Piezoelectric Surface-Wave Sensing for Robot Collision Avoidance

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Solution Overview

Problem

Existing collision avoidance systems for robotic manipulators face challenges in accurately detecting obstacles in cluttered environments, particularly at short ranges, due to limitations of camera-based methods and directional proximity sensors, which are costly and prone to occlusions and blind spots.

Innovation Solution

A system utilizing piezoelectric elements to generate and receive acoustic waves through leaky surface waves (LSW) for full surface proximity detection, combined with signal processing and a neural network to predict obstacle proximity and control robot movements to avoid collisions.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If camera-based object recognition or 3D shape reconstruction is used for collision avoidance, then obstacle detection capability is improved, but detection accuracy at short range deteriorates and the system fails with transparent or mirrored objects

Engineering Contradiction:
Improveobstacle detection accuracyVSAvoiddetection reliability in cluttered environments
Core Design Contradiction:
Measurement precisionVSReliability

Solution Approach 1:

The patent replaces optical sensing systems (cameras) with acoustic sensing systems (piezoelectric elements). The mechanical/acoustic wave-based detection method uses piezoelectric elements to generate and detect acoustic waves that reflect off obstacles, providing reliable short-range detection in cluttered environments where camera-based systems fail due to occlusions, lighting conditions, and transparent objects.

Inventive Principle:
Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent utilizes mechanical vibration in the form of acoustic waves generated by piezoelectric elements. These acoustic waves propagate through the environment and reflect off obstacles, with the reflected waves being detected by piezoelectric sensors. This vibration-based approach enables reliable obstacle detection at short ranges independent of visual conditions.

Inventive Principle:
Principle #18Mechanical vibration

2Measurement precision

If LiDAR or millimeter wave radar is used for short-range detection, then detection accuracy is improved, but system cost and complexity increase significantly

Engineering Contradiction:
Improveshort-range detection accuracyVSAvoidsensor distribution and management complexity
Core Design Contradiction:
Measurement precisionVSDevice complexity

Solution Approach 1:

The patent employs inexpensive piezoelectric elements instead of expensive LiDAR or millimeter wave radar systems. These piezoelectric elements can be easily manufactured and deployed in multiple locations on the robot without significantly increasing system cost, providing a cost-effective solution for omnidirectional proximity detection.

Inventive Principle:
Principle #27Cheap short-living objects (Disposable)

Solution Approach 2:

The piezoelectric elements serve multiple functions: they can both generate acoustic waves and detect reflected acoustic waves. This dual functionality reduces the need for separate transmitter and receiver components, simplifying the overall sensor system while maintaining omnidirectional detection capability.

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

3Area of stationary object

If directional proximity sensors are distributed throughout the robot to eliminate blind spots, then coverage is improved, but system cost and sensor management overhead increase significantly

Engineering Contradiction:
Improvedetection coverage areaVSAvoidnumber of sensors and management overhead
Core Design Contradiction:
Area of stationary objectVSDevice complexity

Solution Approach 1:

The patent merges the functions of multiple directional sensors into a unified acoustic wave-based detection system. By using piezoelectric elements that generate and detect acoustic waves propagating in multiple directions, the system achieves omnidirectional coverage with fewer components, reducing both cost and management complexity while eliminating blind spots.

Inventive Principle:
Principle #5Merging (Combining)

Applied Scientific Principles

This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.

Function Achieved in This Case

Enables reliable, low-cost, omnidirectional proximity detection with no dead spots, requiring minimal modifications to the robot, and achieving high accuracy in detecting approaching obstacles within 10 cm, thus enhancing safety and efficiency in dynamic environments.

Implementation Method 1

control a first one among piezoelectric elements disposed on a surface of an object, to generate an acoustic wave along the surface of the object

Methodology Applied
Scientific EffectPiezoelectric effect: Piezoelectric Effect

Implementation Method 2

receive, via a second one among the piezoelectric elements, an acoustic wave signal corresponding to the generated acoustic wave

Methodology Applied
Scientific EffectPiezoelectric effect: Converse Piezoelectric Effect

Data Source

PatentEP4217154B1Method and apparatus for robot collision avoidance by full surface proximity detection
Publication Date: 2025.10.29 SAMSUNG ELECTRONICS CO LTD
  • EP4217154B1 patent drawingFigure 1A
  • EP4217154B1 patent drawingFigure 1B
  • EP4217154B1 patent drawingFigure 2~3A

AI summary

An apparatus includes a plurality of piezoelectric elements, a memory storing instructions, and at least one processor configured to execute the instructions to control a first one among the piezoelectric elements to generate an acoustic wave along a surface of an object, receive, via a second one among the piezoelectric elements, an acoustic wave signal corresponding to the generated acoustic wave, filter the received acoustic wave signal, using a band-pass filter for reducing noise of the received acoustic wave signal, obtain a proximity signal for proximity detection, from the filtered acoustic wave signal, by using a linear time-invariant filter, predict whether an obstacle is within a first distance from the surface of the object by inputting the obtained proximity signal to a neural network. and control the object to avoid collision with the obstacle, based on the obstacle being within the first distance from the surface of the object.