360 results about "Fleet management" patented technology

An autonomous robotic vehicle with dynamic path planning capabilities, including: a dynamic path planning module configured to generate and adapt navigation paths in real time based on environmental changes and mission objectives; a sensor fusion module that integrates data from multiple sensors, including LiDAR, radar, cameras, ultrasonic sensors and GPS, to create a comprehensive environmental model; an obstacle avoidance module capable of detecting and predicting both stationary and moving obstacles and modifying the navigation path to avoid collisions; an artificial intelligence-based decision-making module for analyzing environmental data and learning from past navigation scenarios for improved performance; a decentralized control module that enables the independent operation of the vehicle in areas with interrupted or no connection to a central server; an energy management module configured to optimize battery usage by dynamically adjusting operating parameters based on real-time data and mission requirements; a cooperation module that enables communication and coordination between multiple autonomous robot vehicles for fleet management or the synchronized execution of tasks; a fail-safe module to ensure uninterrupted operation and data security in the event of communication failures or hardware malfunctions.

A system receives vehicle metric data from a gateway device connected to a vehicle. The vehicle gateway device gathers data related to operation of the vehicle and / or location data. The system receives data from multiple vehicles in a fleet. The vehicle gateway device gathers vehicle metric data at a high frequency. Instead of transmitting a large amount of vehicle metric data at a fine level of granularity, the vehicle gateway device aggregates and buckets the vehicle metric data over a period of time (such as, every five minutes). The system uses the bucketed data for fleet management analysis.

In an implementation of a method of operation of a robot fleet management system, the robot fleet management system accesses a set of tasks available to be performed by a fleet of robots, accesses a respective power consumption for each task from the set of tasks, and accesses a respective power state of each robot in the fleet. The robot fleet management system allocates a selected robot to a selected task, based at least in part on the power state of at least the selected robot and the power consumption for at least the selected task. The power consumption may be determined by the robot fleet management system and / or be provided by the task provider. The set of tasks includes tethered and untethered tasks. The robot fleet management system allocates the selected robot to an untethered task after determining the selected robot has sufficient power to complete the untethered task.

In one embodiment, a method herein comprises: registering, by an orchestration service agent, with a central management device to receive a corresponding configuration for the orchestration service agent, wherein the central management device receives a registration from each of a plurality of orchestration service agents; self-configuring, by the orchestration service agent, based on the corresponding configuration; obtaining, by the orchestration service agent and from the central management device, an observability-agent-specific configuration package; and configuring, by the orchestration service agent and based on the observability-agent-specific configuration package, a corresponding observability agent associated with the orchestration service agent and executing on a corresponding entity.

Leveraging a large language model (‘LLM’) for managing a fleet of storage systems, including: receiving, by a fleet management service, data describing states of a plurality of storage systems in a fleet of storage systems; requesting, by the fleet management service and from a large language model (LLM), a fleet management recommendation based on the data; and receiving, by the fleet management service and from the LLM, a fleet management recommendation for the fleet of storage systems.

A robot fleet management platform includes a job configuration system that determines tasks to be performed by robots of a robot fleet based on a job request and a first fleet objective. A proxy service applies fleet configuration services to the tasks to produce a data structure. An intelligence layer activates intelligence services to produce a robot task and associated contextual information that facilitates robot selection and task ordering. A job workflow system generates a workflow defining a performance order of the tasks. A workflow simulation system simulates performance of the job request based on the workflow to recursively redefine the tasks, the data structure, or the workflow until the simulation result satisfies a second fleet objective. In response to the simulation result satisfying the set of fleet objectives, a plan generator generates a job execution plan based on the set of robot tasks, the data structure, and the workflow.

The disclosed system for testing of fleet management software generates virtual assets (e.g., simulations of machinery) that are associated with simulated sensor values. For at least one particular asset in the set, the system generates a simulated application programming interface (API) message definition structured to capture the simulated sensor values. The system binds the set of virtual assets to a simulation scenario record that includes a trigger condition. The system causes a scheduler to detect that the trigger condition is met, fetch the simulation scenario record, and generate simulated API messages for the assets according to the API message definitions.

The collection, communication, storage, monitoring, and analysis of environmental, vehicle, charger, and charging session data over a single EV or over a fleet of EVs is used to enhance or optimize existing services as well as deliver new services based on analysis of the collected data. One such service is reduction in total cost of travel. The cost minimization is achieved with near-real-time data, models populated using past collected data, and knowledge of energy costs and of chargers located in different geographic areas with potentially limited power for charging available at each charger site or station. A fleet management application used for cost minimization optimizes cost of travel for a single EV or for an entire fleet. Using individual EVs cost optimization as a goal, each EV may be optimized for potential local minimum cost of travel based on predictions using data collected from the fleet or from multiple fleets.

Techniques for host fleet management in a cloud provider network are described. Forecast data including a forecasted demand for virtual machines in each capacity pool of a set of capacity pools is obtained. A mathematical optimizer application is executed to generate a first optimal fleet plan, the mathematical optimizer application having an objective function to minimize a number of new host computer systems to add to the set of host computer systems to satisfy the forecasted demand for each capacity pool, the first optimal fleet plan includes an identification of a set of hardware types and, for each hardware type in the set, a quantity of new host computer systems of the hardware type. A plurality of new host computer systems is deployed, based on the first optimal fleet plan, for a hardware type in the set of hardware types into the set of host computer systems.

The invention discloses a double-flywheel system and an intelligent mine field system and method.The double-flywheel system comprises a sensing system and a planning system, the sensing system fuses acquired multi-dimensional environment data of a mine field through the bird's-eye view angle technology and a deep learning algorithm, a mine field 3D environment model is established, and environment sensing data is output; the planning system performs planning path prediction on each mine car by using a deep learning algorithm and a Monte Carlo tree search algorithm based on vehicle management information, task scheduling data and environment perception data fed back by the motorcade management platform, and generates an optimal driving path. The vehicle management information comprises vehicle posture and health information, vehicle position state information, vehicle operation task priority information and mine field safety standard information. According to the invention, through deep cooperation of sensing and planning and integration of motorcade management information and safety standards, the vehicles can work according to the optimal path more accurately, the working yield of the mine field is improved, and the development of the intelligent mine field is promoted.

Systems and methods are provided for improved fleet management. For example, disclosed embodiments access toll transaction data received from one or more toll agencies, normalize the toll transaction data from each toll agency, access telematic data, normalize the telematic data, and generate a correlated trip dataset comprising the normalized toll transaction data and the normalized telematic data. Based on the correlated trip dataset, systems are configured to calculate a net present toll value, identify toll bill errors, identify pre-violations, and identify vehicle fraud and abuse.

A relative positioning method is combined with the known absolute pose of a primary vehicle to provide the absolute position and / or pose of the secondary vehicle wherein the secondary vehicle is only equipped with a non-contact relative positioning system. Thereby allowing for numerous advantages such as fleet management, safe vehicle interactions, and further automation at a fraction of the cost of using a sensor suite commonly required to calculate the absolute pose of the secondary vehicle.

Techniques are presented for the detection and management of collision warning (CW) events. A training dataset comprising videos of vehicle collisions and non-collisions, sensor readings, environmental conditions, and more is utilized to train a CW classification model for detecting potential collision events in vehicles. A backend CW classification model, with greater computational resources, employs a more complex neural network to review CW events received by the Behavioral Monitoring System (BMS) based on video data, achieving higher precision and reducing false positives. The CW model is installed in vehicles for real-time detection, while the backend model is deployed at the BMS. The BMS validates detected CW events, filters out false positives, and streamlines the review process for fleet administrators and customers. Additional BMS filtering operations include assessing non-proximity-related CW events and camera impairments, with the filtered CW events presented for review in the safety inbox.

A process can include receiving monitoring information associated with a machine learning (ML) or artificial intelligence (AI) workload implemented by an edge compute unit of a plurality of edge compute units. Status information corresponding to a plurality of connected edge assets can be received, the plurality of edge compute units and connected edge assets included in a fleet of edge devices. A remote fleet management graphical user interface (GUI) can display a portion of the monitoring or status information for a subset of the fleet of edge devices, based on a user selection input, and can receive a user configuration input indicative of an updated configuration for at least one workload corresponding to a pre-trained ML or AI model deployed on the at least one edge compute unit. A cloud computing environment can transmit control information corresponding to the updated configuration to the at least one edge compute unit.

The present disclosure provides methods, systems, and devices for controlling electric vehicle charging across multiple customers and multiple fleets of electric vehicles. These methods, systems, and devices may implement machine learning to determine distinct charging strategies for a plurality of charging depots. Scheduling methods systems, and devices disclosed herein do not require fixed electric vehicle arrival times but may instead update charging strategies in real time based on changes in a state of a system.

A vehicle includes a chassis, a cab, a drum coupled to the chassis and configured to mix a concrete mixture received therein and selectively dispense the concrete mixture, a chute configured to be operable between a raised position and a lowered position such that, when in the lowered position, the chute is configured to receive the concrete mixture from the drum and provide the concrete mixture to a work location, a sensor configured to detect an operational characteristic and provide signals relating to the operational characteristics, and a control system. The control system is configured to receive the signals relating to the operational characteristic from the sensor, determine, based on signals relating to the operational characteristic, when the vehicle entered an operational state, generate a timestamp indicating when the vehicle entered the operational state, provide the timestamp and the operational state to a fleet management system.

An autonomous warehouse robotics system integrates a multi-sensor platform, adaptive payload handling, dynamic task reallocation, advanced navigation, energy management, and comprehensive safety features into one mobile robot chassis. The system utilizes LiDAR, stereo vision, ultrasonic sensors, mmWave radar, thermal cameras, and event cameras to perform complete environmental sensing and obstacle detection. Sensor fusion combines adaptive weighting, multi-modal data integration, and statistical filtering to create high-confidence maps for reactive path planning and collision avoidance. The robot's payload system features machine vision for item recognition, telescopic lifts, variable-width grippers, and real-time toolhead verification to handle a variety of goods. Fleet management is achieved through dynamic task reallocation that considers robot location, battery level, and operational delays, all coordinated by a cross-platform middleware architecture that ensures standardized communication, remote monitoring, and over-the-air updates among diverse robot brands. Energy management optimizes power usage via predictive routing, autonomous return-to-charge, and auction-based scheduling. Safety is maintained through proximity detection, behavior-based intervention, and human-robot cohabitation protocols while advanced localization is enhanced by fusing ultra-wideband positioning with visual landmark alignment, inertial sensing, and machine learning to deliver high accuracy in non-line-of-sight conditions. An onboard edge AI module further refines navigation and task prioritization through neural network inference, ensuring robust, adaptive operation in dynamic, unstructured warehouse environments.

A system for electric aircraft fleet management for at least an electric aircraft is provided. The system includes a computing device communicatively connected to at least an electric aircraft, wherein the computing device is configured to receive measured data from the at least an electric aircraft, generate the aircraft performance model output as a function of the measured data, transmit the aircraft performance model output to a user, receive a flight plan from the user; and manage the at least an electric aircraft using the flight plan.

A system includes a fleet resources data store that maintains a fleet resource inventory indicating fleet resources that can be assigned to perform tasks. For each fleet resource, the inventory indicates features of each fleet resource and a respective status. A set of task definitions is accessible to an intelligence layer to facilitate improving task definition based on feedback from task-specific outcomes. The system receives a job request for a robotic fleet to perform a job and determines a job definition data structure indicating a set of tasks to be performed for the job. The system applies an outcome of performing a task by a resource assigned to perform the task to a machine learning system of the intelligence layer that facilitates improving, based on the outcome, the set of task definitions. The system updates the set of task definitions based on a result of applying the machine learning system.

A fleet management system will receive, from each vehicle of a fleet of vehicles, a vehicle identification number (VIN). The system also will receive data elements that comprise: (a) vehicle operational parameters gathered during a run of the vehicle; (b) a hardware identification code that identifies a hardware component of the vehicle; and / or (c) a software identification code that identifies an installed software component in the vehicle. The processor will generate a data block that comprises the VIN and the one or more data elements. The processor will then save the data block to a shared digital ledger that includes VINs and data elements for a plurality of the vehicles in the fleet.

A product delivery system includes a product transport vehicle having at least a plurality of tank compartments, internal valves, control valves, fluid property sensors, temperature sensors, overfill sensors, pressure sensors, a system controller, and a memory module. The product delivery system may be operable to communicatively connect with the loading system at a loading station, a fleet management system, or a cloud system to obtain information, such as the amount of liquid product to be loaded into a tank compartment. The product delivery system may be operable to compare the volumes of the tank compartments with the amounts of liquid product to be loaded at a loading station and maintain the internal valve or control valve in a locked state or transition the internal valve or control valve to an unlocked state based on the comparison. Other comparisons based on liquid type or distribution tank volume can also be made.

The present disclosure generally relates to identification and prioritization of autonomous vehicle (AV) data collection and data offload. In some aspects, the present disclosure provides a process for receiving one or more data collection triggers from an AV fleet management system, wherein the one or more triggers identify at least one condition that triggers collection of AV data by an AV; compiling the AV data in response to detecting the at least one condition identified by the one or more data collection triggers; determining a set of priorities that are associated with the one or more data collection triggers, wherein the set of priorities includes at least one instruction for prioritizing transmission of the AV data to the AV fleet management system; and transmitting, based on the set of priorities, the AV data to the AV fleet management system.

The invention provides a commercial vehicle abnormal energy consumption identification system and method based on Internet of Vehicles multi-dimensional data. The commercial vehicle abnormal energy consumption identification system based on Internet of Vehicles multi-dimensional data comprises a data acquisition module, a data processing module, an abnormal energy consumption identification module, an abnormal energy consumption reason diagnosis module and a pushing module. The commercial vehicle abnormal energy consumption identification method based on the Internet of Vehicles multi-dimensional data comprises the following steps: S1, collecting vehicle data and carrying out preprocessing; s2, abnormal energy consumption identification is carried out based on the vehicle data, and high-energy-consumption vehicles are screened out; s3, diagnosing the cause of high energy consumption; s4, pushing a diagnosis result; according to the method, comprehensive energy consumption analysis is carried out on multi-dimensional data such as vehicle states, driving tracks and driving behaviors, multi-level similarity analysis is carried out on similar vehicles, routes and road characteristics, high-energy-consumption vehicles are recognized more accurately, and errors of a single standard are avoided; abnormal energy consumption reasons can be accurately and timely diagnosed, motorcade management is optimized, and operation cost is reduced.

The disclosed system for testing of fleet management software generates virtual assets (e.g., simulations of machinery) that are associated with simulated sensor values. For at least one particular asset in the set, the system generates simulated application programming interface (API) messages. For at least one simulated API message in the simulated API message set, the system parses, from the simulated API message, simulated message data that relates to message flow through integration points. The system generates a visualization that includes an indication of system latency for messages flowing through the integration points. The system latency is determined based on the simulated message data.

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.

The invention belongs to the field of automobile intelligent management, and particularly relates to a vehicle tree interface optimization method for motorcade management, which comprises the following steps of: 1, constructing a vehicle tree data structure, and storing all vehicle node objects in a dictionary; 2, various vehicle states, a state display priority rule, a state propagation rule and a local sorting rule are defined, and according to the state propagation rule, when the states of vehicle nodes change, state change information is propagated upwards step by step along the recursive nested data structure; step 3, when the vehicle node changes, generating a local refreshing instruction according to the vehicle node change information, updating statistical information according to a state propagation rule, and notifying a UI layer to refresh through an agent mechanism; and step 4, dynamic unfolding and folding of grouping nodes, rapid searching and screening of vehicles and groups and batch management operation are realized based on a multi-stage TableView auxiliary class. The problems of low vehicle state refreshing efficiency and low display sorting efficiency when the data volume is large are solved.

Provided herein is an autonomous or semi-autonomous vehicle fleet comprising a plurality of electric autonomous vehicles for apportioned display of a media, operating autonomously and a fleet management module for coordination of the autonomous vehicle fleet. Each autonomous or semi-autonomous vehicle comprising a screen configured to display the media. Activation, deactivation, brightness modification, in combination with specific media selection enables more efficient media display.

Systems and methods for using risk profiles for fleet management of a fleet of vehicles are disclosed. Fleet management may include determining the performance levels of particular vehicle operators. The risk profiles characterize values representing likelihoods of occurrences of vehicle events. The values are based on vehicle event information for previously detected vehicle events. Exemplary implementations may: receive, from a particular vehicle, particular vehicle event information for particular vehicle events that have been detected by the particular vehicle; determine one or more metrics that quantify a performance level of the particular vehicle operator, based on the risk profiles; compare the one or more metrics for the particular vehicle operator with aggregated metrics that quantify performance levels of a set of vehicle operators; and store, transfer, and / or present results of the comparison.

The invention relates to a vehicle active safety early warning device and early warning method based on a call center, and the early warning device comprises a vehicle-mounted equipment module, a data analysis server, and a call center module. The vehicle-mounted equipment module is composed of an MCU control unit, a GNSS positioning module, a driver visual recognition module, a lateral blind area early warning module, a forward collision early warning module and a 4G communication module. A vehicle positioning database, a vehicle early warning database, a data analysis engine and an Internet of Vehicles data gateway are installed in the data analysis server; the call center module is composed of a telephone traffic center server SIP switch and a mobile terminal. The vehicle-mounted equipment module is connected with a vehicle networking data gateway of the data analysis server through the 4G communication module, and the data analysis server is connected with a telephone traffic center server of the call center module through an intranet communication network cable. The system has the advantages that the real-time monitoring and active early warning of the vehicle safety state are realized, the motorcade management efficiency is improved, and the driving safety is guaranteed.