Movable object and control method thereof

Resolve Bottlenecks,
Find Innovative Solutions
Generate Solutions

Solution Overview

Problem

Movable objects face challenges in accurately determining their location amidst changing environmental factors, such as variations in illumination or feature point extraction difficulties, using existing control methods.

Innovation Solution

A method and system for a movable object that involves acquiring omnidirectional images, converting them to panoramic images, and using signal strength to generate maps, which are then applied to algorithms like deep neural networks to determine location and orientation, enabling the object to adapt and navigate effectively.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If feature point extraction is used for location determination, then location accuracy can be achieved, but environmental changes such as illumination variations cause recognition failures

Engineering Contradiction:
Improvelocation determination accuracyVSAvoidenvironmental adaptability
Core Design Contradiction:
Measurement precisionVSAdaptability or versatility

Solution Approach 1:

The patent transforms the imaging parameter from conventional 2D plane images to 360-degree panoramic images, fundamentally changing the spatial representation parameter. This allows the system to capture complete environmental information around the movable object, making location determination robust against illumination changes and feature point variations in any single direction

Inventive Principle:
Principle #35Parameter changes

Solution Approach 2:

The panoramic image serves multiple functions simultaneously: it provides complete environmental context for location determination, maintains accuracy under varying illumination conditions, and works across different environmental scenarios. The single panoramic capture replaces multiple conventional images that would be needed to achieve the same universality

Inventive Principle:
Principle #6Universality (Multi-functionality)

2Device complexity

If conventional 2D images are used for mapping, then device complexity is reduced, but location recognition accuracy deteriorates under environmental changes

Engineering Contradiction:
Improveimaging system complexityVSAvoidlocation recognition accuracy
Core Design Contradiction:
Device complexityVSMeasurement precision

Solution Approach 1:

The patent applies spherical geometry by capturing 360-degree panoramic images that wrap around the movable object in all horizontal directions. This spherical field of view provides comprehensive environmental context, enabling accurate location determination even when specific features are obscured or illuminated differently, without requiring complex multi-camera arrays

Inventive Principle:
Principle #14Spheroidality (Curvature)

3Measurement precision

If multiple separate control means are used, then control precision is improved, but ease of operation deteriorates

Engineering Contradiction:
Improvecontrol precisionVSAvoidcontrol convenience
Core Design Contradiction:
Measurement precisionVSEase of operation

Solution Approach 1:

The patent merges multiple control functions (location determination, environmental mapping, navigation planning) into a single integrated control method that processes panoramic images and signal strengths unifiedly. This consolidation maintains precision by using comprehensive data while improving ease of operation by eliminating the need for separate control systems for each function

Inventive Principle:
Principle #5Merging (Combining)

Data Source

PatentEP3336648B1Movable object and control method thereof
Publication Date: 2022.06.15 SAMSUNG ELECTRONICS CO LTD
  • EP3336648B1 patent drawingFigure 1
  • EP3336648B1 patent drawingFigure 2
  • EP3336648B1 patent drawingFigure 3

AI summary

Disclosed herein are a movable object and a movable object control method. The movable object control method may include acquiring an image of a movable object's surroundings, acquiring a signal having strength changing depending on a location of the movable object, generating a map on the basis of the signal and the image of the surroundings of the movable object, and applying the map to an algorithm to acquire a learned algorithm.