Provided are a marker for space recognition, a method of moving and lining up a robot based on space recognition, and a robot, and the robot that lines up and moves based on space recognition includes: a camera sensor that images a marker disposed on a traveling floor of the robot or a side of the traveling floor; and a control unit that analyzes an image captured by the camera sensor, calculates a moving direction or a moving speed of the robot on the basis of the marker, and controls a movement unit.

Provided is a map generation unit that generates an attribute-attached occupancy grid map including an existence probability of an obstacle in a space around a moving body for each grid, and an attribute of the obstacle labelled in the occupancy grid map. A position estimation unit that estimates a position of the moving body by matching in a shape of a non-moving body and the attribute between the attribute-attached occupancy grid map and a pre-map that is the attribute-attached occupancy grid map prepared beforehand.

A device and a method for performing control to change a flexible virtual bumper for maintaining a space between a mobile object and an obstacle to be equal to or larger than a predetermined distance are enabled. A data processing unit that executes control to change the flexible virtual bumper for maintaining the space between the mobile object and the obstacle to be equal to or larger than the predetermined distance, and a drive unit that drives the mobile object in such a way that no obstacle enters the flexible virtual bumper are included. The data processing unit executes control to change the flexible virtual bumper at least either in size or shape. For each one of a plurality of travel route candidates for the mobile object, the data processing unit executes a simulation of changing the bumper size in such a way that no obstacle enters the flexible virtual bumper, and selects a safe travel route.

A system having a guidance module to obtain a travel path from an agricultural machine to a target. The guidance module including a perception module to determine target line that extends from the machine to the target, and obstacle detection module to detect an obstacle in the first target line, the obstacle having an obstacle boundary that intersects the target line. The guidance module including a mitigation module to obtain a mitigation path around the obstacle based on the target line and the obstacle boundary, the mitigation path including a mitigation segment. The mitigation module including an entrance module configured to determine a starting position of the mitigation path based on a second target line and an exit module configured to determine the ending position of the mitigation path based on a third target line. The guidance module including convergence module to combine the mitigation path with the travel path.

A self-moving device includes a body, a walking assembly arranged on the body, and a control system arranged in the body. The self-moving device further includes an optical receiving device and at least two optical emitting devices arranged on the body. Paths of emitted light emitted by the at least two optical emitting devices are different. The optical receiving device is adapted to receive a reflected light formed after the emitted light emitted by at least one of the optical emitting devices hits an obstacle. A distance measuring method of the self-moving device is also disclosed.

Systems and methods provide a travel control system to augment a supplemental object detection system of a material handling vehicle. Speed limits can be calculated for material handling vehicles based on properties of the vehicle, and a field of view of the object detection systems of the material handling vehicle. For given steer angles or ranges of steer angles, the speed of the material handling vehicle can be limited to ensure that the vehicle could stop before contact with a newly-detected object that was previously outside the field of view of the object detection system.

A computer-implemented method when executed by data processing hardware causes the data processing hardware to perform operations. The operations include detecting a candidate support surface at an elevation less than a current surface supporting a legged robot. A determination is made on whether the candidate support surface includes an area of missing terrain data within a portion of an environment surrounding the legged robot, where the area is large enough to receive a touchdown placement for a leg of the legged robot. If missing terrain data is determined, at least a portion of the area of missing terrain data is classified as a no-step region of the candidate support surface. The no-step region indicates a region where the legged robot should avoid touching down a leg of the legged robot.

A system for controlling a forklift truck has several operating modes and allows, in particular, operation in manual mode or autonomous mode. A forklift truck is provided with such a control system.

The disclosure generally relates to autonomous or semi-autonomous driving vehicles. An exemplary embodiment of the disclosure relates to a system to provide one or more threat vectors to a cluster of vehicles. An exemplary vehicle detection system includes a communication module configured to receive a first threat vector from a first vehicle in a cluster of vehicles. The first threat vector may include a plurality of primary attributes associated with a moving object. The vehicle detection system may also include a detector module configured to detect the moving object and to provide one or more secondary attributes associated with the moving object; and a controller to construct a second threat vector as a function of one or more of the first threat vector, the primary attributes and the secondary attributes associated with the moving object.

An ADV may determine whether there is preexisting map data for an environment or geographical area/location where the ADV is located/travelling. If there is no preexisting data, the ADV may generate map data based on sensor data obtained from one or more sensors of the ADV. The ADV may determine a path for the ADV based on the generated map data. If there is preexisting map data, the ADV may determine a path for the ADV based on the preexisting map data.