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    METHOD AND SYSTEM FOR REAL-TIME PATH PLANNING
    1.
    发明申请
    METHOD AND SYSTEM FOR REAL-TIME PATH PLANNING 有权

    公开(公告)号：US20210094182A1

    公开(公告)日：2021-04-01

    申请号：US17025550

    申请日：2020-09-18

    Applicant: Tata Consultancy Services Limited

    Inventor： Arup Kumar Sadhu ,  Arnab Sinha ,  Titas Bera ,  Mohit Ludhiyani ,  Ranjan Dasgupta

    IPC: B25J9/16 ,  G05D1/02

    Abstract: This disclosure relates generally to real-time path planning. Planning amidst obstacles in a cluttered indoor environment is a difficult task for a robotic agent. The disclosed method provides semidefinite programming induced free-space based path planning. Free-space is generated by an efficient environment grid resolution independent seeding technique. In the proposed resolution independent seeding technique, initial position of the robotic agent is considered as the first seed. For subsequent seeding, information of the expanded earlier seeds are employed intelligently. This process is followed unto a finite sequence, which naturally results in a contiguous navigable convex free-space. This contiguous navigable convex free-space is employed to create an undirected graph, which is then used for path planning. Path planning is done locally by evaluating the subgoal with respect to a final goal. Local planning cumulatively assists the planner to attain the final goal.

    EVENT TRIGGERED LOCATION BASED PARTICIPATORY SURVEILLANCE
    3.
    发明申请
    EVENT TRIGGERED LOCATION BASED PARTICIPATORY SURVEILLANCE 有权
    基于事件触发位置的参与式监视

    公开(公告)号：US20150070506A1

    公开(公告)日：2015-03-12

    申请号：US14386518

    申请日：2013-03-12

    Applicant: TATA CONSULTANCY SERVICES LIMITED

    Inventor： Dhiman Chattopadhyay ,  Arpan Pal ,  Rohan Banerjee ,  Ranjan Dasgupta ,  Priyanka Sinha ,  Aniruddha Sinha ,  Chirabrata Bhaumik

    IPC: G06K9/00 ,  G08B25/08 ,  G08B13/196

    CPC classification number: G06K9/00771 ,  G06K9/00718 ,  G06K9/00979 ,  G06K2009/00738 ,  G06K2209/27 ,  G08B13/19613 ,  G08B25/08 ,  G08B27/006 ,  H04N7/181

    Abstract: The present invention provides the multimodality filtration surveillance comprising of a plurality of filtration stages executed at the backend server to confirm nature of anomaly in an event, the filtration stages comprising: a first filter of a video anomalies detection in the event for a specified time-place value, a second filter of a city soundscape adapted to provide a localized decibel maps of a city, a third filter of a geocoded social network adapted to semantically read and analyze data from one or more social media corresponding to the specified time-place value, and a fourth filter of an event triggered or proactive local participatory surveillance adapted to provide augmented information on the detected anomalies.

    Abstract translation: 本发明提供了多模过滤监视，其包括在后端服务器上执行的多个过滤阶段，以确认事件中异常的性质，所述过滤阶段包括：在特定时间段内的视频异常检测的第一过滤器， 位置值，适于提供城市的局部分贝地图的城市音景的第二滤波器，适于语义地读取和分析来自对应于指定时间位置值的一个或多个社交媒体的数据的地理编码社交网络的第三滤波器 以及适于提供关于检测到的异常的增强信息的事件触发或主动的本地参与式监视的第四过滤器。

    Method and system to detect and estimate dynamic obstacles using RGB-D sensors for robot navigation
    4.
    发明授权
    Method and system to detect and estimate dynamic obstacles using RGB-D sensors for robot navigation 有权

    公开(公告)号：US12296488B2

    公开(公告)日：2025-05-13

    申请号：US18059133

    申请日：2022-11-28

    Applicant: Tata Consultancy Services Limited

    Inventor： Arindam Saha ,  Ranjan Dasgupta

    IPC: G06V20/58 ,  B25J9/16

    Abstract: This disclosure relates generally to method and system to detect and estimate dynamic obstacles using Red Green Blue-Depth RGB-D sensors for robot navigation. Obstacle detection and tracking tasks with efficient processing of depth images becomes complex in dynamic and cluttered environments. The method detects one or more obstacles being identified in the one or more depth images in two dimensional space using the depth map captured using a Red Green Blue-Depth RGB-D sensor configured to a mobile robot. Further, a restricted v-depth map is computed between specified columns taken from the width of each dynamic obstacle of the u-depth map and within the depth range derived from the u-depth map. The one or more dynamic obstacles are tracked by matching segments of subsequent depth images to predict future position in the region of the monitoring environment by computing a closeness signature. The method evaluates correctness with open data sequence and real world data.

    Method and system for path planning
    6.
    发明授权
    Method and system for path planning 有权

    公开(公告)号：US11767026B2

    公开(公告)日：2023-09-26

    申请号：US16874116

    申请日：2020-05-14

    Applicant: Tata Consultancy Services Limited

    Inventor： Arup Kumar Sadhu ,  Shubham Shukla ,  Titas Bera ,  Ranjan Dasgupta

    IPC: B60W60/00

    CPC classification number: B60W60/001 ,  B60W2420/42

    Abstract: The disclosure relates to motion planning for autonomous vehicles to find a contiguous path to travel from a source to a destination in a real time environment. But, motion planning suffers due to dimensionality problem during planning a path in high dimensional configuration space. Conventional a priori path planning techniques utilize deterministic selection of a seed and there is a challenge in providing resolution completeness, especially in a cluttered environment. Here, the resolution problem associated with the cluttered environment, is rectified by utilizing random seed generation technique. A convex region is grown around each seed and a set of hyperplanes are generated around each convex region. A contiguous polytope is created by utilizing the set of hyperplanes associated with each convex region. Further, an undirected graph is created based on a plurality of polytope points associated with the contiguous polytope and a shortest path in the graph is computed.

    Method and system for free space detection in a cluttered environment
    8.
    发明授权
    Method and system for free space detection in a cluttered environment 有权

    公开(公告)号：US11269336B2

    公开(公告)日：2022-03-08

    申请号：US16578255

    申请日：2019-09-20

    Applicant: Tata Consultancy Services Limited

    Inventor： Arup Kumar Sadhu ,  Ranjan Dasgupta ,  Balamuralidhar Purushothaman

    IPC: G06T7/64 ,  G05D1/02 ,  G06T7/11 ,  G05D1/00

    Abstract: Robots are used extensively in different applications so as to perform specific tasks. However, the robots are required to move around in a location where they are present, so as to perform the tasks. For path planning, free space identification is performed by the robots during which obstacles are detected and free space is identified. However, the existing systems for path planning struggle to identify free space in cluttered environments. The disclosure herein generally relates to robotic path planning, and, more particularly, to a method and system for free space detection in a cluttered environment for robotic path planning. The system inscribes obstacles in bounding boxes and all unit grids inscribed by the bounding boxes are considered as occupied. Further, by seeding the occupancy grid map, the system identifies unified segments and corresponding seeds. Further a convex expansion is executed in the occupancy grid map to detect the free space.

    System and method of multirotor dynamics based online scale estimation for monocular vision
    10.
    发明授权
    System and method of multirotor dynamics based online scale estimation for monocular vision 有权

    公开(公告)号：US10748299B2

    公开(公告)日：2020-08-18

    申请号：US16580403

    申请日：2019-09-24

    Applicant: Tata Consultancy Services Limited

    Inventor： Mohit Ludhiyani ,  Vishvendra Rustagi ,  Arnab Sinha ,  Ranjan Dasgupta

    IPC: G06T7/20 ,  G06T7/70 ,  G06T7/277 ,  G01C3/08 ,  B64C39/02

    Abstract: Robotic vision-based framework wherein an on-board camera device is used for scale estimation. Unlike conventional scale estimation methods that require inputs from more than one or more sensors, implementations include a system and method to estimate scale online solely, without any other sensor, for monocular SLAM by using multirotor dynamics model in an extended Kalman filter framework. This approach improves over convention scale estimation methods which require information from some other sensors or knowledge of physical dimension of an object within the camera view. An arbitrary scaled position and an Euler angle of a multirotor are estimated from vision SLAM (simultaneous localization and mapping) technique. Further, dynamically integrating, computed acceleration to estimate a metric position. A scale factor and a parameter associated with the multirotor dynamics model is obtained by comparing the estimated metric position with the estimated arbitrary position.

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