Journal of Bionic Engineering ›› 2024, Vol. 21 ›› Issue (6): 2792-2803.doi: 10.1007/s42235-024-00582-8
Multi-Sensor Fusion for State Estimation and Control of Cable-Driven Soft Robots
Jie Ma1,2 · Jinzhou Li3 · Yan Yang1,2 · Wenjing Hu1 · Li Zhang3 · Zhijie Liu1,2
- 1. School of Intelligence Science and Technology, University of Science and Technology Beijing, Beijing 100083, People’s Republic of China 2. Key Laboratory of Intelligent Bionic Unmanned Systems, Ministry of Education, University of Science and Technology Beijing, Beijing 100083, People’s Republic of China 3. School of Artificial Intelligence, China University of Mining and Technology, Beijing 100083, People’s Republic of China
Multi-Sensor Fusion for State Estimation and Control of Cable-Driven Soft Robots
Jie Ma1,2 · Jinzhou Li3 · Yan Yang1,2 · Wenjing Hu1 · Li Zhang3 · Zhijie Liu1,2
- 1. School of Intelligence Science and Technology, University of Science and Technology Beijing, Beijing 100083, People’s Republic of China 2. Key Laboratory of Intelligent Bionic Unmanned Systems, Ministry of Education, University of Science and Technology Beijing, Beijing 100083, People’s Republic of China 3. School of Artificial Intelligence, China University of Mining and Technology, Beijing 100083, People’s Republic of China
摘要： Cable-driven soft robots exhibit complex deformations, making state estimation challenging. Hence, this paper develops a multi-sensor fusion approach using a gradient descent strategy to estimate the weighting coefficients. These coefficients combine measurements from proprioceptive sensors, such as resistive flex sensors, to determine the bending angle. Additionally, the fusion strategy adopted provides robust state estimates, overcoming mismatches between the flex sensors and soft robot dimensions. Furthermore, a nonlinear differentiator is introduced to filter the differentiated sensor signals to address noise and irrational values generated by the Analog-to-Digital Converter. A rational polynomial equation is also introduced to compensate for temperature drift exhibited by the resistive flex sensors, which affect the accuracy of state estimation and control. The processed multi-sensor data is then utilized in an improved PD controller for closed-loop control of the soft robot. The controller incorporates the nonlinear differentiator and drift compensation, enhancing tracking performance. Experimental results validate the effectiveness of the integrated approach, demonstrating improved tracking accuracy and robustness compared to traditional PD controllers.Cable-driven soft robots exhibit complex deformations, making state estimation challenging. Hence, this paper develops a multi-sensor fusion approach using a gradient descent strategy to estimate the weighting coefficients. These coefficients combine measurements from proprioceptive sensors, such as resistive flex sensors, to determine the bending angle. Additionally, the fusion strategy adopted provides robust state estimates, overcoming mismatches between the flex sensors and soft robot dimensions. Furthermore, a nonlinear differentiator is introduced to filter the differentiated sensor signals to address noise and irrational values generated by the Analog-to-Digital Converter. A rational polynomial equation is also introduced to compensate for temperature drift exhibited by the resistive flex sensors, which affect the accuracy of state estimation and control. The processed multi-sensor data is then utilized in an improved PD controller for closed-loop control of the soft robot. The controller incorporates the nonlinear differentiator and drift compensation, enhancing tracking performance. Experimental results validate the effectiveness of the integrated approach, demonstrating improved tracking accuracy and robustness compared to traditional PD controllers.