene, processing the fused 3D map to detect objects of interest and produce secondary detection results and performing object level fusion on the primary and the secondary detection results.

In general, the present disclosure is directed to a time of flight (ToF) sensor arrangement that may be utilized by a robot (e.g., a robot vacuum) to identify and detect objects in a surrounding environment for mapping and localization purposes. In an embodiment, a robot is disclosed that includes a plurality of ToF sensors disposed about a housing of the robot. Two or more ToF sensors may be angled/aligned to establish overlapping field of views to form redundant detection regions around the robot. Objects that appear therein may then be detected by the robot and utilized to positively identify, e.g., with a high degree of confidence, the presence of the object. The identified objects may then be utilized as data points by the robot to build/update a map. The identified objects may also be utilized during pose routines that allow the robot to orient itself within the map.

In general, the present disclosure is directed to a time of flight (ToF) sensor arrangement that may be utilized by a robot (e.g., a robot vacuum) to identify and detect objects in a surrounding environment for mapping and localization purposes. In an embodiment, a robot is disclosed that includes a plurality of ToF sensors disposed about a housing of the robot. Two or more ToF sensors may be angled/aligned to establish overlapping field of views to form redundant detection regions around the robot. Objects that appear therein may then be detected by the robot and utilized to positively identify, e.g., with a high degree of confidence, the presence of the object. The identified objects may then be utilized as data points by the robot to build/update a map. The identified objects may also be utilized during pose routines that allow the robot to orient itself within the map.

The present disclosure provides a hypertube system for detecting a position of a hypertube vehicle, including a hypertube vehicle, a tube configured to surround a travel path of the hypertube vehicle, At least one LiDAR sensor each mounted on an inner wall of the tube and including a laser transmitter configured to irradiate a laser beam toward the hypertube vehicle and a laser receiver configured to detect a laser, and a reflector configured to reflect the laser irradiated from the LiDAR sensor, wherein the reflector may be disposed in the hypertube vehicle, and wherein the laser beam reflected from the reflector reaches the laser receiver of the LiDAR sensor to be used in detecting the position of the hypertube vehicle.

A portable sensor system is provided for detecting objects in the environment of the portable sensor system and for being temporarily attached to a mobile object. The portable sensor system includes an environment detection sensor for detecting objects, an attaching device connected to the environment detection sensor for temporarily attaching the portable sensor system to an external surface of the mobile object, and a position determining apparatus for determining a sensor position of the environment detection sensor when the environment detection sensor is attached to the external surface. The position determining apparatus is configured to determine the sensor position relative to the mobile object on the basis of a predetermined geometrical model of the external surface and a region of the external surface detected by the environment detection sensor.