Autonomous Robot Collision Detection Using Multi-Sensor LSTM
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Solution Overview
Problem
Autonomous robots face challenges in detecting collisions with obstacles on their path due to the limitations of existing sensor systems, which often provide disparate and unreliable information, leading to instability in collision detection and response.
Innovation Solution
A method and system that combines input images from cameras, LiDAR distance information, and acceleration data using a Long Short-Term Memory (LSTM) model and a Fully Connected Neural Network (FNN) model to learn and determine collision situations, optimizing the training models based on a cost function to improve collision detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple sensor types (camera, LiDAR, acceleration sensor) are used to detect collisions, then the coverage and information diversity improve, but the stability and reliability of collision detection deteriorate due to disparate and unreliable sensor information
Solution Approach 1:
The patent combines data from multiple sensor types (camera, LiDAR, acceleration sensor) into a unified collision detection framework using deep learning models. The LSTM network processes temporal sequences of multi-sensor data, while the FNN integrates features from different sensor modalities, merging disparate information streams into coherent collision predictions that improve both coverage and stability.
Solution Approach 2:
The patent introduces deep learning models (LSTM and FNN) as intermediary processing layers between raw sensor data and collision detection outputs. These intermediary models learn to reconcile discrepancies between different sensor types, transforming unreliable disparate information into stable and reliable collision predictions through trained feature extraction and pattern recognition.
2Measurement precision
If sensor data from different types is integrated, then the collision detection accuracy improves, but the system complexity increases
Solution Approach 1:
The patent segments the collision detection system into distinct functional modules: data acquisition from multiple sensors, LSTM network for temporal sequence processing, FNN for feature integration, and output prediction. This segmentation allows each component to handle specific aspects of the complex task, managing overall system complexity while maintaining high detection accuracy through specialized processing at each stage.
Solution Approach 2:
The patent employs universal deep learning models (LSTM and FNN) that can process multiple types of sensor data (image, distance, acceleration) through unified architectures. These multi-functional models handle diverse input modalities using the same computational framework, reducing the need for separate processing systems for each sensor type and thereby managing complexity while preserving accuracy.
3Measurement precision
If LSTM and FNN models are used to process multi-sensor data, then the collision prediction accuracy improves, but the computational time and processing load increase
Solution Approach 1:
The patent performs preliminary processing of sensor data by the LSTM network, which extracts temporal features and prepares refined inputs for the FNN stage. This preliminary action reduces the computational burden on the subsequent FNN model, allowing the system to maintain high prediction accuracy while optimizing overall processing time through staged feature extraction and preparation.
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 enhances the stability of collision detection by integrating diverse sensor data, enabling timely alerts and responses to potential collisions, thus ensuring the safe operation of autonomous robots.
Implementation Method 1
light detection and ranging (LiDAR) distance information
Data Source
AI summary
A method and system for detecting a collision by an autonomous robot based on a multi-sensor long short-term memory (LSTM) are disclosed. The method includes generating an input of an LSTM model by combining an input image received from an autonomous robot, light detection and ranging (LiDAR) distance information, and acceleration information, learning a collision alert situation by inputting the input to the LSTM model, and determining a collision situation using an output of the LSTM model and a fully connected neural network (FNN) model.