TY - JOUR
T1 - A Data-Driven Soft Sensor for Swarm Motion Speed Prediction using Ensemble Learning Methods
AU - Khaldi, Belkacem
AU - Harrou, Fouzi
AU - Benslimane, Sidi Mohammed
AU - Sun, Ying
N1 - KAUST Repository Item: Exported on 2021-06-09
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
UR - http://hdl.handle.net/10754/669445
UR - https://ieeexplore.ieee.org/document/9448037/
U2 - 10.1109/JSEN.2021.3087342
DO - 10.1109/JSEN.2021.3087342
M3 - Article
SN - 2379-9153
SP - 1
EP - 1
JO - IEEE Sensors Journal
JF - IEEE Sensors Journal
ER -