AMR Object Recognition for Situation-Aware Mapping and Task Adaptation

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Solution Overview

Problem

Autonomous mobile robots (AMRs) face challenges in accurately recognizing and classifying objects in their operational area, leading to inefficient task execution and adaptation to changing situations, as they often rely on basic obstacle detection without identifying specific objects or evaluating complex situations.

Innovation Solution

The method involves navigating AMRs using navigation sensors, detecting objects with classifiers, and assigning object classes, which enables the creation of detailed maps and adaptive behavior based on situation assessment, incorporating data from various sensors and external sources to improve object recognition and situation evaluation.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Device complexity

If basic obstacle detection is used, then device complexity is reduced, but object recognition accuracy deteriorates

Engineering Contradiction:

Improvesensor system complexityVSAvoidobject recognition accuracy

Core Design Contradiction:

Device complexityVSMeasurement precision

Solution Approach 1:

The patent combines multiple sensor types (navigation sensors, first sensors for object detection, second sensors for situation assessment) into an integrated sensing system. This merging allows the robot to achieve high object recognition accuracy through data fusion while managing complexity through unified sensor management and coordinated operation of multiple sensor subsystems.

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The navigation sensors serve dual functions: they detect obstacles for basic navigation and simultaneously provide data for object detection and situation assessment. This multi-functionality allows the system to achieve high measurement precision without proportionally increasing device complexity, as existing sensors are utilized for multiple purposes.

Inventive Principle:

Principle #6Universality (Multi-functionality)

2Productivity

If detailed object classification is implemented, then task execution efficiency is improved, but processing time increases

Engineering Contradiction:

Improvetask execution efficiencyVSAvoidobject processing time

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The system performs preliminary situation assessment and object classification during navigation, before specific tasks require detailed object information. By pre-processing and categorizing objects as the robot moves through the environment, the system prepares data in advance, reducing processing time when tasks are executed and improving overall task execution efficiency without significant time loss.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent replaces traditional mechanical or rule-based object identification systems with classifier-based automated detection systems. These classifiers automatically categorize objects based on sensor data, significantly reducing processing time compared to manual or complex mechanical identification methods, thereby improving task execution efficiency while minimizing time loss.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Adaptability or versatility

If situation assessment capabilities are added, then adaptability is improved, but device complexity increases

Engineering Contradiction:

Improvesituation assessment capabilityVSAvoidsystem architecture complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The situation assessment system is segmented into distinct functional modules: navigation sensors for basic movement, first sensors for object detection, second sensors for situation assessment, and classifiers for data processing. This segmentation allows adaptability to be improved through modular addition of assessment capabilities while managing complexity by keeping each module independent and focused on specific functions.

Inventive Principle:

Principle #1Segmentation

4Reliability

If multiple sensors are integrated, then information accuracy is improved, but device complexity increases

Engineering Contradiction:

Improveinformation accuracyVSAvoidsensor integration complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent introduces classifiers as intermediary processing units that receive data from multiple sensors and integrate the information systematically. These classifiers act as mediators that fuse data from navigation sensors, first sensors, and second sensors, improving information accuracy through coordinated multi-sensor input while managing integration complexity by providing a standardized data processing interface.

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS20250013243A1Situation Assessment By Way of Object Recognition in Autonomous Mobile Robots

Publication Date: 2025.01.09 PAPST LICENSING GMBH & CO KG

AI summary

A description is given of a method for an autonomous mobile robot (AMR). According to one exemplary embodiment, the method comprises navigating the AMR through an operational area with the aid of one or more navigation sensors: acquiring information about the surroundings of the AMR in the operational area: automatically detecting subareas within the operational area and classifying the detected subareas by way of a classifier based on the acquired information wherein an area class is determined: and storing detected subareas, including the ascertained area class, in an electronic map of the AMR.