Autonomous Robot Hazard Detection for Slippery and Uneven Surfaces
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Solution Overview
Problem
Existing autonomous robots lack the capability to detect and alert for hazardous conditions such as slipperiness, liquids, and uneven surfaces, which are common causes of falls and accidents, especially in elderly populations.
Innovation Solution
Equipping autonomous robots with sensors like tribometers, accelerometers, and cameras to detect surface characteristics, constructing feature vectors, and using supervised learning to map these vectors to confidence levels about hazard presence, triggering alerts when thresholds are met.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autonomous robots are equipped with sensors and hazard detection capabilities, then safety and reliability are improved, but device complexity increases
Solution Approach 1:
The robot system integrates multiple functions including autonomous navigation, surface scanning, hazard detection, and alert generation into a single platform. The sensors (tribometers, accelerometers, cameras) serve both navigation and hazard detection purposes, reducing the need for separate dedicated systems and managing complexity through functional integration.
Solution Approach 2:
A machine learning classifier acts as an intermediary between raw sensor data and hazard identification. The classifier processes feature vectors from multiple sensors and outputs confidence levels, mediating the complex interaction between diverse sensor inputs and the hazard detection decision-making process.
2Measurement precision
If multiple sensors are used to detect surface characteristics, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The hazard detection process is segmented into distinct stages: sensor data collection, feature vector construction, machine learning classification, and alert generation. Each sensor type (tribometer for friction, accelerometer for motion, camera for visual characteristics) captures specific surface properties, and the machine learning model integrates these segmented measurements to achieve comprehensive hazard identification.
3Reliability
If continuous monitoring is performed to detect hazards, then reliability is improved, but use of energy increases
Solution Approach 1:
The robot performs periodic surface scanning and hazard assessment during its autonomous navigation cycles. Rather than continuous monitoring, the system evaluates surface characteristics at regular intervals or at specific locations, allowing the robot to maintain reliability through repeated assessments while conserving energy by not operating sensors continuously.
Data Source
AI summary
A computer-implemented method includes detecting, by one or more sensors of a robot, one or more characteristics of a current point on a surface on which the robot travels. A feature vector is constructed to describe the current point on the surface on which the robot travels, based on the one or more characteristics. The feature vector is mapped to a confidence level that a hazard exists at the current point on the surface. It is determined that the confidence level meets a threshold confidence. An alert is issued in association with the current point on the surface, based on the confidence level meeting the threshold confidence.