A method includes maneuvering a robot in (i) a following mode in which the robot is controlled to travel along a path segment adjacent an obstacle, while recording data indicative of the path segment, and (ii) in a coverage mode in which the robot is controlled to traverse an area. The method includes generating data indicative of a layout of the area, updating data indicative of a calculated robot pose based at least on odometry, and calculating a pose confidence level. The method includes, in response to the confidence level being below a confidence limit, maneuvering the robot to a suspected location of the path segment, based on the calculated robot pose and the data indicative of the layout and, in response to detecting the path segment within a distance from the suspected location, updating the data indicative of the calculated pose and/or the layout.

Disclosed herein is a robot generating a map based on multi sensors and artificial intelligence and moving based on the map, the robot according to an embodiment including a controller generating a pose graph that includes a LiDAR branch including one or more LiDAR frames, a visual branch including one or more visual frames, and a backbone including two or more frame nodes registered with any one or more of the LiDAR frames or the visual frames, and generating orodometry information that is generated while the robot is moving between the frame nodes.

Disclosed herein is a robot generating a map based on multi sensors and artificial intelligence and moving based on the map, the robot according to an embodiment including a controller generating a pose graph that includes a LiDAR branch including one or more LiDAR frames, a visual branch including one or more visual frames, and a backbone including two or more frame nodes registered with any one or more of the LiDAR frames or the visual frames, and generating orodometry information that is generated while the robot is moving between the frame nodes.

A system of determining a flight path for an aerial vehicle, includes a memory storing a program code, and a processor configured to execute the program code to identify, during a flight, an abnormal state occurring at a first location, in response to the abnormal state, control the aerial vehicle to fly along a first flight path from the first location to a first destination, after the aerial vehicle arrives at the first destination, evaluate a status of the aerial vehicle at the first destination to obtain an evaluation result, and based on the evaluation result, determine a second flight path of the aerial vehicle to a second destination. The first flight path is a reverse of a last flight path of the aerial vehicle before the aerial vehicle reaches the first location.

A method for traveling down a prescribed arrangement of paths which are connected to one another at nodes with a mobile robot. The robot changes from an initial route, which contains all as yet untraveled paths, to a different replacement route including a loop route which retakes at least one path and at least one further path, and a subsequent remaining route which contains all as yet untraveled paths at that time if a value of a quality function for the replacement route is lower than a value of this quality function for the initial route. The quality function is dependent on a first effort, a second effort, and a variable weighting of the first and second values in relation to one another. The variable weighting weights the second effort lower for a first localization uncertainty of the robot.

An autonomous vehicle includes one or more sensors for detecting an object in an environment surrounding the autonomous vehicle and a vehicle computing system comprising one or more processors receiving canonical route data associated with at least one canonical route, and controlling travel of the autonomous vehicle based on sensor data from the one or more sensors and the canonical route data associated with the at least one canonical route. The at least one canonical route comprises at least one roadway connected with another roadway in a plurality of roadways in a geographic location that satisfies at least one route optimization function derived based on trip data associated with one or more traversals of the plurality of roadways in a geographic location by one or more autonomous vehicles.

The present disclosure provides a neural network-based method for calibration and localization of an indoor inspection robot. The method includes the following steps: presetting positions for N label signal sources capable of transmitting radio frequency (RF) signals; computing an actual path of the robot according to numbers of signal labels received at different moments; computing positional information moved by the robot at a t.sup.th moment, and computing a predicted path at the t.sup.th moment according to the positional information; establishing an odometry error model with the neural network and training the odometry error model; and inputting the predicted path at the t.sup.th moment to a well-trained odometry error model to obtain an optimized predicted path. The present disclosure maximizes the localization accuracy for the indoor robot by minimizing the error of the odometer with the odometry calibration method.

This disclosure details exemplary portable power systems for vehicles. The portable power systems may be configured as a secondary battery pack that is removably stored within a front trunk of a vehicle. The portable power systems may include a plurality of individually removable battery units that can be used to extend vehicle driving range or power electrically powered devices that are separate from the vehicle. In some embodiments, when the portable power system is stored in the vehicle and an electrically powered device is connected to the portable power system, the vehicle may be controlled in a Following Mode in which the vehicle is autonomously moved to follow an operator of the electrically powered device.

The present disclosure provides systems and methods that control the motion of an autonomous vehicle by rewarding or otherwise encouraging progress toward a goal, rather than simply rewarding distance traveled. In particular, the systems and methods of the present disclosure can project a candidate motion plan that describes a proposed motion path for the autonomous vehicle onto a nominal pathway to determine a projected distance associated with the candidate motion plan. The systems and methods of the present disclosure can use the projected distance to evaluate a reward function that provides a reward that is positively correlated to the magnitude of the projected distance. The motion of the vehicle can be controlled based on the reward value provided by the reward function. For example, the candidate motion plan can be selected for implementation or revised based at least in part on the determined reward value.

The invention relates to a system and method for navigating an autonomous driving vehicle (ADV) that utilizes an-onboard computer and/or one or more ADV control system nodes in an ADV network platform. The on-board computer receives battery monitoring and management data concerning a battery stack. The on-board computer, utilizing a battery management system, determines the current state of charge (SOC) and other information concerning the battery stack and determines if the estimated total amount of electrical power required to navigate an ADV along a generated route to reach the predetermined destination is available. In response to determining that the ADV cannot reach the predetermined destination, the on-board computer automatically initiates a dynamic routing algorithm, which utilizes artificial intelligence, to generate alternative routes in an effort to find a route that the ADV can navigate to reach the destination utilizing the current state of charge (SOC) of the battery stack.