Provided is a medium storing instructions that when executed by one or more processors of a robot effectuate operations including: obtaining, with a processor, first data indicative of a position of the robot in a workspace; actuating, with the processor, the robot to drive within the workspace to form a map including mapped perimeters that correspond with physical perimeters of the workspace while obtaining, with the processor, second data indicative of displacement of the robot as the robot drives within the workspace; and forming, with the processor, the map of the workspace based on at least some of the first data; wherein: the map of the workspace expands as new first data of the workspace are obtained with the processor; and the robot is paired with an application of a communication device.

A method for a leading transportation vehicle and for taking over control of a transportation vehicle to be controlled, including identifying the transportation vehicle to be controlled; determining a dynamic holding area relative to the leading transportation vehicle for the transportation vehicle to be controlled, wherein the dynamic holding area is defined so that the transportation vehicle to be controlled remains behind the leading transportation vehicle and uses a different lane; and transmitting a message relating to the dynamic holding area to the transportation vehicle to be controlled.

A method for a leading transportation vehicle and for taking over control of a transportation vehicle to be controlled, including identifying the transportation vehicle to be controlled; determining a dynamic holding area relative to the leading transportation vehicle for the transportation vehicle to be controlled, wherein the dynamic holding area is defined so that the transportation vehicle to be controlled remains behind the leading transportation vehicle and uses a different lane; and transmitting a message relating to the dynamic holding area to the transportation vehicle to be controlled.

An autonomous vehicle having sensors advantageously varied in capabilities, advantageously positioned, and advantageously impervious to environmental conditions. A system executing on the autonomous vehicle that can receive a map including, for example, substantially discontinuous surface features along with data from the sensors, create an occupancy grid based upon the map and the data, and change the configuration of the autonomous vehicle based upon the type of surface on which the autonomous vehicle navigates. The device can safely navigate surfaces and surface features, including traversing discontinuous surfaces and other obstacles.

An autonomous vehicle having sensors advantageously varied in capabilities, advantageously positioned, and advantageously impervious to environmental conditions. A system executing on the autonomous vehicle that can receive a map including, for example, substantially discontinuous surface features along with data from the sensors, create an occupancy grid based upon the map and the data, and change the configuration of the autonomous vehicle based upon the type of surface on which the autonomous vehicle navigates. The device can safely navigate surfaces and surface features, including traversing discontinuous surfaces and other obstacles.

Described is a system for training a neural network for estimating surface normals for use in operating an autonomous platform. The system uses a parallelizable k-nearest neighbor sorting algorithm to provide a patch of points, sampled from the point cloud data, as input to the neural network model. The points are transformed from Euclidean coordinates in a Euclidean space to spherical coordinates. A polar angle of a surface normal of the point cloud data is estimated in the spherical coordinates. The trained neural network model is utilized on the autonomous platform, and the estimate of the polar angle of the surface normal is used to guide operation of the autonomous platform within the environment.

A vehicle includes a navigation device, and a controller electrically connected to the navigation device, wherein the controller is configured to determine a first speed of the vehicle based on a main line speed limit of a road on which the vehicle is traveling, wherein the main line speed limit is included in navigation information output by the navigation device, determine a second speed for decelerating the vehicle to a predetermined speed when the vehicle is positioned at a target point of a curved lane based on an arc length from a branching point of the road to the curved lane, which is determined based on the predetermined speed and a predetermined allowable maximum deceleration amount in the curved lane in route information included in the navigation information and the navigation information, and determine the first speed or the second speed as a control target speed of the vehicle at the branching point.

Methods and systems for autonomous vehicle recharging or refueling are disclosed. Autonomous electric vehicles may be automatically recharged by routing the vehicles to available charging stations when not in operation, according to methods described herein. A charge level of the battery of an autonomous electric vehicle may be monitored until it reaches a recharging threshold, at which point an on-board computer may generate a predicted use profile for the vehicle. Based upon the predicted use profile, a time and location for the vehicle to recharge may be determined. In some embodiments, the vehicle may be controlled to automatically travel to a charging station, recharge the battery, and return to its starting location in order to recharge when not in use.

Embodiments herein relate to an autonomous vehicle or self-driving vehicle. The system can determine a collision avoidance path by: 1) predicting the behavior/trajectory of other moving objects (and identifying stationary objects); 2) given the driving trajectory (issued by autonomous driving system) or predicted driving trajectory (human), establishing the probability for a collision that can be calculated between the vehicle and one or more objects; and 3) finding a path to minimize the collision probability.

A method and system for predicting a near future path for a vehicle. For predicting the near future path sensor data and vehicle driving data is collected. Road data is collected indicative of a roadway on the presently occupied road for the vehicle. The sensor data and the vehicle driving data is pre-processed to provide object data comprising a time series of previous positions, headings, and velocities of each of the objects relative the vehicle. The object data, the vehicle driving data, and the road data is processed in a deep neural network to predict the near future path for the vehicle. The invention also relates to a vehicle comprising the system.