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公开(公告)号：US20190212753A1
公开(公告)日：2019-07-11
申请号：US16022363
申请日：2018-06-28
CPC分类号： G05D1/0291 , G01C21/206 , G05D2201/0216 , G06F9/4881 , G06Q10/047 , G06Q10/08 , G06Q50/28
摘要： Systems and methods of the present disclosure address the capacity constrained vehicle routing (CVRP) problem that may be applied to a warehouse scenario wherein multi-robot task allocation is required. Conventional methods can solve CVRP instances up to 100 nodes. In the present disclosure, a nearest-neighbor based Clustering And Routing (nCAR) approach is provided that makes the systems and methods of the present disclosure scalable wherein the number of nodes can be in the range of several hundreds to several thousands within an order wave.
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公开(公告)号：US20210232121A1
公开(公告)日：2021-07-29
申请号：US17007391
申请日：2020-08-31
IPC分类号： G05B19/4155 , B25J9/16
摘要： This disclosure provides systems and methods for robotic task planning when a complex task instruction is provided in natural language. Conventionally robotic task planning relies on a single task or multiple independent or serialized tasks in the task instruction. Alternatively, constraints on space of linguistic variations, ambiguity and complexity of the language may be imposed. In the present disclosure, firstly dependencies between multiple tasks are identified. The tasks are then ordered such that a dependent task is always scheduled for planning after a task it is dependent upon. Moreover, repeated tasks are masked. Thus, resolving task dependencies and ordering dependencies, a complex instruction with multiple interdependent tasks in natural language facilitates generation of a viable task execution plan. Systems and methods of the present disclosure finds application in human-robot interactions.
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3.发明申请公开(公告)号：US20200015761A1
公开(公告)日：2020-01-16
申请号：US16268473
申请日：2019-02-05
发明人： Chayan SARKAR , Pradip PRAMANICK
摘要： A method and a robotic system for online localized fatigue-state detection of a subject in a co-working environment using a non-intrusive approach is disclosed. A force sensor, mounted on the robotic system is capable of capturing effective force applied by local muscles of the subject co-working with the robotic system, providing a non-intrusive sensing. The captured force is analyzed on-line by the robotic system 102 to detect current fatigue state of the subject and proactively predict the future state of the subject. Thus, enables alerting the subject before time avoiding any possible accident.
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公开(公告)号：US20220148586A1
公开(公告)日：2022-05-12
申请号：US17161767
申请日：2021-01-29
摘要： The disclosure herein relates to methods and systems for enabling human-robot interaction (HRI) to resolve task ambiguity. Conventional techniques that initiates continuous dialogue with the human to ask a suitable question based on the observed scene until resolving the ambiguity are limited. The present disclosure use the concept of Talk-to-Resolve (TTR) which initiates a continuous dialogue with the user based on visual uncertainty analysis and by asking a suitable question that convey the veracity of the problem to the user and seek guidance until all the ambiguities are resolved. The suitable question is formulated based on the scene understanding and the argument spans present in the natural language instruction. The present disclosure asks questions in a natural way that not only ensures that the user can understand the type of confusion, the robot is facing; but also ensures minimal and relevant questioning to resolve the ambiguities.
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公开(公告)号：US20210406594A1
公开(公告)日：2021-12-30
申请号：US17138224
申请日：2020-12-30
摘要： This disclosure relates to system and method for enabling a robot to perceive and detect socially interacting groups. Various known systems have limited accuracy due to prevalent rule-driven methods. In case of few data-driven learning methods, they lack datasets with varied conditions of light, occlusion, and backgrounds. The disclosed method and system detect the formation of a social group of people, or, f-formation in real-time in a given scene. The system also detects outliers in the process, i.e., people who are visible but not part of the interacting group. This plays a key role in correct f-formation detection in a real-life crowded environment. Additionally, when a collocated robot plans to join the group it has to detect a pose for itself along with detecting the formation. Thus, the system provides the approach angle for the robot, which can help it to determine the final pose in a socially acceptable manner.
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6.发明申请公开(公告)号：US20210110822A1
公开(公告)日：2021-04-15
申请号：US17009317
申请日：2020-09-01
发明人： Pradip PRAMANICK , Chayan SARKAR , Balamuralidhar PURUSHOTHAMAN , Ajay KATTEPUR , Indrajit BHATTACHARYA , Arpan PAL
IPC分类号： G10L15/22 , G06F40/30 , G06F40/205
摘要： This disclosure relates generally to human-robot interaction (HRI) to enable a robot to execute tasks that are conveyed in a natural language. The state-of-the-art is unable to capture human intent, implicit assumptions and ambiguities present in the natural language to enable effective robotic task identification. The present disclosure provides accurate task identification using classifiers trained to understand linguistic and semantic variations. A mixed-initiative dialogue is employed to resolve ambiguities and address the dynamic nature of a typical conversation. In accordance with the present disclosure, the dialogues are minimal and directed to the goal to ensure human experience is not degraded. The method of the present disclosure is also implemented in a context sensitive manner to make the task identification effective.
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公开(公告)号：US20240013538A1
公开(公告)日：2024-01-11
申请号：US18207836
申请日：2023-06-09
IPC分类号： G06V20/50 , G06F40/284 , G06F40/205 , G06F40/40 , G06T15/00 , G06V10/764
CPC分类号： G06V20/50 , G06F40/284 , G06F40/205 , G06F40/40 , G06T15/00 , G06V10/764
摘要： This disclosure addresses the unresolved problems of tackling object disambiguation task for an embodied agent. The embodiments of present disclosure provide a method and system for disambiguation of referred objects for embodied agents. With a phrase-to-graph network disclosed in the system of the present disclosure, any natural language object description indicating the object disambiguation task can be converted into a semantic graph representation. This not only provides a formal representation of the referred object and object instances but also helps to find an ambiguity in disambiguating the referred object using a real-time multi-view aggregation algorithm. The real-time multi-view aggregation algorithm processes multiple observations from an environment and finds the unique instances of the referred object. The method of the present disclosure demonstrates significant improvement in qualifying ambiguity detection with accurate, context-specific information so that it is sufficient for a user to come up with a reply towards disambiguation.
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8.发明申请公开(公告)号：US20200262650A1
公开(公告)日：2020-08-20
申请号：US16792945
申请日：2020-02-18
发明人： Marichi AGARWAL , Chayan SARKAR
摘要： Systems and methods for optimizing scheduling of non-preemptive tasks in a multi-robot environment are provided. Traditional systems and methods cite scheduling of preemptive task(s) allocation but such scheduling techniques generally do not provide for an efficient scheduling in the multi-robot environment since tasks are preemptive. Additionally, critical parameters like deadline and performance loss are not considered. Embodiments of the present disclosure provide for optimizing the scheduling of non-preemptive tasks in the multi-robot environment by defining a plurality of tasks; merging the plurality of tasks; scheduling, by implementing an Online Minimum Performance Loss Scheduling (OMPLS) technique, initially, tasks with a higher performance loss value and then secondly, tasks that can be scheduled within their deadline and having a low performance loss value amongst the merged tasks; and finally minimizing, a performance loss value of a remaining subset of tasks that cannot be scheduled within a pre-defined deadline.
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9.发明申请公开(公告)号：US20200004588A1
公开(公告)日：2020-01-02
申请号：US16284991
申请日：2019-02-25
发明人： Chayan SARKAR , Marichi AGARWAL
摘要： Systems and methods for scheduling non-preemptive tasks in a multi-robot environment is provided. Traditional systems and methods facilitating preemptive task(s) allocation in a multi-processor environment are not applicable in the multi-robot environment since tasks are preemptive. Additionally, critical parameters like deadline and performance loss are not considered. Embodiments of the present disclosure provide for scheduling of a set of non-preemptive tasks by partitioning, the set of non-preemptive tasks either as a set of schedulable tasks or as a set of non-schedulable tasks; sorting, by a scheduling technique, the set of non-preemptive tasks partitioned; determining, by the scheduling technique, a possibility of execution of each of the set of schedulable tasks; and scheduling the set of schedulable tasks and the set of non-schedulable tasks upon determining the possibility of execution of each of the set of schedulable tasks. The present disclosure focuses on performance loss minimization when deadline miss is unavoidable.
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10.发明申请公开(公告)号：US20230124552A1
公开(公告)日：2023-04-20
申请号：US17661852
申请日：2022-05-03
发明人： Chayan SARKAR , Marichi AGARWAL , Aniruddha SINGHAL , Rajesh SINHA , Ankush OJHA , Supratim GHOSH
摘要： Online 3-dimensional bin packing problem (O3D-BPP) is getting renewed prominence due to the industrial automation brought by Industry 4.0. However, due to limited attention in the past and its challenging nature, a good approximate technique is in scarcity as compared to 1D or 2D problems. Present disclosure provides system and method that considers real-time O3D-BPP of cuboidal boxes with partial information (look-ahead) in an automated robotic sorting center. System presents two rolling-horizon mixed-integer linear programming (MILP) cum-heuristic based algorithms wherein a framework is provided that adapts and improves performance of BP heuristics by utilizing information in an online setting with look-ahead.
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