Modular Robot Part for Cloud-Based Environmental Adaptation
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Solution Overview
Problem
Existing robotic systems lack the ability to operate efficiently in diverse environments due to limited sensing capabilities and connectivity, hindering their adaptability and task execution.
Innovation Solution
A stand-alone robot part with a processor, sensors, and wireless connectivity that connects to various robotic systems, transmitting sensor data to the cloud for environment analysis and generating commands to control the robotic system's tasks, enhancing sensing capabilities and connectivity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If robotic systems use traditional built-in sensors and processing units, then system simplicity is maintained, but sensing capabilities and processing power are limited
Solution Approach 1:
The robotic system is divided into modular components: a robot part with sensors and processing unit that can be detached and attached to different robotic systems. This segmentation allows the sensing capabilities to be improved without permanently increasing the base system's complexity, as the additional components can be removed when not needed.
Solution Approach 2:
The robot part is designed as a universal module that can connect to multiple different robotic systems through standardized interfaces. The processor and sensors serve multiple functions: local processing, cloud communication, and environment interpretation, thereby improving versatility without proportionally increasing complexity.
2Adaptability or versatility
If robotic systems integrate cloud connectivity and environment interpretation capabilities, then task execution in diverse environments improves, but device complexity increases
Solution Approach 1:
The robot part acts as an intermediary between the robotic system and the cloud. It includes a wireless communication interface that handles cloud connectivity and an environment interpretation module that processes sensor data, serving as a mediator that offloads complex processing tasks from the main robotic system while enabling environmental adaptability.
Solution Approach 2:
The system adds a new dimension of cloud-based processing and communication capability through the robot part. By incorporating wireless communication interfaces and cloud connectivity, the system transitions from purely local processing to a multi-dimensional architecture that leverages both local and remote computing resources, improving environmental adaptability without proportionally increasing local device complexity.
3Measurement precision
If the robot part continuously transmits sensor data to the cloud, then environment information accuracy improves, but energy consumption increases
Solution Approach 1:
The robot part transmits sensor data to the cloud periodically or at scheduled intervals rather than continuously. This periodic transmission approach maintains environment information accuracy by regularly updating cloud-based interpretations while significantly reducing energy consumption compared to continuous data streaming.
Solution Approach 2:
The robot part selectively transmits only the most relevant or changed sensor data to the cloud rather than all sensor data continuously. This partial transmission approach maintains sufficient environment information accuracy for task execution while minimizing energy consumption by avoiding redundant data transmissions.
Data Source
AI summary
Example implementations may relate a robot part including a processor, at least one sensor, and an interface providing wireless connectivity. The processor may determine that the robot part is removablly connected to a particular robotic system and may responsively obtain identification information to identify the particular robotic system. While the robot part is removablly connected to the particular robotic system, the processor may (i) transmit, to an external computing system, sensor data that the processor received from the at least one sensor and (ii) receive, from the external computing system, environment information (e.g., representing characteristics of an environment in which the particular robotic system is operating) based on interpretation of the sensor data. And based on the identification information and the environment information, the processor may generate a command that causes the particular robotic system to carry out a task in the environment.