117 results about "Fleet management" patented technology

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.

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 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.

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.

A fleet of all-electric, battery powered industrial or commercial mobile power units (MPUs) or equipment is provided. Systems and methods for managing the fleet of equipment is also provided. Observable data, such as i) equipment performance and health data, ii) electricity cost data, iii) telematic data, and iv) scheduling data, can be used by machine learning models to optimize fleet management and decision making. The machine learning model can be configured to generate suggested actions, commands, or decisions which are transmitted to fleet managers who are responsible for activities such as i) equipment charging and maintenance, ii) equipment delivery logistics, and iii) rental pricing strategy. Feedback data from the outcome of these decisions, or the completion of the related activities, can tracked and used to update the machine learning model. Interactive portals may also be utilized by customers to make reservations and managers to manage reservations, monitor the fleet of equipment and provide support to MPUs across the product lifecycle.

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.

A system and method for artificial intelligence enhanced mobile energy and data and network control, planning and optimization. The present invention relates to a system and method for optimizing energy and data and network use in mobile platforms, such as facilities, vehicles, tools, and devices. The system leverages AI, data analytics, and context-aware techniques to collect, process, and analyze data from various sources, including sensors, weather data, and spatial and temporal data with locality aware computing, transport, storage and networking across assets that may be owned or operated by multiple stakeholders. By considering a variety of factors, the system generates optimized recommendations for data storage, compute, transmission, device settings, fleet management, and physical and virtual route planning and logic locality planning. The invention offers benefits, including improved energy efficiency, enhanced data and network management, increased operational efficiency, and cost savings.

Systems, apparatus, and methods for emergency charging. Within the context of an EV fleet depot, the fleet vehicles are typically owned / controlled by a company or organization which has an interest in communal resource allocation. The communal resource management considerations of vehicle fleets create new opportunities for resource allocation which are not readily available to e.g., individual consumers. Various aspects of the present disclosure are directed to shared communication protocols within a fleet of vehicles that facilitate efficient resource allocation. Communication and evaluation may be formalized according to a shared rule set and metrics (promulgated by the fleet authority, etc.). Various aspects of the present disclosure may further extend the concepts of priority and / or severity to site-wide power management. Conceptually, power may be distributed to power a first priority, before distributing the remaining power to the next priority, etc. This isolates the first prioritization level from internal and / or external throttling.

The invention relates to the technical field of motorcade management systems, and discloses a motorcade intelligent scheduling decision-making and monitoring management system and method combined with a car roof holder, and the system comprises a management server and a vehicle-mounted terminal, and the vehicle-mounted terminal collects a space monitoring abnormal value; the management server obtains a motorcade business value cardinal number, a business performance path and a real-time position coordinate, determines a corresponding vehicle business performance rigidity coefficient, and calculates a business performance deviation degree; according to the method, a dynamic adjustment mechanism of a macroscopic business performance state to a bottom layer physical sensing signal is established, ineffective triggering caused by bottom layer environment fluctuation is inhibited when the business state height is determined, and the weight correction parameter of the car roof pan-tilt is generated according to the business performance rigidity coefficient and the business performance deviation degree. Supervision resources are accurately focused on the substantial default risk, and the resource configuration efficiency of whole motorcade scheduling management is improved.

An automatic personal enhancement system that is tailored to each user's special access needs. The system having a multimedia presentation device, an onsite node having a user protocol time code message protocol, and the system sending a real-time time-code to the system, over a private LAN, a transmission device to transmit information to the onsite node, a fleet management subsystem, a content database server, a smart device, and a web server. The web server having instructions for: identifying a user entering a venue; connecting to the show control subsystem; selecting a language for multimedia pretensions from the show control subsystem; tracking the user's smart device with the personal enhancement system for automatically playing multimedia associated with the location in the venue in proximity to the user's smart device; and stopping the tracking and disconnecting the personal enhancement system from the user's smart device after the user exits the venue.

Fleet management of unmanned aerial vehicles (UAVs) is disclosed. Fleet management systems and methods may be implemented within UAVs, or systems utilizing UAVs, utilized in a delivery system configured to pick up a payload or a package at a shipping location and deliver a payload or package to a delivery location. The fleet management systems and methods may facilitate system operations of a UAV fleet, including, without limitation, positioning or deploying UAVs to meet or fill demand, prepositioning UAVs to meet a demand prediction, association delivery' orders to UAVs, order fulfillment, fleet maintenance management during operations, and flight path planning or generation.

An automation system manages operations of a fleet of robotic devices at a construction site. The system receives a construction plan including site data and a list of tasks with dependencies. Based on the plan, the system generates a virtual representation of the construction site and allocates tasks to individual robotic devices. Each robotic device transmits operational status data and sensor data, which is received by the system and used to update the virtual representation. The updated representation reflects the status of each task and incorporates real-time site information. The system generates a visualization of the virtual representation and transmits it to a client device operated by a user, causing the visualization to be presented on the client device.

The invention relates to the technical field of state monitoring of new energy trucks, and particularly discloses a real-time online monitoring system for the running state of a new energy truck and a working method.The system is characterized in that a vehicle-mounted sensor is connected with a vehicle-mounted controller, the vehicle-mounted controller is connected with a vehicle CAN bus system, the vehicle CAN bus system is connected with a vehicle-mounted wireless terminal, and the vehicle-mounted wireless terminal is wirelessly connected with a mobile phone APP; computational logic is built in the mobile phone APP; vehicle state information recognized by the vehicle-mounted sensor is sent to the vehicle-mounted controller in an electric signal mode; the vehicle-mounted controller identifies the electric signal and forms message information, the vehicle-mounted controller informs a vehicle CAN bus system of the current state of the whole vehicle by sending the message information, and the vehicle-mounted wireless terminal reads the state information; the vehicle-mounted wireless terminal transmits the information to the mobile phone APP through a wireless signal and displays the information in a page; according to the invention, the user and the motorcade management background can conveniently know the real-time state of the vehicle, and the management of the vehicle and the use of the user are greatly facilitated.

A production plannings of products and goods or shop floor logistics in producing, trading or distributing products / goods is provided in which it is addressed to maintain or keep data about the production planning of the products and goods or the shop floor regarding the logistics in producing, trading or distributing the products and goods up to date, and at least one mobile logistics robot to automate a logistic transport of the products, the goods or production material for the products and goods within a shop floor is equipped each with a sensor technology appropriate to measure or capture and control a current state or changes of the shop floor by sensor data, when each the robot is managed by a fleet management system for distributing or scheduling logistic tasks among the at least one robot executing transport tasks of a transport task queue maintained by an automated logistics planning system.

The invention relates to a method for managing a fleet of unmanned vehicles (5) by means of a computer system (1) comprising a supervisor (2) connected to a user interface (3) and a mission manager (4) that controls the movements of the vehicles. The invention also relates to a computer system (1) implementing such a method. Figure for the abstract: 1

Techniques described herein relate to controlling and / or influencing routes driven by vehicles (autonomous or other vehicles, e.g., vehicles in a fleet managed by a fleet management system). In some cases, the techniques described herein involve centrally generating road weights using a remote system, such as a fleet management system, and providing these pre-calculated weights to vehicles to simplify on-board routing. In contrast to sending a large amount of raw traffic, road conditions, and other data to each vehicle, the remote system pre-processes the data into compressed road weights that are optimized for route planning. The architecture provides various technical advantages, such as reduced data transmission, reduced computational load on the vehicle, and decentralized control of route planning of the entire fleet.

Methods, systems, and computer programs are presented for immobilizing a vehicle based on the detection of threats, such as tampering or jamming. The method includes configuring a vehicle for tamper and jamming detection, installing a communication device and an engine immobilizer in the vehicle, and providing a user interface for configuring the communication device to activate the engine immobilizer. The method further includes parallel paths for tampering detection, which immobilizes the vehicle and notifies the server upon detection, and for jamming detection, which similarly immobilizes the vehicle and notifies the server, including the vehicle's GPS position. Optionally, image information from the vehicle may be included in the notification or to trigger immobilization upon detecting multiple occupants, indicating a potential carjacking. This solution enables fleet managers to react to thefts and maximize vehicle recovery rates quickly.

A system for autonomous vehicle preparation and retrofit monitoring for efficient fleet management includes sensors equipped on a vehicle. Each sensor has a different field of view of the vehicle or the surroundings of the vehicle. A human-machine interface (HMI) accessible to a fleet manager, the cloud computing server, and the controller communicate with each other and with the vehicle. The controller executes a fleet management application (FMA) having a first control logic for authorized personnel detection (APD), a second control logic for cargo and retrofit (CU) management, and a third control logic for vehicle daily preparation (VDR) management. The outputs from the first control logic, the second control logic, and the third control logic are transmitted to the HMI and a cloud computing server, and the FMA suggests a vehicle stop time mitigation policy to a fleet manager based on the outputs from the first control logic, the second control logic, and the third control logic.

Embodiments of the present application provide a vehicle control method, device, equipment, medium and system based on a vehicle key, the method comprising: obtaining identification information and driving information of all vehicles in a vehicle fleet; sending control information to the vehicles based on the driving information; establishing and updating a pairing relationship based on the identification information; obtaining and training a vehicle control model based on driving information, vehicle performance parameters and road condition information of the vehicle; when the vehicle is a master vehicle, issuing a parameter acquisition instruction according to the pairing relationship and receiving model parameter information; performing model parameter aggregation processing based on multiple model parameter information, and sending parameter update information to corresponding slave vehicles; and when the vehicle is a slave vehicle, updating the vehicle control model of the vehicle based on the parameter update information. The application can verify the vehicles through the identification of the vehicle key in the identification information, and then cooperatively control the vehicles in the vehicle fleet, significantly improving the intelligent level and operation efficiency of vehicle fleet management, and ensuring vehicle safety.

The application discloses a kind of automobile full-dimension fault intelligent detection systems, belong to intelligent network connection automobile technical field, solve the pain points such as high landing cost of prior art, cloud edge collaborative adaptability is poor, model iteration and privacy protection contradiction.System uses lightweight cloud edge three-layer collaborative architecture, fusion five big core technologies, to realize hybrid collection with "original vehicle data as main + optional auxiliary installation sensor", overall modular decoupling and support plug-in extension.The system can complete vehicle full-stack fault detection, root cause positioning, prediction and early warning, etc.Full-process operation, with high diagnostic accuracy, strong scene adaptability, adapt to all types of vehicles, cloud support on-demand deployment, significantly reduce the access threshold of small and medium-sized car enterprises, can be widely used in automobile production quality inspection, after-sales maintenance, fleet management and other scenes, realize the whole life cycle management and control of automobile fault.

The invention discloses a tire life prediction system and method fusing vehicle-mounted high-precision positioning and image analysis. The system comprises an image acquisition and processing module, a vehicle-mounted high-precision positioning module, a multi-source data acquisition module, a data fusion modeling module and a result output module. The image acquisition and processing module extracts characteristics such as tread pattern depth, the vehicle-mounted high-precision positioning module acquires the position and track of a vehicle, the multi-source data acquisition module acquires road conditions, weather, driving behaviors and tire parameters, the data fusion modeling module fuses multi-source data to construct a prediction model, and the result output module generates a prediction result and early warning; the system solves the problem of low precision due to dependence on a single data source in the prior art, realizes accurate prediction and early warning of the tire life, and is suitable for individual vehicle owners and vehicle team management scenes.

Systems and methods herein describe an autonomous vehicle user assignment management system for improving fleet vehicle utilization. The method comprises receiving a first transportation service request from a user device. matching a first autonomous vehicle to fulfill the request, and displaying a permission request for autonomous transportation services on the user device. Upon obtaining user permission. the first autonomous vehicle is instructed to provide the transportation service. When a subsequent transportation service request is received from the same user device, a second autonomous vehicle is matched to provide the service, but the permission request is omitted based on the previously obtained consent. The second autonomous vehicle is then instructed to begin providing the transportation service without requiring additional user permission, thereby reducing user interaction friction and improving operational efficiency in autonomous vehicle fleet management.

The application provides a vehicle team management method, system, electronic device and computer readable storage medium. A specific embodiment of the method comprises: if a vehicle team splitting request is received during the travel of a main vehicle team, determining sub travel routes corresponding to the split vehicle teams; generating a convergence electronic fence according to the sub travel routes; and if any two sub vehicle teams are detected to reach a convergence area indicated by the convergence electronic fence, managing the any two sub vehicle teams by using the convergence electronic fence. The method can generate a convergence electronic fence based on sub travel routes corresponding to the sub vehicle teams. Thus, even if some members change the travel route or stay at a certain location for a period of time, they will converge at a certain location, and then the team administrator can manage the converged vehicle team by using the convergence electronic fence, thereby reducing the management difficulty.

A method for confidentiality-preserving fleet management for automation equipment in industrial plants includes providing a platform comprising a fleet management for automation equipment application within a trusted execution environment, TEE, wherein the fleet management for automation equipment application comprises a calculation module and is associated with a first company; receiving first data indicative of information about a first fleet of automation equipment associated with a second company into the fleet management for automation equipment application within the trusted execution environment; processing the first data by using the calculation module; and outputting from the trusted execution environment a result of the processing.

A technique is directed to methods and systems for managing electric vehicle charging (500). The electric vehicle management system can determine a battery state of charge using a data driven model (504) and send geolocation push notifications (310) regarding battery charging states to the electric vehicle, operators, and / or fleet managers. The electric vehicle management system can determine the routes (450) for available chargers, the transit time, battery charging time and rate, and peak load costs for a charging an electric vehicle (506, 508). A user can access the electric vehicle management system via an application (350) on a user device.

Disclosed are a method and a system for controlling a robot via a web application in a fleet management system. The system for controlling a robot according to an embodiment may comprise: a fleet management system that provides a command for at least one robot; and the at least one robot. Each of the at least one robot may, by using a web application, by at least one processor, process a first process of receiving a command from the fleet management system, a second process of processing business logic for a service according to the command, and a third process of, when robot control is required according to the command, transferring the command to a server application so that the robot control according to the command is processed via the server application.

Methods and systems for fleet management for driver analysis of a vehicle are provided. In one embodiment, a disclosed system includes one or more vehicle sensors, one or more transceivers, and one or more processors. The one or more vehicle sensors are configured to collect sensor data relating to a cognitive load of a driver of the vehicle. The one or more transceivers are configured to transmit sensor data via a wireless communication network to a remote server physically remote from the plurality of vehicles. The one or more processors are configured to at least facilitate: aggregating the internal data and the external data for all of the plurality of vehicles in the fleet, generating aggregated fleet data; and providing one or more control actions for a plurality of vehicles of the fleet based on the aggregated fleet data.

Methods and systems for automatically checking-in a vehicle upon its return to a station or depot. A computing device may receive fleet management data that includes a return location for the vehicle from a fleet management system. The computing device may also receive geolocation data collected by an on-board unit of the vehicle, and use the received fleet management data and the received geolocation data to determine whether the vehicle is at its return location. The computing device may then close a contract associated with the vehicle in response to determining that the vehicle is at the return location. Closing the contract may include incrementing a total rental fee value based on a total rental mileage, a return time that the vehicle entered the return location, whether the total rental mileage is within an expected rental mileage, whether the return time is before an expected return time, etc.

In one embodiment, a method includes a computing system obtaining, via one or more sensors of a personal mobile vehicle (PMV), a reflection signal indicating a characteristic of a road surface of an environment external to the PMV. The computing system may determine one or more characteristic factors of the road surface of the environment external to the PMV based on the reflection signal. The computing system may generate a vulnerable road user (VRU) classification probability distribution by using a machine learning classifier. The VRU classification probability distribution indicates whether a combination of the one or more characteristic factors is associated with the VRU. The computing system may determine, based on the VRU classification probability distribution, that the road surface is associated with the VRU. The computing system may present an alert message to a display associated with the PMV.