Abstract: A network computing system can coordinate on-demand transport serviced by transport providers operating throughout a transport service region. The transport providers can comprise a set of internal autonomous vehicles (AVs) and a set of third-party AVs. The system can receive a transport request from a requesting user of the transport service region, where the transport request indicates a pick-up location and a destination. The system can determine a subset of the transport providers to service the respective transport request, and executing a selection process among the subset of the transport providers to select a transport provider to service the transport request. The system may then transmit a transport assignment to the selected transport provider to cause the selected transport provider to service the transport request.

Abstract: A method for autonomously servicing a first cleaning component of a battery-operated mobile device, including: inferring, with a processor of the mobile device, a value of at least one environmental characteristic based on sensor data captured by a sensor disposed on the mobile device; actuating, with a controller of the mobile device, a first actuator interacting with the first cleaning component to at least one of: turn on, turn off, reverse direction, and increase or decrease in speed such that the first cleaning component engages or disengages based on the value of at least one environmental characteristic or at least one user input received by an application of a smartphone paired with the mobile device; and dispensing, by a maintenance station, water from a clean water container of the maintenance station for washing the first cleaning component when the mobile device is docked at the maintenance station.

Abstract: A robot cleaner and a control method thereof are disclosed. A robot cleaner, according to an embodiment of the present invention, comprises: a tilt sensor module; a distance sensor module; and a light amount sensor module. Each piece of sensed information is transmitted to a lifting information calculation module. The lifting information calculation module calculates information on whether the robot cleaner is lifted, by using each piece of the transmitted information. If it is calculated that the robot cleaner is lifted off a floor, a power module is stopped. In addition, if it is calculated that the robot cleaner is not lifted off the floor, the power module is driven. Accordingly, when a user lifts the robot cleaner, injuries to the user caused by operation of a drive module can be prevented.

Abstract: An automatic working system that includes a self-moving device and a positioning device. The self-moving device includes a movement module, a task execution module. The positioning device is configured to detect a current position of the self-moving device. The automatic working system includes: a storage unit, configured to store a working area map, and: a map confirmation procedure, which including: providing a drive circuit instruction to move along a working area boundary, and receiving a confirmation signal from a user to complete the map confirmation procedure; and a working procedure including providing a drive circuit instruction to move within a working area defined by the map and execute the working task; and a control module, configured to monitor an output of the positioning device to execute the map confirmation procedure and execute the working procedure after the map confirmation procedure is completed.

Abstract: A method for autonomously planning work duties of a robot within an environment of the robot, including: obtaining, with a processor of the robot, first data indicative of a presence or an absence of at least one human within the environment at a particular time; actuating, with the processor, the robot to execute work duties based on the first data, wherein: the robot executes the work duties when the first data indicates the absence of the at least one human from the environment; and the work duties comprise cleaning at least a portion of the environment; capturing, with at least one sensor of a plurality of sensors disposed on the robot, second data while the robot executes the work duties; and altering, with the processor, a navigational route of the robot or the work duties based on the second data.

Abstract: Operating an autonomous grounds maintenance machine includes determining a travel path for the machine to reach a destination waypoint in a zone of a work region. The travel path is analyzed for problem areas based on a predetermined terrain map. Rotations of the machine may be planned even before the machine begins to travel down the path to proactively prevent the machine from becoming trapped.

Abstract: An apparatus for generating a lane network includes a processor configured to calculate, for each of individual points in a predetermined region connecting an entry lane and an exit lane, a probability distribution indicating a probability that the point lies on a standard trajectory, based on trajectories of one or more vehicles that traveled through the region, generate two virtual borders each connecting the positions of respective edges of the entry lane and the exit lane, set, of the individual points in the predetermined region, at least two points that lie between the two virtual borders and at which the variance of the probability distribution is not greater than a predetermined variance threshold, as control points, and generate a Bézier curve, based on the at least two control points, as a line connecting two lanes in the predetermined region in a lane network.

Abstract: In various examples, an end-to-end perception evaluation system for autonomous and semi-autonomous machine applications may be implemented to evaluate how the accuracy or precision of outputs of machine learning models—such as deep neural networks (DNNs)—impact downstream performance of the machine when relied upon. For example, decisions computed by the system using ground truth output types may be compared to decisions computed by the system using the perception outputs. As a result, discrepancies in downstream decision making of the system between the ground truth information and the perception information may be evaluated to either aid in updating or retraining of the machine learning model or aid in generating more accurate or precise ground truth information.

Abstract: The present invention relates to an autonomous omnidirectional drive unit including a chassis (40) defining a front third (41), which contains a first drive wheel (11) with a first gearbox (13), a second drive wheel (21) with a second gearbox (23) coaxial with a horizontal geometric axis (EH), and a battery (30) between same, an intermediate third (42) which contains a first motor (12) and a second motor (22) connected to the first and second drive wheels (11, 21) and a vertical housing (70) between same concentric with a vertical geometric axis (EV); and a rear third which contains at least one caster wheel (50), the first and second motors (12, 22) being controlled by a control device (80) which generates control commands considering the distance (D) existing between the horizontal and vertical geometric axes (EH, EV).

Abstract: A method for preprocessing a set of feasible transfers within a multimodal transportation network of predetermined stations, comprising, for each trip in the multimodal transportation network hereafter called origin trip: (a) for each station (pti) of the origin trip (t), computing at this station (pti) an earliest arrival/change time associated with all transportation modes (m) of the multimodal transportation network; (b) for at least one transfer of the set of feasible transfers from a station (pti) on the origin trip (t) to a reachable station (puj) on a target trip (u), computing, at each station (puk>j) of the target trip (u) after the reachable station (puj), a value of the earliest arrival/change time specifically associated with the transportation mode (mu) of the multimodal transportation network used by the target trip (u); (c) removing the transfer only if determining that each computed value of the earliest arrival/change time is not improved by the transfer; (d) outputting the set of feasible

Abstract: Provided are an information processing device and a mobile robot capable of ensuring the accuracy of estimation of a self-position even with an environmental change and maintaining the consistency of the self-position on a map. The information processing device of the mobile robot includes a control section and a detection section configured to detect a distance to a peripheral object and the direction thereof as detection information. The control section includes a storage, a map producer configured to produce a peripheral map, an existing map self-position estimator configured to estimate a current self-position based on an existing map, a current map self-position estimator configured to estimate the current self-position based on a current map, a reliability evaluator configured to evaluate the reliability of each of the estimated self-positions, and a self-position updater configured to update either one of the self-positions based on the reliability.

Abstract: System, methods, and computer-readable media for an object path prediction model, and an associated training technique, to output a path that is considered an object collision path. The object path prediction model outputs a set of predicted paths for the object that are outputted to a trained planning algorithm, which includes paths that are most likely to occur and a path that is considered an object collision path. The predicted paths are sent to the trained planning algorithm and used for planning a trajectory for the autonomous vehicle that is associated with a low probability of colliding with or taking a sudden evasive action to avoid the object.

Abstract: A system and method for providing long term and key intentions for trajectory prediction that include receiving image data and LiDAR data associated with RGB images and LiDAR point clouds that are associated with a surrounding environment of an ego agent and processing a long term and key intentions for trajectory prediction dataset (LOKI dataset) that is utilized to complete joint trajectory and intention prediction for heterogeneous traffic agents. The system and method also include encoding a past observation history of each of the heterogeneous traffic agents and sampling a respective goal. The system and method further include decoding and predicting future trajectories associated with each of the heterogeneous traffic agents based on data included within the LOKI dataset, the encoded past observation history, and the respective goal.

Abstract: A method and an apparatus for generating sensor data for controlling an autonomous vehicle in an environment is provided, such as driverless transport vehicles in a factory for example. Sensor positions of static sensors and the sensors of autonomous vehicles are defined in a global coordinate system on the basis of an environment model, such as a BIM model for example. Sensor data is centrally generated in this global coordinate system for all sensors as a function of these sensor positions. The sensor data is then transformed into a local coordinate system of an autonomous vehicle and transferred for controlling the autonomous vehicle.

Abstract: A system for context-aware decision making of an autonomous agent includes a computing system having a context selector and a map. A method for context-aware decision making of an autonomous agent includes receiving a set of inputs, determining a context associated with an autonomous agent based on the set of inputs, and optionally any or all of: labeling a map; selecting a learning module (context-specific learning module) based on the context; defining an action space based on the learning module; selecting an action from the action space; planning a trajectory based on the action S260; and/or any other suitable processes.

Abstract: A platooning control apparatus may include: a navigation unit configured to guide an ego vehicle to a destination set by a driver; a driving module configured to drive the ego vehicle; and a control unit configured to primarily select platooning groups based on the destination set in the navigation unit, analyze platooning information of the primarily selected platooning groups, finally decide any one of the primarily selected platooning groups, and then control the driving module to join the finally decided platooning group.

Abstract: Techniques are disclosed for utilizing synchronized robot observations. The techniques may include receiving first log data associated with a first vehicle, receiving second log data associated with a second vehicle, determining that a first portion of the first log data is time related to a second portion of the second log data, and using the first portion and the second portion for mapping and/or training models.

Abstract: To infer dynamic control information on a controlled object. An inferring device includes one or more memories and one or more processors. The one or more processors are configured to: input at least data about a state of a controlled object and time-series control information for controlling the controlled object, into a network trained by machine learning; acquire predicted data about a future state of the controlled object controlled based on the time-series control information via the network into which the data about the state of the controlled object and the time-series control information have been input; and output new time-series control information for controlling the controlled object to bring the future state of the controlled object into a target state based on the predicted data acquired via the network.

Abstract: A self-position estimation apparatus comprises a camera part; an environmental data storage part that stores information that changes according to the position of a mobile object on a travel route in association with the position information thereof; a first estimation part that estimates the position of mobile object from sensor data containing information that changes according to the position of mobile object and the information in the environmental data storage part; a second estimation part that estimates the self-position of mobile object from a known object included in an image inputted from the camera part; a weighting determination part that determines weighting to the position estimation results by the first estimation part and the second estimation part; and a self-position calculation part that calculates a self-position by linearly combining the self-positions estimated by the first estimation part and the second estimation part using the result from the weighting determination part.

Abstract: An object assessment device determines whether or not an object is present within a predetermined range, and has a processor configured to determine whether or not first object detection information detected at a first detection time point and second object detection information detected at a second detection time point after the first detection time point, are detecting a same object, determine whether or not a predetermined region including a location of the vehicle at the second detection time point satisfies predetermined terrain condition, when it has been determined that the same object has not been detected, determine that the object is present within the predetermined range from the vehicle, when it has been determined that the same object has been detected, or when it has been determined that the predetermined region including the location of the vehicle satisfies the predetermined terrain condition, and give notification of the assessment result.

Abstract: A vehicle controller includes a processor configured to determine whether to transfer driving control of a vehicle under autonomous driving control to a driver, notify the driver of a transition demand via a notification device in the case where driving control will be transferred to the driver, execute deceleration control of the vehicle at predetermined timing after notification timing of the transition demand, and continue deceleration control of the vehicle even after a predetermined period from the notification timing in the case where the speed of the vehicle decreases to a predetermined speed threshold or less within the predetermined period and where operation by the driver to take over driving is not detected.

Abstract: Autonomous carriers or totes that include vacuum units are provided. As the totes move or are moved through a warehouse carrying products, they collect debris. The debris can be analyzed at the tote, and actions can be performed based upon the analysis.

Abstract: A system may include a display and a processor communicatively coupled to the display. The processor may be configured to: output, to the display, a view of an airport moving map (AMM); receive aircraft state data and airport surface data; at least based on the current position of the aircraft and at least one factor, obtain commonly used taxi route data from a data structure, the data structure including taxi route data, the taxi route data including information of commonly used taxi routes for the airport, wherein the commonly used taxi route data includes information of a commonly used taxi route for the aircraft on the airport surface; based at least on the commonly used taxi route data, the aircraft state data, and the airport surface data, generate taxi routing guidance content; and output, to the display, the taxi routing guidance content.

Abstract: A parking assistance method includes: detecting, from an image of surroundings of a vehicle, a first target object position; detecting a first image capturing situation when the image is captured; with respect to a second target object position of a target object detected from a past image of surroundings of a target parking position in a past, retrieving one or more combinations of a relative positional relationship between the second target object position and the target parking position and a second image capturing situation when the past image is captured; selecting, the relative positional relationship combined with the second image capturing situation having a difference from the first image capturing situation less than or equal to a predetermined difference; and calculating, based on the selected relative positional relationship and the first target object position, a relative position between a current position of the vehicle and the target parking position.

Abstract: A hardware processor of a mobile object executes the program stored in a storage device to acquire information indicating a road situation in a traveling direction of a mobile object; to recognize whether the mobile object is moving on a roadway or a predetermined region different from the roadway; to recognize presence of a contact portion between the sidewalk and the predetermined region in the traveling direction of the mobile object; to control the speed of the mobile object at least partially, and limit a speed at which the mobile object is moving on the roadway to a first speed and limit a speed at which the mobile object is moving on a sidewalk to a second speed slower than the first speed; and to bring a speed of the mobile object closer to the second speed when the mobile object is moving on the roadway, the contact portion is recognized within a predetermined range from the mobile object, and the road situation is in a predetermined state.

Abstract: A control method for a self-moving device includes: obtaining a first working time corresponding to an unworked area in a target working area when a current remaining power of the self-moving device meets a preset charging condition, the target working area being an area where the self-moving device works; determining a first power consumption required by the unworked area according to the first working time; and charging the self-moving device according to the first power consumption. A control apparatus for implementing the control method for the self-moving device is also disclosed.

Abstract: Techniques for item delivery and delivery systems including autonomous delivery vehicles used to identify optimal delivery locations and/or deliver physical items to pedestrians is discussed herein. In some examples, a fleet management system may receive sensor data captured from sensor devices of vehicles of a fleet of vehicles while traversing within an environment. The fleet management system may use the sensor data to identify region(s) of the environment within which a threshold number of pedestrians may be located. Based on identifying a region having a threshold number of pedestrians, the fleet management system may determine how many vehicles of the fleet of vehicles are available to render delivery services to the pedestrians within the region. In some examples, the fleet management system may identify an available vehicle of the fleet of vehicles to which instructions may be sent. Such instructions may include transporting items to pedestrians within the region.

Abstract: A travel route determination system including: a route generating unit generating planned travel routes including work routes along which a work vehicle performs autonomous travel; a control unit capable of causing the work vehicle to perform autonomous travel along the planned travel routes; an information obtaining unit obtaining position information and orientation information on the work vehicle; and a determination unit determining an autonomous travel candidate route at which the work vehicle can start autonomous travel, before the work vehicle starts autonomous travel. The determination unit sets a candidate determination region based on the position information and orientation information on the work vehicle, and the determination unit determines, among the work routes, a work route in the candidate determination region as the autonomous travel candidate route.

Abstract: An automatic guided vehicle system includes an automatic guided vehicle and a docking station for docking with the automatic guided vehicle. One of the automatic guided vehicle and the docking station has a pair of first vertical guide surfaces and the other has a pair of rows of rollers. The first vertical guide surfaces extend a predetermined length along a first horizontal direction and are spaced by a predetermined distance in a second horizontal direction perpendicular to the first horizontal direction. The rows of rollers are in rolling contact with the first vertical guide surfaces to guide the automatic guided vehicle to a predetermined position in the docking station.

Abstract: A vehicle control system for an agricultural vehicle comprising a processing circuit including a processor and memory, the memory having instructions stored thereon that, when executed by the processor, cause the processing circuit to receive position information associated with a position of at least one of a second agricultural vehicle or a vehicular implement, determine, based on the position information, an unloading point associated with a position of an unloading mechanism relative to a receiving container, store the determined position, operate at least one of the agricultural vehicle or the second agricultural vehicle to position the receiving container such that a subsequent unloading point is the same as the determined unloading point.

Abstract: A vehicle parking assist apparatus acquires and registers first information on a parking lot from a camera image when a vehicle stops by the parking lot. The vehicle parking assist apparatus acquires and registers second information on the parking lot from the camera image when completing parking the vehicle in the parking lot by a parking assist control. The vehicle parking assist apparatus acquires the information on the parking lot as current parking lot information from the camera image when the vehicle stops by the parking lot after registering the first and second parking lot information and executes the parking assist control with realizing a relationship in position between the vehicle and the parking lot by comparing the current parking lot information with the registered parking lot information.

Abstract: A method of operating a mobile robot includes generating a segmentation map defining respective regions of a surface based on occupancy data that is collected by a mobile robot responsive to navigation of the surface, identifying sub-regions of at least one of the respective regions as non-clutter and clutter areas, and computing a coverage pattern based on identification of the sub-regions. The coverage pattern indicates a sequence for navigation of the non-clutter and clutter areas, and is provided to the mobile robot. Responsive to the coverage pattern, the mobile robot sequentially navigates the non-clutter and clutter areas of the at least one of the respective regions of the surface in the sequence indicated by the coverage pattern. Related methods, computing devices, and computer program products are also discussed.

Abstract: There are included an own vehicle position sensor which detects the position of an own vehicle; a reference path acquisition unit which acquires a reference path on which the own vehicle travels; a reference path determination unit which determines whether or not the own vehicle is traveling on the reference path; a temporary vehicle path calculation unit which, when it is determined by the reference path determination unit that the own vehicle is not traveling on the reference path, calculates a temporary vehicle path from at least two points, a travel start point of the own vehicle and an end point of the reference path; and a vehicle path setting unit which sets either the temporary vehicle path calculated by the temporary vehicle path calculation unit or the reference path on which it is determined by the reference path determination unit that the own vehicle is traveling as the vehicle path of the own vehicle.

Abstract: A self-propelled floor processing device with an evaluation unit, which is designed to navigate the floor processing device within an environment based on an area map and, during a movement, to determine a behavior parameter of the floor processing device and a movement path of the floor processing device. The evaluation unit is set up to analyze the behavior parameter and movement path, automatically determine a no-go area which the floor processing device must not traverse depending on the result of the analysis, and enter the determined no-go area in the area map or change a no-go area already entered in the area map.

Abstract: Techniques for dividing a geographical area into districts are described herein. Geospatial vector data, barrier geospatial vector data, road infrastructure data, and historical delivery demand data for a geographical area may be obtained. A plurality of clusters from a stratified sampling of data points for the delivery demand data and barrier penalties from a barrier-aware road graph are generated. A first set of polygons for the plurality of clusters may be generated using a concave hull algorithm. A second set of polygons may be generated using a barrier constrained network Voronoi algorithm that uses the barrier-aware road graph and the first set of polygons as seeds. The second set of polygons may be modified using a bounded Voronoi algorithm that uses a raster cost allocation based on barrier penalties. Coordinates for each polygon of the modified second set of polygons are determined that divide the geographical area.

Abstract: A robotic device including one or more proximity sensing systems coupled to various portions of a robot body. The proximity sensing systems detect a distance of an object about the robot body and the robotic device reacts based on the detected distance. The proximity sensing systems obtain a three-dimensional (3D) profile of the object to determine a category of the object. The distance of the object is detected multiple times in a sequence to determine a movement path of the object.

Abstract: According to the present invention, a moving body capable of moving within a preset range of movement acquires an image from the environment surrounding the location of the moving body, and determines entry determination conditions for obtaining a result of an entry determination as to whether or not the moving body is allowed to enter a region identified by the location of the moving body and the image. A management terminal outputs the entry determination conditions and the image to an output device, and receives modification information relating to the entry determination conditions from an input device. A server uses the modification information received by the input device of the management terminal to perform learning, including updating of the entry determination conditions, thereby setting the movement range of the moving body, the movement of which is controlled in accordance with the entry determination result.

Abstract: The route generation device includes a controller configured to generate, in response to acceptance of an allowable arrival time period assigned to at least one of the plurality of destinations, an input of information regarding specifics of a task to be performed at the at least one destination, and setting of a required time period according to the specifics of the task, a route that allows the autonomous mobile body to arrive at the at least one destination within the allowable arrival time period, based on the required time period. The controller is configured to control and cause the autonomous mobile body to travel along the generated route.

Abstract: A method for 3D object detection is described. The method includes predicting, using a trained monocular depth network, an estimated monocular input depth map of a monocular image of a video stream and an estimated depth uncertainty map associated with the estimated monocular input depth map. The method also includes feeding back a depth uncertainty regression loss associated with the estimated monocular input depth map during training of the trained monocular depth network to update the estimated monocular input depth map. The method further includes detecting 3D objects from a 3D point cloud computed from the estimated monocular input depth map based on seed positions selected from the 3D point cloud and the estimated depth uncertainty map. The method also includes selecting 3D bounding boxes of the 3D objects detected from the 3D point cloud based on the seed positions and an aggregated depth uncertainty.

Abstract: A robotic work tool system (200) for defining a stay-out area (120) within a work area (150). The stay-out area (120) is an area which is to be excluded from the work area (150) in which a robotic work tool (100) is subsequently intended to operate. The robotic work tool system (200) comprises a boundary definition unit (130) comprising at least one position unit (175) configured to receive position data and at least one controller (110, 210). The controller (110, 210) is configured to receive a stay-out area definition trigger signal, which indicates that the boundary definition unit (130) has approached the stay-out area to be defined. The controller (110, 210) is further configured to receive, based on the received signal, position data indicating the present position of the boundary definition unit (130) from the position unit (175). The controller (110, 210) is further configured to define the stay-out area (120) as an area centered at an offset from the received position data.

Abstract: In a method for storing and picking stackable article carriers in an order fulfillment facility, in which transport loading aids are transported between a loading station with an automatically operated loading device, a storage zone and a removal station with an automatically operated unloading device by autonomously moveable, driverless transport vehicles, the transport loading aids are loaded with article carrier stacks in the loading station and the article carrier stacks are discharged from the transport loading aids in the unloading station. Subsequently, the article carriers for a picking order are reloaded onto one or multiple target loading aids. Further, an order fulfillment facility stores and stackable article carriers.

Abstract: Aspects of the disclosure relate to providing information about a vehicle dispatched to pick up the user. In one example, a request for the vehicle to stop at a particular location is sent. In response, information identifying a current location of the vehicle is received. A map is generated. The map includes a first marker identifying the received location of the vehicle, a second marker identifying the particular location, and a shape defining an area around the second marker at which the vehicle may stop. The shape has an edge at least a minimum distance greater than zero from the second area. A route is displayed on the map between the first marker and the shape such that the route ends at the shape and does not reach the second marker. Progress of the vehicle towards the area along the route is displayed based on received updated location information for the vehicle.

Abstract: A robot controller includes: axis motor control units that control motors for driving axes of a robot; and an action command generation unit that generates a first action command having the shortest action time when the robot is moved from an action start point to an action goal point without considering an obstacle, and selects, from among the axes, a major axis having the longest action time when the action is performed in accordance with the first action command. The first action command includes another axis command, and a major axis command, and the action command generation unit adjusts the other axis command so as to reduce an action time according to the other axis command and outputs a second action command including the major axis command and the adjusted other axis command and corresponding to a first trajectory when determining that the first trajectory avoids a clash between the robot and the obstacle.

Abstract: A method for controlling motion of a vehicle, the method comprising the steps of: obtaining input information on a vector related to the velocity of said vehicle; computing repeatably a future trajectory of said vehicle based on said input information and trial torques to be applied to at least one wheel of said vehicle for optimizing said future trajectory in view of a target vehicle motion, thereby obtaining target trial torques; and applying the obtained target trial torques to the at least one wheel for controlling the motion of said vehicle.

Abstract: The present invention provides a guide display device capable of recognizing a feature in a work area while distinguishing whether the feature is a moving body or not. In a guide display device of a crane, 3D maps are created for continuous frames; an altitude value for each grid in a 3D map that is created at time closest to the current time is obtained as a first altitude value; altitude values for respective corresponding grids in a predetermined number of 3D maps other than the 3D map that is created at time closest to the current time are obtained, and the average of the altitude values is calculated as a second altitude value; and it is determined that the feature is a moving body when the difference between the first altitude value and the second altitude value exceeds a predetermined threshold.

Abstract: An autonomous unmanned aerial vehicle detecting system for monitoring a geographic area includes an unmanned blimp adapted to hover in air, at least one camera mounted on the blimp to scan at least a portion of the geographic area, a location sensor to determine a location of the blimp, and a controller arranged in communication with blimp, the at least one camera, and the location sensor. The controller is configured to position the blimp at a desired location in the air based on inputs received from the location sensor, and monitor the geographic area based on the images received from at least one camera. The controller is also configured to detect a presence of an unmanned aerial vehicle within the geographic area based on the received images, and determine whether the detected unmanned aerial vehicle is an unauthorized unmanned aerial vehicle based on the received images.

Abstract: The disclosure relates to a method for controlling a robot to return to a base. The method includes the robot receives a return-to-base control signal; the robot walks along different preset paths according to different receiving conditions of guidance signals; and when detecting an intermediate signal during walking along a preset path, the robot returns to the base directly under piloting of the intermediate signal instead of walking along the preset path. The guidance signals are signals sent by a charging base, for piloting the robot to return to the base, and include the intermediate signal.

Abstract: Methods, systems, and computer-readable media are disclosed herein that generate computer-executable instructions that are executed by an autonomous vehicle and cause the autonomous vehicle to follow a specific route to deliver or pickup of an item. Using an inference model, historical data of off-street terrain used for prior deliveries and on-street terrain in map data are leveraged to generate candidate routes for the “last 10 feet” of a delivery. One of the candidate routes is selected by the inference model. Then, computer-executable instructions are generated that, when executed by an autonomous vehicle, cause the autonomous vehicle to follow the selected route and perform the last 10 feet of delivery.

Abstract: A computer includes a processor and a memory, and the memory stores instructions executable by the processor to receive a set of points indicating a lane boundary; select a first subset of the points, wherein the set of points includes a first point that is not in the first subset; determine a path of the lane boundary based on the first subset of the points; and, upon determining that the first point is greater than a threshold distance from the path, add the first point to the first subset.

Abstract: The present invention provides specific systems, methods and algorithms based on artificial intelligence expert system technology for determination of preferred routes of travel for electric vehicles (EVs). The systems, methods and algorithms provide such route guidance for battery-operated EVs in-route to a desired destination, but lacking sufficient battery energy to reach the destination from the current location of the EV. The systems and methods of the present invention disclose use of one or more specifically programmed computer machines with artificial intelligence expert system battery energy management and navigation route control. Such specifically programmed computer machines may be located in the EV and/or cloud-based or remote computer/data processing systems for the determination of preferred routes of travel, including intermediate stops at designated battery charging or replenishing stations.