.png){kind=icon}
.png){kind=icon}
Return
A Data-Driven Soft Sensor for Swarm Motion Speed Prediction Using Ensemble Learning Methods
.png){kind=icon}
PRE
AI
DOI：10.1109/JSEN.2021.3087342.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Machine Learning (ML) for swarm motion prediction is a relatively unexplored area that could help sustain and monitor daily swarm robotics collective tasks. This paper focuses on a specific application of swarm robotics which is pattern formation, to demonstrate the ability of Ensemble Learning (EL) approaches to predict the motion speed of swarm robots. Specifically, the boosted trees (BST) and bagged trees (BT) algorithms are introduced to predict the motion speed of a swarm of miniature two-wheels differential driver mobile robots performing a circle-formation via the viscoelastic control model. This choice's motivation is due to EL-based models' ability to improve the performance of ML models by combining multiple learners versus single regressors. Both BST and BT algorithms' performances are compared to ten commonly known prediction models based on Support Vector Regressors (SVRs) and Gaussian Process Regressors (GPRs) with different kernel functions. Using simulated measurements recorded every 0.1 second from the robots' sensors, we demonstrate the effectiveness of the developed methods over conventional ML models (SVR and GPR) in a free/non-free obstacles environment. Results showed that the BST and BT regression models reached the highest prediction performance with fully and partially connected swarms and even when involving different swarm sizes.
Keywords:
Predictive models
Swarm robotics
Mathematical model
Robot sensing systems
Prediction algorithms
Task analysis
Mobile robots
Swarm robotics
swarm motion speed prediction
ensemble learning
boosted trees
bagged trees
support vector regressors
Gaussian process regressors
Journal
IF:
4.5
Papers:
2.1W
Citations:
7.3W