Mobile cleaning robot artificial intelligence for situational awareness
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Solution Overview
Problem
Current mobile cleaning robots lack situational awareness, failing to adapt their cleaning operations based on dynamic environmental changes and user preferences, leading to inefficient cleaning and potential interference with daily activities.
Innovation Solution
A mobile cleaning robot equipped with artificial intelligence, featuring cameras for environmental sensing, a recognition module using neural networks to identify objects and patterns, and a control module that adjusts cleaning tasks based on recognized objects, barriers, and user preferences, allowing for adaptive navigation and scheduling of cleaning tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the robot uses basic navigation without environmental recognition, then the device complexity is low, but the adaptability to environmental changes is poor
Solution Approach 1:
The robot performs preliminary mapping of the environment before cleaning operations. The mapping module creates a structural map of the environment in advance, allowing the robot to adapt to environmental changes without increasing operational complexity. This preliminary action enables the robot to recognize and adapt to environmental features before actual cleaning tasks begin.
Solution Approach 2:
The patent introduces an intermediary recognition module that acts as a mediator between the basic navigation system and the cleaning operations. This module processes environmental information and translates it into actionable cleaning decisions, allowing the robot to adapt to environmental changes without directly increasing the complexity of the core navigation and cleaning systems.
2Manufacturing precision
If the robot cleans all areas uniformly, then the productivity is high, but the cleaning effectiveness is reduced due to missed spots around obstacles
Solution Approach 1:
The robot applies different cleaning strategies to different areas based on environmental recognition. The control module adjusts cleaning parameters locally around detected obstacles versus open areas, ensuring thorough cleaning effectiveness in complex regions while maintaining high productivity in simple regions. This local differentiation allows the robot to optimize both cleaning quality and overall efficiency.
Solution Approach 2:
The cleaning operation is made dynamic and adaptive rather than static and uniform. The robot continuously updates its cleaning path and parameters based on real-time environmental recognition, allowing it to dynamically adjust around obstacles while maintaining overall productivity. The cleaning process becomes flexible, adapting to environmental changes without sacrificing thoroughness.
3Duration of action of moving object
If the robot frequently returns to charging station, then the duration of action is extended, but the loss of time increases due to repeated interruptions
Solution Approach 1:
The robot performs preliminary environmental mapping and obstacle recognition during initial navigation phases, allowing it to plan more efficient cleaning paths that minimize interruptions. By understanding the environment in advance, the robot can optimize its cleaning route to reduce the frequency and duration of returns to the charging station, thereby extending effective cleaning duration while reducing time loss.
4Measurement precision
If the robot uses simple obstacle detection, then the device complexity is low, but the measurement precision of environmental features is insufficient
Solution Approach 1:
The environmental recognition system is segmented into multiple specialized modules, each handling specific types of environmental features. The mapping module handles structural features, the recognition module identifies objects and obstacles, and the control module processes cleaning decisions. This segmentation allows high measurement precision for different environmental aspects while keeping individual module complexity manageable.
Solution Approach 2:
The environmental recognition system is designed with multi-functional capabilities that serve multiple purposes. The same mapping and recognition modules that provide precise environmental measurement also enable adaptive path planning, obstacle avoidance, and cleaning optimization. This universality allows high measurement precision without proportionally increasing device complexity, as the same components serve multiple functions.
Data Source
AI summary
A mobile cleaning robot includes a cleaning head configured to clean a floor surface in an environment, and at least one camera having a field of view that extends above the floor surface. The at least one camera is configured to capture images that include portions of the environment above the floor surface. The robot includes a recognition module is configured to recognize objects in the environment based on the images captured by the at least one camera, in which the recognition module is trained at least in part using the images captured by the at least one camera. The robot includes a storage device is configured to store a map of the environment. The robot includes a control module configured to control the mobile cleaning robot to navigate in the environment using the map and operate the cleaning head to perform cleaning tasks taking into account of the objects recognized by the recognition module.