PRIORITY CLAIM

This U.S. patent application claims priority under 35 U.S.C. § 119 to: India Application No. 201821024658, filed on 2 Jul. 2018. The entire contents of the aforementioned application are incorporated herein by reference.

TECHNICAL FIELD

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.

BACKGROUND

Advancements in domains such as robotics, artificial intelligence, and so on, have backed industrial automation. As part of industrial automation, initially computers were being used to aid users with data processing and decision making. However, new generation computers having advanced data processing capabilities have almost replaced human beings. Similarly, robots with specific capabilities are used to handle specific tasks. For example, in inventory management applications, robots are used for various operations such as but not limited to fetch product(s) from racks, placing products on racks, and delivering products to customers.

Such industry automation systems that use robots for handling various activities include one or more data processing and decision making units, which are used to process data and generate appropriate action plans which in turn are used to control activities/functions being handled by the robots.

The inventors here have recognized several technical problems with such conventional systems, as explained below. Such systems that are currently available typically deploy robots with specific capability to handle specific tasks. In that case, algorithms and data to handle specific tasks are hardcoded to robots, which may limit use of the robot even though it is capable of handling other tasks. From a resource management perspective, such an implementation has a disadvantage that a robot designated to handle a specific task cannot be used to handle another task, even though the robot is available for use and has hardware capabilities to handle the other task.

SUMMARY

Embodiments of the present disclosure present technological improvements as solutions to one or more of the above-mentioned technical problems recognized by the inventors in conventional systems. For example, in one embodiment, a processor-implemented method for action planning in a robotic network is provided. A robot that is part of the robotic network is assigned at least one goal, via one or more hardware processors. Upon receiving the at least one goal, by using a robotic agent of the robot decomposes the at least one goal to a plurality of sub-goals, via the one or more hardware processors. Further, each of the plurality of sub-goals is processed using the robotic agent, wherein processing of the sub-goal by the robotic agent involves: identifying at least one target and at least one action on the target, as requirements corresponding to the sub-goal; identifying a plurality of attributes corresponding to the at least one target; identifying a set of attributes corresponding to the at least one action; determining capability of the at least one robot, by querying a robot capability knowledge base; and generating an action plan corresponding to the collected at least one goal, based on 1. the plurality of attributes corresponding to the at least one action, 2. the set of attributes corresponding to the at least one target, 3. the determined capability of the robot, 4. data pertaining to environment in which the at least one robot is functioning, and 5. data pertaining to an algorithm to be executed by the at least one robot for executing the action plan. Identifying the plurality of attributes corresponding to the at least one action and the at least one target by the robotic agent involves: fetching said plurality of attributes from a local database of the at least one robot, if said plurality of attributes are available in the local database; fetching said plurality of attributes from a global database of the at least one robot, if said plurality of attributes are not available in the local database of the robot, and are available in the global database; and obtaining said plurality of attributes dynamically based on at least one real-time input obtained based on perception of the at least one robot.

In another embodiment, a robotic agent for action planning in a robotic network is provided. The robotic agent includes a memory module storing a plurality of instructions; one or more communication interfaces; and one or more hardware processors. The one or more hardware processors are coupled to the memory module via the one or more communication interfaces, wherein the one or more hardware processors are configured by the instructions to collect data pertaining to at least one goal that has been assigned to a robot with which the robotic agent is associated. The at least one goal is then decomposed to a plurality of sub-goals. Each sub-goal is then processed by the robotic agent, wherein processing of the sub-goal includes: identifying at least one target and at least one action on the target, as requirements corresponding to the sub-goal; identifying a plurality of attributes corresponding to the at least one target; identifying a set of attributes corresponding to the at least one action; determining capability of the at least one robot, by querying a robot capability knowledge base; and generating an action plan corresponding to the collected at least one goal, based on 1. the plurality of attributes corresponding to the at least one action, 2. the set of attributes corresponding to the at least one target, 3. the determined capability of the robot, 4. data pertaining to environment in which the at least one robot is functioning, and 5. data pertaining to an algorithm to be executed by the at least one robot for executing the action plan. Identifying the plurality of attributes corresponding to the at least one action and the at least one target by the robotic agent involves: fetching said plurality of attributes from a local database of the at least one robot, if said plurality of attributes are available in the local database; fetching said plurality of attributes from a global database of the at least one robot, if said plurality of attributes are not available in the local database of the robot, and are available in the global database; and obtaining said plurality of attributes dynamically based on at least one real-time input obtained based on perception of the at least one robot.

In yet another embodiment, a non-transitory computer readable medium for action planning in a robotic network is provided. The non-transitory computer readable medium, using a robotic agent associated with a robot in the robotic network, performs action planning for a robot that is part of the robotic network, and generates an action plan corresponding to at least one goal that has been assigned to the robot. Upon receiving the at least one goal, the robotic agent decomposes the at least one goal to a plurality of sub-goals, via the one or more hardware processors. Further, each of the plurality of sub-goals is processed using the robotic agent, wherein processing of the sub-goal by the robotic agent involves: identifying at least one target and at least one action on the target, as requirements corresponding to the sub-goal; identifying a plurality of attributes corresponding to the at least one target; identifying a set of attributes corresponding to the at least one action; determining capability of the at least one robot, by querying a robot capability knowledge base; and generating an action plan corresponding to the collected at least one goal, based on 1. the plurality of attributes corresponding to the at least one action, 2. the plurality of attributes corresponding to the at least one target, 3. the determined capability of the robot, 4. data pertaining to environment in which the at least one robot is functioning, and 5. data pertaining to an algorithm to be executed by the at least one robot for executing the action plan. Identifying the plurality of attributes corresponding to the at least one action and the at least one target by the robotic agent involves: fetching said plurality of attributes from a local database of the at least one robot, if said plurality of attributes are available in the local database; fetching said plurality of attributes from a global database of the at least one robot, if said plurality of attributes are not available in the local database of the robot, and are available in the global database; and obtaining said plurality of attributes dynamically based on at least one real-time input obtained based on perception of the at least one robot.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.

