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    DETECTING STALLED STATE OF DYNAMIC POOL EQUIPMENT
    4.
    发明公开
    DETECTING STALLED STATE OF DYNAMIC POOL EQUIPMENT 审中-公开

    公开(公告)号：US20240345604A1

    公开(公告)日：2024-10-17

    申请号：US18631135

    申请日：2024-04-10

    申请人： Maytronics Ltd.

    发明人： Leo HERSZENHAUT ,  Gilad GOLDENBERG

    IPC分类号： G05D1/86 ,  G05D101/15 ,  G05D105/10 ,  G05D109/30

    CPC分类号： G05D1/86 ,  G05D2101/15 ,  G05D2105/10 ,  G05D2109/38

    摘要： Disclosed herein is a method of detecting stalled state of a dynamic pool equipment unit, comprising receiving a plurality of movement features relating to a dynamic pool equipment unit deployed in a water pool which are captured during a predefined sampling window and comprise (1) motion features of the pool equipment unit, and (2) operational features of electric motor(s) of the pool equipment unit, determining a movement pattern of the pool equipment unit using one or more statistical models applied to the plurality of movement features which are trained to estimate a stalled state of the pool equipment unit in which the pool equipment unit is pitched up and unable to advance on a slopped obstacle in the water pool, and causing the pool equipment unit to stop attempted advance in a current direction responsive to determining that the pool equipment unit is in the stalled state.

    GENERAL PRE-TRAINED TRANSFORMER SERVICE FOR A GENERAL-PURPOSE ROBOTICS OPERATING SYSTEM
    5.
    发明公开
    GENERAL PRE-TRAINED TRANSFORMER SERVICE FOR A GENERAL-PURPOSE ROBOTICS OPERATING SYSTEM 审中-公开

    公开(公告)号：US20240338032A1

    公开(公告)日：2024-10-10

    申请号：US18627663

    申请日：2024-04-05

    申请人： Perrone Robotics, Inc.

    发明人： Paul J. Perrone

    IPC分类号： G05D1/60 ,  G05D101/15 ,  G06N3/0475

    CPC分类号： G05D1/60 ,  G06N3/0475 ,  G05D2101/15

    摘要： Provided herein are system, apparatus, article of manufacture, method and/or computer program product aspects, and/or combinations and sub-combinations thereof, for artificial intelligence in mobile autonomous robotics and autonomous mobile platforms. An example aspect operates by a method of using a general-purpose robotics operating system (GPROS) with generative pre-trained transformers (GPT) (GPROS-GPT) model. The method includes training the GPROS-GPT model and querying the GPROS-GPT model to generate GPROS configuration data and service extension files. The method further includes loading the configuration data and the service extension files into a GPROS-based application and using the GPROS-based application to operate a GPROS-based robot or a GPROS-based autonomous vehicle.

    ADAPTIVE Q LEARNING IN DYNAMICALLY CHANGING ENVIRONMENTS
    8.
    发明公开
    ADAPTIVE Q LEARNING IN DYNAMICALLY CHANGING ENVIRONMENTS 审中-公开

    公开(公告)号：US20240311641A1

    公开(公告)日：2024-09-19

    申请号：US18602351

    申请日：2024-03-12

    申请人： Technology Innovation Institute - Sole Proprietorship LLC

    发明人： Reda ALAMI ,  Hakim HACID

    IPC分类号： G06N3/092 ,  G05D1/644 ,  G05D101/15 ,  G05D109/20

    CPC分类号： G06N3/092 ,  G05D1/644 ,  G05D2101/15 ,  G05D2109/20

    摘要： Systems, methods, and computer-readable media for dynamic changes to both a learned control policy in the event of a change in the environment (e.g., introduction of a new or unseen obstacle). Rather than having to implement an entirely new policy (and a new global Q table), which can delay performance of tasks by agent(s), the present embodiments allow for a reduced delay in updating local Q table(s) based on detection of a new change in the environment. Locally changing the policy allows for more efficient updating of the policy based on changes in the environment, rather than globally changing the Q table after each change. Particularly in an event with multiple changes in the environment, the present embodiments increase efficiency in updating local and global Q tables while also reducing a delay in providing new instructions to the agent(s) in completing tasks.

    Intention-driven reinforcement learning-based path planning method
    10.
    发明授权
    Intention-driven reinforcement learning-based path planning method 有权

    公开(公告)号：US12124282B2

    公开(公告)日：2024-10-22

    申请号：US17923114

    申请日：2021-12-13

    申请人： SOUTHEAST UNIVERSITY

    发明人： Hua Zhang ,  Na Su ,  Junbo Wang

    IPC分类号： G05D1/644 ,  B63B79/40 ,  G05D101/15 ,  G05D109/30

    CPC分类号： G05D1/644 ,  B63B79/40 ,  G05D2101/15 ,  G05D2109/30

    摘要： The present invention discloses an intention-driven reinforcement learning-based path planning method, including the following steps: 1: acquiring, by a data collector, a state of a monitoring network; 2: selecting a steering angle of the data collector according to positions of surrounding obstacles, sensor nodes, and the data collector; 3: selecting a speed of the data collector, a target node, and a next target node as an action of the data collector according to an ε greedy policy; 4: determining, by the data collector, the next time slot according to the selected steering angle and speed; 5: obtaining rewards and penalties according to intentions of the data collector and the sensor nodes, and updating a Q value; 6: repeating step 1 to step 5 until a termination state or a convergence condition is satisfied; and 7: selecting, by the data collector, an action in each time slot having the maximum Q value as a planning result, and generating an optimal path. The method provided in the present invention can complete the data collection path planning with a higher probability of success and performance closer to the intention.