An autonomous mobile robot control method and apparatus, a device and a readable storage medium. An autonomous mobile robot determines a sound source direction according to a voice signal from a user, and determines moving objects around the autonomous mobile robot itself. The autonomous mobile robot determines, from the moving objects, a target object located in the sound source direction, determines a working area according to the target object, and moves to the working area, and executes a task.

Techniques and methods for securing vehicle systems. For instance, an authorization system may store data representing frequencies at which destination locations are associated pick-up locations for a fleet of autonomous vehicles. The authorization system may then receive a request for an autonomous vehicle to pick up a passenger at a first location and drop off the passenger at a second location. Based on the first location and the second location, the authorization system may determine a frequency for the request. The authorization system may then determine whether a control system for the fleet of autonomous vehicles is compromised based on whether the frequency is less than or equal to a threshold frequency. If the authorization system determines that the control system is compromised, the authorization system may perform a remedial action, such as notifying a teleoperator.

A method and system to improve autonomous robotic system responsive behavior, to control the autonomous responsive behavior of a robotic system based on a set of simultaneously and cooperatively performed real-time action based on a plurality of acquisition sources providing relevant data about the surroundings of the system, wherein the data is processed by a set of modules globally controlled and managed by a Central Processing Module comprising a multitude of control and decision AI algorithms multidirectionally that allow define the autonomous responsive behaviors of the robotic system.

Automatic private public transport system, comprising a plurality of vehicles, operating by optoguidance along a colored strip, each vehicle incorporating an inertial unit, capable of managing all the parameters associated with the movements of a vehicle, namely: a starting point, the direction, accelerations, durations, as well as all the successive variations of these different parameters, the processing of the aforementioned data enables the on-board computer to calculate and define the vehicle's route and to store it, the said route data reproducing a succession of points on the said route, i.e. a virtual computer image of the colored strip of the route travelled. In this way, the on-board computer uses the virtual computer strip to continue its journey if the colored strip is no longer visible.

Aspects of the subject disclosure may include, for example, a method in which a processing system determines that performance of a service is required at a first location in a smart community; identifies a robot to be transported, and determines whether physical transport or virtual transport is to be performed for the robot. If physical transport is to be performed, the system initiates communication with a robot transport controller to schedule the physical transport to the first location from a different second location. If virtual transport is to be performed, the processing system initiates communication with a radio access network (RAN) to schedule the virtual transport; the virtual transport includes configuring the robot at the first location. Other embodiments are disclosed.

The present disclosure provides a general purpose operating system (GPROS) that shows particular usefulness in the robotics and automation fields. The operating system provides individual services and the combination and interconnections of such services using built-in service extensions, built-in completely configurable generic services, and ways to plug in additional service extensions to yield a comprehensive and cohesive framework for developing, configuring, assembling, constructing, deploying, and managing robotics and/or automation applications. The disclosure includes GPROS extensions and features directed to use as an autonomous vehicle operating system. The vehicle controlled by appropriate versions of the GPROS can include unmanned ground vehicle (UGV) applications such as a driverless or self-driving car. The vehicle can likewise or instead include an unmanned aerial vehicle (UAV) such as a helicopter or drone. In cases, the vehicle can include an unmanned underwater vehicle (UUV), such as a submarine or other submersible.

A vehicle routing system includes a vehicle routing and analytics (VRA) computing device, one or more databases, and one or more vehicles communicatively coupled to the VRA computing device. The VRA computing device is configured to generate an optimal route for a vehicle to travel that maximizes potential revenue for operation of the vehicle, the optimal route including a schedule of a plurality of tasks, and generate analytics associated with operation of the vehicle. The VRA computing device is further configured to provide a management hub software application accessible by vehicle users associated with vehicles, tasks sources, and other users.

A model for semantic navigation for service robots to find out-of-view objects in an indoor environment is provided. Initially, the system receives a target object to be reached by the mobile robot in the indoor environment. Further, a current location of the mobile robot is identified by a localization technique. An embedding corresponding to each of a plurality of visible regions is computed using a pretrained Graph Neural Network GNN. The GNN is pretrained using a trajectory data and a spatial relationship graph associated with the indoor environment. Further, a similarity score is computed for each of the plurality of visible regions based on the corresponding embedding using a scoring technique. An optimal visible region is identified by comparing the similarity score. Finally, a next action to be taken by the mobile robot selected from a plurality of actions based on the optimal visible region.

A system for identifying an aspect of interest on a vehicle that includes a local AI system that can analyze sensor data from an on-site sensor to make an attempt to identify the aspect of interest according to first criterion. The aspect of interest can be information printed on the vehicle and/or on a seal of the vehicle. If the local AI system is unable to identify and validate the information on the first effort, it can consult with a central/global AI system that can leverage its own database and other local systems at other locations for subsequent attempts at identifying and validating the aspects of interest.

Systems and methods described herein are directed to an environment involving a plurality of robots, wherein for receipt of a plurality of orders, the systems and methods generate a plurality of task batches to fulfill the plurality of orders; generate a parameter set for execution by the plurality of robots to execute the plurality of task batches. For a determination by a controller that one or more of the plurality of robots is to execute the plurality of task batches, the systems and methods load the parameter set; and control the one or more of the plurality of robots based on the loaded parameter set to execute the task batch.