Robot Task Assignment via Visual Region Selection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing computing systems lack efficient methods for assigning tasks to self-directed robot devices in dynamic environments, such as IoT-enabled settings, where tasks need to be defined and executed based on visual inputs from image capturing devices.
Innovation Solution
A method where a target region is selected from a displayed image, and tasks are defined and communicated to a self-directed robot device using natural language text data and ontological analysis, allowing the robot to perform tasks according to a workflow schedule, with the ability to interpret and execute tasks based on image data and metadata.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If tasks are manually assigned to robot devices in traditional computing systems, then task execution control is maintained, but operational efficiency and flexibility are reduced in dynamic IoT environments
Solution Approach 1:
The robot device autonomously captures images, processes visual data, identifies objects, and executes tasks without continuous human intervention. The system enables self-directed operation where the robot services itself by interpreting visual inputs and autonomously determining task execution sequences.
Solution Approach 2:
The system pre-processes visual data by capturing images, identifying objects, and determining task workflows before robot execution. Task definitions and object identifications are established in advance based on visual analysis, allowing the robot to execute pre-planned sequences efficiently.
2Adaptability or versatility
If visual input processing and natural language interpretation are added to task assignment, then task definition flexibility is improved, but system complexity increases
Solution Approach 1:
The system introduces an intermediary processing layer that translates natural language text data and visual image data into structured task definitions. This intermediary layer includes text processing modules and visual data analysis components that bridge human-language instructions and robot-executable task sequences, managing complexity through modular architecture.
Solution Approach 2:
The system replaces manual mechanical task assignment with automated visual data processing and natural language interpretation. Instead of direct human-robot task specification, the system uses image capturing devices, text processing algorithms, and object identification algorithms to automatically generate and assign tasks.
3Measurement precision
If image capturing and visual data processing are implemented for task identification, then object recognition accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The visual data processing is segmented into distinct modules: image capturing by the image capturing device, text data processing for task definitions, object identification from visual data, and task workflow determination. This segmentation allows parallel processing of different data streams and reduces overall processing time through modular computation.
Data Source
AI summary
Embodiments for assigning tasks to a robot device by a processor. A target region may be selected from a displayed image of an image capturing device. One or more tasks may be defined according to a plurality of objects displayed within the target region such that the defined one or more tasks are arranged according to a task workflow. The defined one or more tasks may be communicated to a self-directed device thereby assigning the self-directed robot to perform the defined one or more tasks according to the task workflow.