Interaction Force Estimation Using Extended State Observers: An Application to Impedance-Based Assistive and Rehabilitation Robotics

Sebastian, Gijo; Li, Zeyu; Crocher, Vincent; Kremers, Demy; Tan, Ying; Oetomo, Denny

doi:10.1109/lra.2019.2894908

Cited by 42 publications

(15 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The use of (6) has distinguished the driving torque and disturbance torque required for the movement. Since the unmodeled factors in the motion will also bring interference torque, we assume that the external moments suffered by the robot are all generated by external forces and can be equivalent to the foot position [29]. Then we can use (14) to get the external force of the foot [6].…”

Section: A Design Of Generalized Momentum Observermentioning

confidence: 99%

Contact Force Estimation Method of Legged-Robot and Its Application in Impedance Control

Zhang

et al. 2020

IEEE Access

View full text Add to dashboard Cite

Legged robots have extremely strong adaptability in different terrain environment and their stable walking in the wild is still a current research hotspot. Although they have shown great motion performance in recent years, most of them rely on high-performance sensors , especially force sensors settled on foot. However, it is impractical to select special forces sensors in the field environment, and the performance degradation and noise during the usage will bring fatal control defects. In order to solve this problem, we propose a foot contact force estimation method that does not require force sensors, and applies the estimated contact force to the impedance control of the legged robot to achieve the robot's active compliance control. The method of force estimation is based on the generalized momentum of the robot. It only needs to provide the position information of the joint and the information of the applied control torque. The designed feedforward and compensation terms effectively improve the speed and accuracy of the force estimation. We improved the response speed of the system to the contact force by improving the structure of the impedance control, and applied the estimated contact force to the impedance control to achieve the stable motion of the quadruped robot. Simulation experiments verify the effectiveness of the proposed force estimation method.

show abstract

Section: A Design Of Generalized Momentum Observermentioning

confidence: 99%

Contact Force Estimation Method of Legged-Robot and Its Application in Impedance Control

Zhang

et al. 2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…However, the performance of this scheme can be worsened by the neglected dynamic uncertainty. In addition, an extended state observer (ESO) was developed for estimating the time-varying external forces on a robotic manipulator [9]. Nevertheless, the model errors of the robot dynamic model reduce the accuracy of the ESO observer, and the high frequency tremors may limit the suitability of the method.…”

Section: Introductionmentioning

confidence: 99%

Disturbance Observer-Based Force Estimation and Fault Detection for Robotic Manipulator in Radioactive Environments

2022

View full text Add to dashboard Cite

Robotic systems with force sensing have great potential for use in radioactive environments. In this study, a modified observer-based method was developed to calculate the unknown external force without adding a redundant sensor and achieve the fault detection in the presence of a force sensor. A dynamic model of a serial robotic manipulator was built and the design procedure for a modified disturbance observer (MDO) was established. The output of observer was then used to suppress the disturbance and generate the fault signature. Moreover, the stability analysis shows that the convergence of the observer error is ultimately bounded. Simulation results under the step and composite sinusoidal disturbance torque demonstrate the performance of the force estimation and disturbance rejection. The results, obtained using the Kinova Jaco 2 robot manipulator, show that the estimated errors of the external force in X-Y-Z direction are bounded within ±0.5 N, ±2 N, and ±3 N, respectively. Finally, the effectiveness of fault detection is also verified by the experiment results.

show abstract

“…Additionally, adding weight to the robot (with wiring, if needed) regardless of the payload issues affects robot dynamics in the free movement scenario, making the identified robot mathematical models in the literature less accurate. These disadvantages can be circumvented by using methods to estimate the forces instead of direct measurements using physical sensors [3]. The estimated forces can complement physical measurements (and not just replace them, regardless of where the sensor is mounted), since they can be combined with force measurements for an additional source of information, a good example of being able to "distinguish the effects of inertial forces due to acceleration of a heavy tool from contact forces with the environment, as both these forces would show up in the measurements from a force sensor located at the robot wrist" [4].…”

Section: Introductionmentioning

confidence: 99%

End-Effector Force and Joint Torque Estimation of a 7-DoF Robotic Manipulator Using Deep Learning

et al. 2021

View full text Add to dashboard Cite

When a mobile robotic manipulator interacts with other robots, people, or the environment in general, the end-effector forces need to be measured to assess if a task has been completed successfully. Traditionally used force or torque estimation methods are usually based on observers, which require knowledge of the robot dynamics. Contrary to this, our approach involves two methods based on deep neural networks: robot end-effector force estimation and joint torque estimation. These methods require no knowledge of robot dynamics and are computationally effective but require a force sensor under the robot base. Several different architectures were considered for the tasks, and the best ones were identified among those tested. First, the data for training the networks were obtained in simulation. The trained networks showed reasonably good performance, especially using the LSTM architecture (with a root mean squared error (RMSE) of 0.1533 N for end-effector force estimation and 0.5115 Nm for joint torque estimation). Afterward, data were collected on a real Franka Emika Panda robot and then used to train the same networks for joint torque estimation. The obtained results are slightly worse than in simulation (0.5115 Nm vs. 0.6189 Nm, according to the RMSE metric) but still reasonably good, showing the validity of the proposed approach.

show abstract

Interaction Force Estimation Using Extended State Observers: An Application to Impedance-Based Assistive and Rehabilitation Robotics

Cited by 42 publications

References 22 publications

Contact Force Estimation Method of Legged-Robot and Its Application in Impedance Control

Contact Force Estimation Method of Legged-Robot and Its Application in Impedance Control

Disturbance Observer-Based Force Estimation and Fault Detection for Robotic Manipulator in Radioactive Environments

End-Effector Force and Joint Torque Estimation of a 7-DoF Robotic Manipulator Using Deep Learning

Contact Info

Product

Resources

About