Tata Consultancy Services Limited

Telefonaktiebolaget Lm Ericsson (publ)

Ajay Kattepur

Balamuralidhar Purushothaman

Hemant Kumar Rath

Anantha Simha

Arpan Pal

Arijit Mukherjee

Chayan Sarkar

Manoj Karunakaran Nambiar

Sounak Dey

Indrajit Bhattacharya

Pradip Pramanick

Ramamurthy Badrinath

Nanjangud Chandrasekhara Swamy Narendra

Subhrojyoti Roy Chaudhuri

Puneet Patwari

Amar Banerjee

A method performed by a network node for handling one or more operations in a communications network comprising a plurality of computing devices performing one or more tasks. The network node obtains initial parameters relating to the plurality of computing devices, environment and the communications network; and generates a plan by taking one or more operation goals involving the plurality of computing devices into account as well as the obtained initial parameters, wherein the generated plan relates to operation of the plurality of computing devices. The network node further computes a number of back-up plans, wherein the number of back-up plans are taking one or more events into account wherein the one or more events relate to operation of the plurality of computing devices; and executes one or more operations using the generated plan, and in case the one or more events occur, using a computed back-up plan.

Network node and method for handling operations in a communications network

Robots are deployed for handling different tasks in various field of applications. For the robots to function, task planning is required to be done. During the task planning, goal setting is done, as well as actions to be executed for corresponding to each goal are decided. Traditionally, this is carried out first and then the robots start executing the task plan, thereby failing to capture any change in the environment the robots operate, post the task plan generation. Disclosed herein is a method and system for robotic task planning in which a task plan is generated and is executed. However if the task execution fails due to change in any of the parameters/factors, then the system dynamically invokes an adaptation and re-planning mechanism which either updates the already generated task plan (by capturing the change) or generates a new task plan, which the robot can execute to achieve the goal.

Method and system for robotic task planning

This disclosure relates generally to robotic network, and more particularly to a method and system for hierarchical decomposition of tasks and task planning in a robotic network. While a centralized system is used for action planning in a robotic network, any communication network issues can adversely affect working of the robotic network. Further, hardcoding one or more specific tasks to a robot restricts use of the robots irrespective of capabilities of the robots. The robotic agent decomposes a goal assigned to the robot to multiple sub-goals, and for each sub-goal, identifies one or more tasks to be executed/performed by the robot. An action plan is generated based on all such tasks identified, and the robot executes the action plan, in response to the goal assigned to the robot.

Method and system for hierarchical decomposition of tasks and action planning in a robotic network

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.

Conversational systems and methods for robotic task identification using natural language

Systems and methods for generating control system solutions for robotics environments is provided. The traditional systems and methods provide robotics solutions but specialized to only a particular robotic application, domain, and selected structure. The embodiments of the proposed disclosure provide for generating one or more control system solutions for a plurality of robotics environment by acquiring a robotics domain knowledge corresponding to the plurality of robotics environments; extracting one or more solution specifications based upon the robotics domain knowledge; translating the one or more solution specifications into one or more design solutions; generating, the one or more control system solutions for the plurality of robotics environments; and optimizing the one or more control system solutions generated by performing, based upon a set of task execution logs executed, a close loop verification to validate a plurality of commands and a plurality of state transitions executing in the plurality of robotics environments.

Systems and methods for generating control system solutions for robotics environments

This disclosure relates generally to method and system for evaluating performance of workflow resource patterns. Structured way of resource utilization for performing a complex task thereby evaluating end to end performance in any industrial warehouse is crucial. The proposed disclosure processes the received task to disintegrate into a plurality of sub tasks and for each sub task a workflow resource pattern is generated. Further, dynamically each workflow resource pattern is mapped to corresponding queueing mode of queueing station in the hybrid queueing network model. The system further evaluates end to end performance for each workflow resource pattern by computing performance metrics. Based on the performance evaluation the queuing mode may be replaced with another queueing modes for the workflow resource pattern. The proposed disclosure robustly analyses end to end performance of the workflow resource pattern maximizing throughput, improvising accuracy, increasing efficiency and thereby minimizing cost.

Method and system for evaluating performance of workflow resource patterns

In order to make use of computational resources available at runtime through fog networked robotics paradigm, it is critical to estimate average performance capacities of deployment hardware that is generally heterogeneous. It is also not feasible to replicate runtime deployment framework, collected sensor data and realistic offloading conditions for robotic environments. In accordance with an embodiment of the present disclosure, computational algorithms are dynamically profiled on a development testbed, combined with benchmarking techniques to estimate compute times over the deployment hardware. Estimation in accordance with the present disclosure is based both on Gustafson's law as well as embedded processor benchmarks. Systems and methods of the present disclosure realistically capture parallel processing, cache capacities and differing processing times across hardware.

Systems and methods for estimating computation times a-priori in fog computing robotics

This disclosure relates to managing Fog computations between a coordinating node and Fog nodes. In one embodiment, a method for managing Fog computations includes receiving a task data and a request for allocation of at least a subset of a computational task. The task data includes data subset and task constraints associated with at least the subset of the computational task. The Fog nodes capable of performing the computational task are characterized with node characteristics to obtain resource data associated with the Fog nodes. Based on the task data and the resource data, an optimization model is derived to perform the computational task by the Fog nodes. The optimization model includes node constraints including battery degradation constraint, communication path loss constraint, and heterogeneous computational capacities of Fog nodes. Based on the optimization model, at least the subset of the computational task is offloaded to a set of Fog nodes.

Optimal deployment of fog computations in IoT environments

This disclosure relates generally to autonomous devices, and more particularly to method and system to optimally allocate warehouse procurement tasks to distributed autonomous devices. The method includes obtaining, at a coordinating agent, a global task associated with the warehouse and information associated with the robotic agents. The information includes a count and status of the robotic agents. The global task is profiled to obtain a set of sub-tasks and constraints associated with the set of sub-tasks are identified. The constraints include utilization constraint and/or pricing constraints. A distributed, decentralized optimal task allocation is performed amongst the robotic agents based on constraints to obtain optimal performance of robotic agents. The distributed optimal task allocation includes performing primal or dual decomposition of the set of sub-tasks by each robotic agent and updating corresponding primal/dual variables by the coordinating agent when the optimization is performed based on utilization constraint and pricing constraints, respectively.

Method and system for optimally allocating warehouse procurement tasks to distributed robotic agents

Systems and methods for service demand based performance prediction using a single workload is provided to eliminate need for load testing. The process involves identifying a range of concurrencies for the application under test; capturing a single workload pertaining to the application under test; and iteratively performing for the identified range of concurrencies: generating an array of one or more predefined CPU performance metrics based on the captured single workload; generating an array of service demands based on the captured single workload and the generated array of the one or more pre-defined CPU performance metrics; computing an array of throughput based on the generated array of service demands; and updating the generated array of the one or more pre-defined CPU performance metrics based on the computed array of throughput.

Service demand based performance prediction using a single workload

Systems and methods for service demand based performance prediction with varying workloads are provided. Chebyshev nodes serve as optimum number of load testing points to minimize polynomial interpolation error rates. Chebyshev nodes are identified for a pre-determined range of concurrencies in the application under test. An array of interpolated service demands at the Chebyshev nodes, when integrated with a modified multi-server Mean Value Analysis (MVA) technique provides superior throughput and response time predictions.

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Bengaluru, India

Ajay Kattepur