Robot Cleaner AI Escape Path Planning to Avoid Stuck Situations

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Solution Overview

Problem

Robot cleaners often get stuck in confined situations and lack effective methods to actively avoid such situations, leading to inefficient cleaning and potential damage.

Innovation Solution

The implementation of artificial intelligence (AI) in robot cleaners to detect stuck situations using surrounding maps and compensation models, determining optimal escape paths through reinforcement learning, and controlling the driving motor to navigate along these paths.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If the robot cleaner uses traditional obstacle avoidance methods based on distance detection, then it can navigate simple environments, but it cannot actively cope with new stuck situations in confined spaces

Engineering Contradiction:

Improveability to cope with stuck situationsVSAvoidcomplexity of avoidance method

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The robot performs preliminary actions by acquiring surrounding map data and determining escape path factors before actually encountering a stuck situation. The processor pre-processes environmental information and prepares avoidance strategies, enabling the robot to respond more effectively when stuck situations occur without requiring complex real-time decision-making mechanisms

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent introduces an intermediary computational layer (processor) that mediates between simple distance detection sensors and the driving motor. This intermediary processes surrounding map data, determines escape path factors, and generates appropriate driving commands, allowing the robot to handle complex stuck situations without requiring complex hardware modifications

Inventive Principle:

Principle #24Intermediary (Mediator)

2Reliability

If the robot cleaner continuously monitors and actively avoids stuck situations using AI, then it can rapidly escape from confined areas, but it consumes more power

Engineering Contradiction:

Improveeffectiveness of stuck situation avoidanceVSAvoidpower consumption

Core Design Contradiction:

ReliabilityVSUse of energy by moving object

Solution Approach 1:

The robot applies partial action by selectively activating advanced avoidance mechanisms only when stuck situations are detected. Instead of continuously performing complex AI processing, the system monitors for stuck conditions and applies intensive computational resources (acquiring surrounding map data, determining escape path factors) only when necessary, thereby reducing overall power consumption while maintaining high reliability

Inventive Principle:

Principle #16Partial or excessive action

Solution Approach 2:

The system implements feedback by continuously monitoring driving conditions and detecting when the robot encounters a stuck situation. This feedback mechanism allows the robot to adjust its behavior dynamically - maintaining low power consumption during normal operation and activating energy-intensive AI processing only when avoidance actions are required, thus balancing reliability and energy efficiency

Inventive Principle:

Principle #23Feedback

3Productivity

If the robot cleaner gets stuck frequently, then cleaning efficiency decreases, but implementing advanced avoidance methods increases device complexity

Engineering Contradiction:

Improvecleaning efficiencyVSAvoidcomplexity of control system

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The robot performs preliminary actions by pre-acquiring surrounding map data and pre-determining escape path factors before cleaning operations are significantly disrupted by stuck situations. This preliminary processing enables faster response times and more effective avoidance maneuvers, thereby maintaining cleaning efficiency without requiring overly complex real-time control systems

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent substitutes mechanical complexity with computational intelligence. Instead of using complex mechanical structures or multiple specialized sensors to prevent stuck situations, the system replaces mechanical complexity with software-based AI processing that analyzes surrounding map data and determines escape paths, thereby maintaining productivity while avoiding excessive device complexity

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS11318614B2Robot cleaner and operating method thereof

Publication Date: 2022.05.03 LG ELECTRONICS INC

AI summary

A robot cleaner to avoid a stuck situation through artificial intelligence (AI) may acquire a surrounding map, based on the sensing information, determine escape path factors based on the surrounding map by using the compensation model, if the stuck situation of the robot cleaner is detected, and control the driving motor such that the robot cleaner travels, based on the determined escape path factors.