Autonomous Floor Cleaner Escape Navigation in Trapped Areas
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Solution Overview
Problem
Autonomous floor cleaners often get trapped in small areas due to obstacle density, size, and orientation, leading to inefficient cleaning and battery waste as they frequently bounce off obstacles without effectively navigating out of these situations.
Innovation Solution
The autonomous floor cleaner employs a zero-radius turn mechanism with distance sensors to determine occupiable space and select a heading for escape or path planning, allowing it to exit trapped conditions and avoid obstacles efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the autonomous floor cleaner uses random driving to clean the floor surface, then it can cover various areas, but it becomes trapped in small areas due to obstacle density and frequently bounces off obstacles
Solution Approach 1:
The system performs preliminary actions by detecting trapped conditions through sensor data analysis before attempting to escape. It identifies when obstacle density exceeds thresholds and when bounce counts indicate entrapment, then proactively executes escape maneuvers rather than continuing random driving that leads to repeated trapping.
Solution Approach 2:
The system dynamically switches between random driving mode and escape maneuver mode based on real-time sensor feedback. When trapped conditions are detected through continuous monitoring of obstacle density and bounce frequency, the drive system transitions from random navigation to structured escape patterns, adapting behavior to current environmental conditions.
2Ease of operation
If the autonomous floor cleaner frequently bounces off obstacles to navigate, then it can attempt to find exit paths, but it wastes battery life without effectively cleaning new areas
Solution Approach 1:
The system uses feedback from distance sensors and bounce detection to monitor trapped conditions. When the number of bounces exceeds a threshold or sensor data indicates high obstacle density, the system recognizes entrapment and switches to escape maneuvers, using feedback information to optimize energy expenditure by avoiding futile random bouncing.
Solution Approach 2:
The system changes operational parameters by switching from random driving with frequent obstacle bouncing to structured escape maneuvers. This parameter change reduces energy consumption by replacing inefficient random bouncing with directed escape paths that systematically explore available space and find exits from trapped conditions.
3Ease of operation
If the autonomous floor cleaner gets trapped in small areas between furniture, then it can attempt to bounce out, but it does not clean new floor areas and wastes time
Solution Approach 1:
The system performs preliminary detection of trapped conditions by analyzing sensor data for patterns indicating entrapment, such as high obstacle density and repeated bounces. Once trapped conditions are identified, it immediately executes escape maneuvers rather than continuing to bounce randomly, reducing time spent in unproductive trapped states.
Solution Approach 2:
The system skips the inefficient process of repeated random bouncing by directly executing structured escape maneuvers when trapped conditions are detected. This rushing through of the trapping situation using pre-planned escape patterns reduces the time spent in trapped conditions and enables faster transition to productive cleaning of new areas.
Data Source
AI summary
An autonomous floor cleaner includes multiple occupiable space sensors for position and/or proximity sensing. Data from the occupiable space sensors can be used determine areas of occupiable space in proximity to the autonomous floor cleaner. Methods for exiting a trapped condition, obstacle avoidance, and path planning are disclosed.