Robotic Task Feasibility Analysis Using Semantic Maps and Knowledge Graphs
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Solution Overview
Problem
Existing robotic task execution systems fail to assess the feasibility of tasks in uncertain, dynamic environments, leading to increased execution time and power consumption due to the lack of ability to check if user-provided instructions are executable.
Innovation Solution
A method and system for task feasibility analysis with explanation, utilizing a camera, odometer, and ontology to create a semantic map and global knowledge graph, processing image frames to determine task feasibility and generate explanations for non-feasible tasks, enabling the robot to execute navigation and manipulation tasks efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the robot executes tasks without feasibility analysis in uncertain environments, then the robot can attempt more tasks, but the execution time and power consumption increase due to inability to detect dead-end situations
Solution Approach 1:
The system performs task feasibility analysis before executing the actual task. The robot creates a semantic map of the environment and a knowledge graph representing task requirements, then analyzes whether the task can be successfully completed using these pre-built structures. This preliminary assessment prevents the robot from attempting infeasible tasks, thereby reducing wasted execution time and power consumption.
2Device complexity
If the robot executes tasks without feasibility analysis, then the robot can maintain simpler system architecture, but the system lacks the capability to provide explanations for task failures
Solution Approach 1:
The system introduces a knowledge graph as an intermediary structure that stores task requirements, object properties, and spatial relationships. This knowledge graph serves as a mediator between the semantic map (environment representation) and the task feasibility analysis process. By querying this intermediary knowledge graph, the system can determine feasibility and generate explanations for why tasks are infeasible, without requiring complex ad-hoc analysis for each task.
3Reliability
If the robot creates semantic map and knowledge graph for task feasibility analysis, then the task execution accuracy improves, but the initial setup time and computational resources increase
Solution Approach 1:
The system creates the semantic map and knowledge graph in advance, before task execution begins. The semantic map is built from sensor data capturing the environment's geometry and object locations, while the knowledge graph is constructed from task descriptions and object properties. Once these structures are preliminarily established, subsequent task feasibility analyses can be performed quickly by querying these pre-computed structures, reducing the time cost during actual task execution.
Data Source
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AI summary
This disclosure relates generally to a method and system for task feasibility analysis with explanation for robotic task execution. Conventional methods for task feasibility analysis does not utilize an ontology for task capability understanding. The present disclosure uses an explainable semantic approach for checking task feasibility in a real world. The method creates scene graphs which is further used for generating a global knowledge graph and a semantic map. These are used for task feasibility analysis for an input task instruction received from a user. When the user provides the task instruction the method checks whether it is feasible or not. This helps in avoiding dead end tasks and provides the user to alter the task instruction towards feasible task. The disclosed method is used for robotic task execution in an environment.