Modular Robot
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Solution Overview
Problem
Current autonomous robots lack the ability to efficiently adapt to multiple applications and environments due to limited modular design and navigation capabilities, which restricts their versatility and effectiveness in various tasks.
Innovation Solution
A modular robot design equipped with a chassis, wheels, sensors, and a processor that captures images, identifies obstacles, and determines actions based on obstacle types, allowing for customizable functions and efficient navigation through environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a robot is designed with fixed functionality for a single application, then it can perform that specific task reliably, but it lacks versatility to adapt to multiple applications and environments
Solution Approach 1:
The robot is divided into modular components that can be independently configured and reconfigured. Each module serves a specific function, allowing the robot to be customized for different applications by assembling different combinations of modules, thereby achieving versatility without excessive overall complexity.
Solution Approach 2:
The robot employs universal interfaces and standardized connection mechanisms that allow the same base platform to support multiple different functional modules. This enables a single robot chassis to perform various tasks such as cleaning, transportation, and delivery by simply changing the attached modules.
2Adaptability or versatility
If a robot is equipped with advanced navigation and obstacle identification capabilities, then it can efficiently adapt to different environments, but the device complexity and computational requirements increase
Solution Approach 1:
The robot replaces complex mechanical navigation systems with sensor-based detection and image processing. Instead of relying on mechanical sensors and complex computational algorithms, the system uses image capture devices to visually identify obstacles and environments, simplifying the navigation architecture while maintaining adaptability.
Solution Approach 2:
The robot creates visual representations (images) of the environment and obstacles to analyze and navigate. By capturing and processing visual copies of the surroundings rather than directly interacting with physical sensors, the system achieves environmental adaptation through image-based recognition and comparison.
3Productivity
If a robot uses image capture and obstacle identification systems, then it can determine appropriate actions based on obstacle types, but the processing time and computational load increase
Solution Approach 1:
The robot pre-processes and stores image data of various obstacle types during system initialization or prior operations. By having pre-analyzed obstacle patterns ready in memory, the robot can quickly match current obstacles against stored templates without performing complex real-time analysis, thereby reducing processing time while maintaining accurate obstacle identification and appropriate action determination.
Data Source
AI summary
Provided is a robot including a chassis; a set of wheels coupled to the chassis; a plurality of sensors; a processor; and a tangible, non-transitory, machine readable medium storing instructions that when executed by the processor effectuates operations. The operations include capturing, with an image sensor disposed on the robot, a plurality of images of an environment of the robot as the robot navigates within the environment; identifying, with the processor, an obstacle type of an obstacle captured in an image based on a comparison between features of the obstacle and features of obstacles with different obstacles types stored in a database; and determining, with the processor, an action of the robot based on the obstacle type of the obstacle.