Robot Surface Proximity Detection Using Piezoelectric Acoustic Waves
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Solution Overview
Problem
Existing collision avoidance systems for robotic manipulators face challenges in accurately detecting obstacles at short ranges, especially in cluttered environments, due to limitations of camera-based methods and directional sensors, which are costly and have blind spots, complicating robotic system design and increasing costs.
Innovation Solution
A proximity detection system using piezoelectric elements to generate and receive acoustic waves, filtered through band-pass and linear time-invariant filters, and analyzed by a neural network to detect obstacles, providing full surface and omnidirectional detection without dead spots.
Engineering Contradictions & Design Principles
Engineering 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 performance suffers in poor light conditions, with occlusions, and at very short ranges
Solution Approach 1:
The patent replaces camera-based optical detection with acoustic wave-based detection using piezoelectric elements. This substitution enables reliable obstacle detection at very short ranges (including less than 10 cm) and in conditions where cameras fail, such as poor lighting, occlusions, and with transparent or mirrored objects. The acoustic waves propagate along the robot surface and are reflected by obstacles, providing detection capability independent of visual conditions.
2Length of stationary object
If LiDAR or millimeter wave radar is used for short-range detection, then detection range is improved, but cost increases and device complexity increases due to need for multiple sensors
Solution Approach 1:
The patent makes the robot surface itself multi-functional by integrating piezoelectric elements that both generate and detect acoustic waves directly on the surface. This eliminates the need for separate directional sensors like LiDAR or radar, as the entire surface becomes an omnidirectional detection system. A single integrated system provides full-surface coverage without blind spots, reducing both cost and complexity while maintaining effective short-range detection.
3Area of stationary object
If multiple directional sensors are distributed throughout the robot to eliminate blind spots, then detection coverage is improved, but device complexity and cost increase significantly
Solution Approach 1:
The patent transitions from discrete point-based directional sensors to a continuous surface-based detection system. By propagating acoustic waves along the robot surface and detecting reflections at multiple points, the system achieves omnidirectional coverage across the entire surface area. This dimensional approach—moving from 0D point sensors to 2D surface integration—provides complete coverage without requiring multiple separate sensor components or complex spatial distribution.
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
The system achieves reliable, low-cost, and efficient proximity detection across the entire robot surface with minimal modifications, ensuring high true positive rates and zero false positives for obstacle detection.
Implementation Method 1
a first one among a plurality of piezoelectric elements disposed adjacent to a surface of an object generates an acoustic wave along the surface of the object
Implementation Method 2
a second one among the piezoelectric elements receives an acoustic wave signal corresponding to the generated acoustic wave
Data Source
AI summary
An apparatus for collision avoidance by surface proximity detection includes a plurality of piezoelectric elements disposed adjacent to a surface of an object, 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 the surface of the object, and receive, via a second one among the piezoelectric elements, an acoustic wave signal corresponding to the generated acoustic wave. The at least one processor is further configured to execute the instructions to 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, using a linear time-invariant filter, and detect whether an obstacle is proximate to the surface of the object by inputting the obtained proximity signal into a neural network.