Abstract
Soft robotics promises new opportunities for solving problems that were limited by rigid robots due to their compliant physical structure. Pneumatic Soft Actuators (PSA) are a class of soft robots that have gained popularity due to their cost-effective manufacturing and high force-generating capability. Due to their highly nonlinear dynamics, accurate prediction of their response to input pressure (curvature) as well as force output is challenging. Artificial Neural Networks were employed to this effect. This work shows a comparative analysis of three variants of the recurrent neural network used in this paper for predicting the behavior of soft robotic actuators. Each network was trained using the same data set to predict the position, curvature, and applied force by the soft robotics on an external force. The result shows the different performance of the three models, evaluating them using Mean Square Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). The Long Short-term Memory (LSTM) model outperformed both the Gated Recurrent Unit (GRU) and Recurrent Neural Network (RNN) models, producing the lowest error in the three metrics.
Original language | English |
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Title of host publication | 2023 8th International Conference on Robotics and Automation Engineering, ICRAE 2023 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 259-264 |
Number of pages | 6 |
ISBN (Electronic) | 9798350327656 |
DOIs | |
Publication status | Published - 14 Mar 2024 |
Event | 8th International Conference on Robotics and Automation Engineering, ICRAE 2023 - Singapore, Singapore Duration: 17 Nov 2023 → 19 Nov 2023 |
Conference
Conference | 8th International Conference on Robotics and Automation Engineering, ICRAE 2023 |
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Country/Territory | Singapore |
City | Singapore |
Period | 17/11/23 → 19/11/23 |
Keywords
- GRU
- LSTM
- Pneumatic Soft Actuator (PSA)
- prediction
- RNN