Disclosed is a robot cleaner including a light source for irradiating light, a sensor for sensing that the light irradiated from the light source is reflected, and a controller that processes an image using the light sensed by the sensor to calculate a distance value of an individual location of the corresponding image, wherein it is determined that there is an obstacle when there is a dead zone in the image processed by the controller.

A system and method for an on-demand shuttle, bus, or taxi service able to operate on private and public roads provides situational awareness and confidence displays. The shuttle may include ISO 26262 Level 4 or Level 5 functionality and can vary the route dynamically on-demand, and/or follow a predefined route or virtual rail. The shuttle is able to stop at any predetermined station along the route. The system allows passengers to request rides and interact with the system via a variety of interfaces, including without limitation a mobile device, desktop computer, or kiosks. Each shuttle preferably includes an in-vehicle controller, which preferably is an AI Supercomputer designed and optimized for autonomous vehicle functionality, with computer vision, deep learning, and real time ray tracing accelerators. An AI Dispatcher performs AI simulations to optimize system performance according to operator-specified system parameters.

An AGV navigation device is provided, which includes a RGB-D camera, a plurality of sensors and a processor. When an AGV moves along a target route having a plurality of paths, the RGB-D camera captures the depth and color image data of each path. The sensors (including an IMU and a rotary encoder) record the acceleration, the moving speed, the direction, the rotation angle and the moving distance of the AGV moving along each path. The processor generates training data according to the depth image data, the color image data, the accelerations, the moving speeds, the directions, the moving distances and the rotation angles, and inputs the training data into a machine learning model for deep learning in order to generate a training result. Therefore, the AGV navigation device can realize automatic navigation for AGVs without any positioning technology, so can reduce the cost of automatic navigation technologies.

In various examples, a current claimed set of points representative of a volume in an environment occupied by a vehicle at a time may be determined. A vehicle-occupied trajectory and at least one object-occupied trajectory may be generated at the time. An intersection between the vehicle-occupied trajectory and an object-occupied trajectory may be determined based at least in part on comparing the vehicle-occupied trajectory to the object-occupied trajectory. Based on the intersection, the vehicle may then execute the first safety procedure or an alternative procedure that, when implemented by the vehicle when the object implements the second safety procedure, is determined to have a lesser likelihood of incurring a collision between the vehicle and the object than the first safety procedure.

A docking station for a robotic cleaner may include a housing, at least one charging contact coupled to the housing, and at least three optical emitters disposed within the housing. The at least three optical emitters may include a first optical emitter configured to generate a first optical signal within a first field of emission, a second optical emitter configured to generate a second optical signal within a second field of emission, and a third optical emitter configured to generate a third optical signal within a third field of emission. The third optical emitter may be disposed between the first and second optical emitters. The first, second, and third optical signals may be different from each other. The third optical signal may be configured to guide a robotic cleaner in a direction of the housing.

A method for operating a track guidance system including at least one raised floor element includes planning at least one movement of at least one object on the at least one floor element, transmitting at least one control signal for carrying out the planned at least one movement to the object with the aid of an activatable marking on the at least one floor element, and carrying out the planned at least one movement of the at least one object with the aid of an activatable marking on the at least one floor element based upon the at least one control signal.

An autonomous mower including a housing; a mowing module, a traveling module, an information collection device, an energy module, a control module and the control module includes an identification unit, and an alarm module. The autonomous mower has a security patrol working mode in which the identification unit analyzes and judges whether an abnormal object exists in the working area according to the information collected by the information collection device; if the abnormal object exists, the control module controls the alarm module to send an alarm signal to the outside. Therefore, the autonomous mower can achieve a security patrol function in addition to having a function of trimming the lawn. The autonomous mower has multiple uses, so as to save the cost.

A vehicle computing system may identify a scenario in an environment that violates an operating constraint. The vehicle computing system may request remote guidance from a guidance system of a service computing device. The vehicle computing system may receive input from the guidance system including one or more waypoints and/or associated orientations for the vehicle to navigate through the scenario. The vehicle computing system may be configured to validate the input. A validation may include processing the input to determine whether the waypoint(s) and/or orientation(s) associated therewith may cause the vehicle to violate a safety protocol. Based on a determination that the input will not cause the vehicle to violate the safety protocol, the vehicle computing system may control the vehicle according to the input, such as by causing a drive system to operate the vehicle to each waypoint at the associated orientation.

The present disclosure provides a method for controlling a robot cleaner including a travel operation in which the robot cleaner travels, a recognition operation in which when the robot cleaner contacts an obstacle during the travel, the robot cleaner determines whether the obstacle is pushed by the robot cleaner and slides, and an obstacle bypass operation in which upon determination that the obstacle is the pushed-and-sliding obstacle, the robot cleaner stops the travel and then bypasses the pushed-and-sliding obstacle.

Disclosed is a method for controlling a robot cleaner including acquiring, by a camera, an image, irradiating, by a light source, light toward a location the same as a location where the acquired image is captured, receiving, by a sensor, the light irradiated from the light source and reflected on an object, processing an image received from the sensor to contain a distance value of an individual location, and supplementing the image received from the sensor with the image captured by the camera when a singularity is found, wherein distance values calculated in adjacent portions are discontinuous at the singularity.