Embodiments of this specification provide a working map construction method and apparatus, a robot, and a storage medium. The method includes: determining a moving path of a robot when the robot moves forward as a first forward moving path; determining, after the robot moves backward, a position of the robot when the robot changes from moving backward to moving forward again as a correction position; determining an auxiliary position on the first forward moving path according to the correction position in a case that the correction position is not on the first forward moving path; and determining a correction path according to the correction position and the auxiliary position, so as to construct a working map of the robot according to the correction path and the first forward moving path.

An autonomous driving system acquires information concerning a vehicle density in an adjacent lane that is adjacent to a lane on which an own vehicle is traveling, when the own vehicle travels on a road having a plurality of lanes. The autonomous driving system selects the adjacent lane as an own vehicle travel lane, when the vehicle density in the adjacent lane that is calculated from the acquired information is lower than a threshold density that is determined in accordance with relations between the own vehicle and surrounding vehicles. The autonomous driving system performs lane change to the adjacent lane autonomously, or propose lane change to the adjacent lane to a driver, when the adjacent lane is selected as the own vehicle travel lane.

A retrofit kit for a power machine can include a detection module configured to be removably secured to the power machine to detect objects around the power machine. A control module can be configured to receive detected object data from the detection module and control the display module based on the detected object data to provide one or more indicators of an object detected by the detection module.

A method includes identifying, using at least one processor, a first point associated with an uncertain location of an object in a space and a polynomial curve associated with an uncertain location of a feature in the space. The method also includes determining, using the at least one processor, a probabilistic proximity of the object and the feature. The probabilistic proximity is determined by identifying a second point on the polynomial curve, transforming an uncertainty associated with the polynomial curve into an uncertainty associated with the second point, and identifying the probabilistic proximity of the object and the feature using the first and second points and the uncertainty associated with the second point.

A computer, including a processor and a memory, the memory including instructions to be executed by the processor to determine a first action based on inputting sensor data to a deep reinforcement learning neural network and transform the first action to one or more first commands. One or more second commands can be determined by inputting the one or more first commands to control barrier functions and transforming the one or more second commands to a second action. A reward function can be determined by comparing the second action to the first action. The one or more second commands can be output.

Aspects of the disclosure provide for generating distributions for hypothetical or potentially occluded objects. For instance, a location for which to generate one or more distributions may be identified. Observations of road users by perception systems of a plurality of autonomous vehicles may be accessed. Each of these observations may identify a characteristic of one of the road users. A distribution of the characteristic for the location may be determined based on the observations. The distribution may be provided to one or more autonomous vehicles in order to enable the one or more autonomous vehicles to use the distribution to generate a characteristic for a hypothetical occluded road user and to respond to the hypothetical occluded road user.

Proactively mitigating risk to a vehicle traversing a vehicle transportation network is described. First and second hazard zones for first and second objects ahead of the vehicle are respectively determined. The first hazard zone includes a first target lateral constraint that extends over a left lane boundary, and the second hazard zone includes a second target lateral constraint that extends over a right lane boundary. The lateral constraints separately allow the vehicle to avoid the objects without a speed constraint. Where the first and second hazard zones overlap in the longitudinal direction, a lateral buffer is allocated between the lateral constraints to generate first and second allocated lateral constraints. Longitudinal constraints are respectively determined based on times of arrival at each hazard zone. Using the constraints, a proactive trajectory is determined that includes a lateral contingency, a longitudinal contingency, or both. The vehicle is controlled according to the trajectory.

A method of controlling movement of multi-vehicle includes acquiring a constraint condition under which vehicles move and a calculation cycle for calculating movement routes of the vehicles; acquiring a position of each vehicle; specifying a target position for each vehicle; calculating, based on the position of each vehicle, the target position, and the constraint condition, a movement route for prediction steps of each vehicle; determining, based on the movement routes of the vehicles, a driving condition of each vehicle from a current time to a unit time; and controlling movement of each vehicle. Calculating the movement route including performing optimization calculation based on an evaluation function, evaluation of which becomes higher as a deviation between the vehicle and the target position for each prediction step becomes smaller, and the constraint condition, to calculate the movement route.

A mobile robot operation method according to an aspect of the present invention includes: a step for receiving a guidance destination input; a step for generating a global path to the received guidance destination; a step for generating a left travel guideline and a right travel guideline on the left side and the right side of the generated global path; and a step for generating a local path within a travelable range between the left travel guideline and the right travel guideline. Accordingly, the robot operation method may generate a safe and optimal guidance path when providing a guidance service.

A modular mobility base for a modular autonomous bot apparatus transporting an item being shipped including a mobile base platform, a component alignment interface, a mobility controller, a propulsion and steering system, and sensors. The component alignment interface provides an alignment channel into which another modular component can be placed and secured on the platform. The mobility controller generates propulsion control signals for controlling speed of the modular mobility base and steering control signals for navigation of the modular mobility base. The propulsion system is connected to the platform and responsive to the propulsion control signal. The steering system is connected to the mobile base platform and is responsive to the steering control signal to cause changes to directional movement of the modular mobility base. The sensors are disposed on the platform provide feedback sensor data to the mobility controller about a condition of the modular mobility base.