Robot Group Selection Using Environmental Sensor Matching
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Solution Overview
Problem
Existing robot selection methods fail to ensure that robots operate as expected in the desired environment, leading to inefficiencies and potential operational failures.
Innovation Solution
An information processing apparatus that acquires sensor information from the environment, selects a robot group capable of providing labor based on the request and sensor data, and optimizes the selection criteria to ensure stable operation by considering factors like floor surface conditions, robot capabilities, and cost efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If robot selection is based only on general capability matching without environmental sensor data, then selection process is simple, but robot operational reliability in the target environment deteriorates
Solution Approach 1:
The system performs preliminary acquisition of sensor information about the target environment before robot selection. This advance preparation allows the selection process to consider actual environmental conditions (floor surface type, obstacles, space dimensions) upfront, ensuring robots are matched to suitable environments before deployment, thereby improving operational reliability without adding complex real-time decision-making requirements
Solution Approach 2:
Sensor information acts as an intermediary between the robot capabilities and environmental conditions. The sensor data (captured images, floor surface information, obstacle detection) serves as a mediator that translates environmental characteristics into selectable parameters, enabling reliable robot-environment matching through objective measurement rather than direct complex evaluation
2Measurement precision
If multiple sensor types and detailed environmental analysis are used for robot selection, then selection accuracy improves, but information processing time and computational load increase
Solution Approach 1:
The environmental assessment is segmented into distinct measurable components: captured images for visual analysis, floor surface information from dedicated sensors, and obstacle detection. This segmentation allows each aspect to be measured independently and processed in parallel, improving overall measurement precision while managing computational load through modular processing of discrete environmental features
Solution Approach 2:
The sensor system automatically captures and processes environmental information without requiring manual intervention. The captured images and sensor data are automatically analyzed to determine suitable robots, eliminating time-consuming manual environment assessment while maintaining high measurement precision through automated image processing and sensor data interpretation
Data Source
AI summary
An information processing apparatus is configured to acquire a request for provision of labor in a specified environment, acquire sensor information measured by one or more sensors inside the environment, acquire information on a candidate group of robots that provide the labor, and select a first robot group that includes one or more robots capable of providing the labor from the candidate group based on the request and the sensor information.