Robotic force control by using estimated contact force

Mitigating collision is a fundamental issue in contact problems, and is required to ensure the safety of a robotic cell. Research into the contact problem between robots and their environment is divided into two parts: one uses the environmental contact model and parameter estimation, the other uses the robot force control method. There are two main problems with this research method. One is that the two research levels are not effectively combined to form a complete solution for force control in practice. The other problem is that research on excessive contact force in the collision phase has not been studied in depth for force control. In this paper, a sensing-executing bionic system is proposed that combines environmental detection and robotic force control based on the way an ant functions. The bionic system clearly explains the process from environment detection to robot control, which can provide guidance when designing a new robot control system. An adaptive switching control algorithm is proposed to mitigate the collision force in the collision phase. From the simulation results, the collision force is significantly reduced due to the implementation of adaptive switching control. Finally, a new self-sensing device is designed which can be integrated into the robot control device. However, as there are no additional sensors or computational complexity in the system, the effectiveness of the circuit and superiority of the adaptive parameter update must be verified by experimentation.

In this paper, we propose the use of a nonlinear disturbance-observer for estimation of contact forces during haptic interactions. Most commonly used impedance-type haptic interfaces employ open-loop force control under the assumption of pseudostatic interactions. Advanced force control in such interfaces can increase simulation fidelity through improvement of the transparency of the device. However, closed-loop force feedback is limited both due to the bandwidth limitations of force sensing and the associated cost of force sensors required for its implementation. Using a disturbance-observer, we estimate contact forces at the tool tip, then use these estimates for closed-loop control of the haptic interface. Simulation and experimental results, utilizing a custom single degree-of-freedom haptic interface, are presented to demonstrate the efficacy of the proposed disturbance-observer (DO)-based control approach. This approach circumvents the traditional drawbacks of force sensing while exhibiting the advantages of closed-loop force control in haptic devices. Results show that the proposed disturbance-observer can reliably estimate contact forces at the human-robot interface. The DO-based control approach is experimentally shown to improve haptic interface fidelity over a purely open-loop display while maintaining stable and vibration-free interactions between the human user and virtual environment.

Although tendon-driven continuum manipulators have been extensively researched, how to realize tip contact force sensing in a more general and efficient way without increasing the diameter is still a challenge. Rather than use a complex modeling approach, this paper proposes a general tip contact force-sensing method based on a recurrent neural network that takes the tendons’ position and tension as the input of a recurrent neural network and the tip contact force of the continuum manipulator as the output and fits this static model by means of machine learning so that it may be used as a real-time contact force estimator. We also designed and built a corresponding three-degree-of-freedom contact force data acquisition platform based on the structure of a continuum manipulator designed in our previous studies. After obtaining training data, we built and compared the performances of a multi-layer perceptron-based contact force estimator as a baseline and three typical recurrent neural network-based contact force estimators through TensorFlow framework to verify the feasibility of this method. We also proposed a manually decoupled sub-estimators algorithm and evaluated the advantages and disadvantages of those two methods.

Contact behaviors of medical devices, such as guidewires and catheters, are critical in endovascular surgeries. In this work, a new method to predict adhesive contact force between catheter and vascular artery is presented. Multi-asperity adhesion on the surface of vascular artery, deformation of asperity and deformation of vascular substrate are all considered. The single asperity behavior is described with Johnson-Kendall-Roberts (JKR) contact model. The multi-asperity behavior is based on Greenwood-Williamson (GW) asperity model. Vascular substrate is considered as elastic bulk substrate and its deformation is determined with Hertzian pressure from asperity on a circular region on the elastic half space. The model shows that the deformation of vascular substrate accounts for the majority of the total contact deformation and significantly affects the predicted contact force. The model is verified with published experimental data. The comparison shows that the model produces very accurate prediction of contact force between catheter and vascular artery when the contact force is compressive. Parametric analysis based on asperity topography is carried out. The analysis shows that the diameter of the circular region of the interface between asperity and vascular substrate has more significant effect on the estimation of contact force than the radius of asperity. Further validation of prediction accuracy of the model under experiment is needed.

Continuum robots such as robotic catheters are increasingly being used in minimally invasive surgery. Compliance contributes to enhanced safety during e.g. catheter insertion, however, estimation of contact force and location may help clinicians avoiding exerting excessive force. Ultimately this could lead to faster and safer interventions. Researchers proposed force sensors integrated in the catheter tip in the past. However, such sensors add extra complexity to the catheter design. Also, tip force sensors do not provide insights on forces that act along the catheter length. This paper proposes a data-driven approach for localizing contact forces that appear over the length of the catheter. The proposed approach consists of a collision detection method and a contact localization method. The framework only requires the measurement of the catheter’s shape which can be done by an embedded multi-core Fiber Bragg Grating fiber. The method was validated experimentally with a 3D-printed continuum robot with an integrated multi-core fiber. A second contact localization method which is based on identifying the discontinuity in the measured curvature, is also implemented and compared with the proposed method. The static and dynamic experiments show a mean average localization error of 2.3 mm and 4.3 mm which correspond to respectively 3.3% and 6.1% of a 70 mm long flexible robot. These findings demonstrate that the proposed framework outperforms the previous methods and yield promising results. The contact state estimation algorithm can detect collisions in at most approximately <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\text{1.08}\,\text{s}$</tex-math></inline-formula> .

Continuum robots enable safe and adaptive interaction with complex, unstructured, or constrained environments through continuous deformation, making them particularly suitable for medical and industrial applications. Accurate contact force sensing is essential to ensure safe and effective physical interaction in such scenarios. Although various embedded force sensors have been developed, sensor‐free approaches offer advantages in miniaturization, cost‐effectiveness, and biocompatibility. This review provides a comprehensive overview of sensor‐free contact force estimation methods for continuum robots, with an emphasis on algorithmic principles rather than specific continuum robot designs or applications. First, contact forces reported in the literature are systematically classified according to their distribution, components, and dynamics. Next, existing force estimation methods are divided into three categories: actuation‐based, deflection‐based, and environment‐based. For each category, the underlying algorithmic principles are discussed, representative challenges are highlighted, and their typical application scenarios are outlined. Finally, emerging trends and potential directions for future research are outlined.

An investigation on the relationship between grip, push and contact forces applied to a tool handle

The main objective of this paper deals with appropriate modelling (of a vehicle and the tyre–road contact) for online estimation of contact forces. This model will be helpful for trajectory monitoring, for steering control and also for diagnosis to avoid accidents or detection of oversteering or understeering situations. A robust observer is developed for adaptive estimation of the contact forces.

Cable-driven continuum robots are widely used for endoluminal intervention because of their dexterity and shape conforming steerability. However, body contact between the continuum robot and its surrounding anatomy is unavoidable, which imposes a potential safety risk, including vessel wall damage or even perforation. This paper presents an approach for body contact estimation of continuum robots with tension-profile sensing of actuation fibers. First, tension-sensing optical fibers with multiple inscribed fiber Bragg grating (FBG) sensors are used for both actuation and in-situ sensing of the continuum robot. Second, a beam theory-based mechanical model considering segmental differences, multiple fiber interactions and external force interactions is established, followed by robust estimation of contact positions and forces. Finally, detailed simulations are conducted to validate the accuracy and effectiveness of the proposed method. Experiments on a notched continuum robot are carried out, and the results show that the proposed approach can effectively recover in-situ segmental actuation forces without the need of explicit modeling of the friction between the fibers and guiding channels. The method enables the estimation of the number of contact points, as well as contact positions and contact forces along the body of the continuum robot.

In this paper, we propose the estimation of contact force in a collaborative robot without explicit force-sensing based on the adaptive third-order sliding mode observer. An adaptive third-order sliding mode observer was designed to estimate the contact force based only on the position measurement. The information from the observer was used for admittance control and emergent stop control when the robot interacts with a human. The experimental results with the emergent stop control and admittance control, using the proposed contact force estimation for the 3-DOF AT2-FARA robot manipulator, illustrate the capability of the proposed system in real-world application.

The paper presents an algorithm for estimating the contact information i.e. the contact force and the contact location. The estimation algorithm is based on the measurement of the part of joint torques which are caused by the external force. This torques may arise from a wide variety of contact situations, which makes this a complex nonlinear problem. Using some approximations, the complexity of the estimation problem is reduced and solved by the proposed algorithm based on nonlinear constrained optimization. In this paper only the robots with cylindrically shaped links are considered. The simulation and experimental results show that the proposed approach allows estimation of contact forces by using only joint torque sensors without any additional external sensory systems for detection of contacts along the robot body structure.

Adaptive neuro fuzzy estimation of underactuated robotic gripper contact forces

As the manipulation object of a patient transfer robot is a human, which can be considered a complex and time-varying system, motion adjustment of a patient transfer robot is inevitable and essential for ensuring patient safety and comfort. This paper proposes a motion adjustment method based on a two-level deep neural network (DNN) and a greedy algorithm. First, a dataset including information about human posture and contact forces is collected by experiment. Then, the DNN, which is used to estimate contact force, is established and trained with the collected datasets. Furthermore, the adjustment is conducted by comparing the estimated contact force of the next state and the real contact force of the current state by a greedy algorithm. To assess the validity, first, we employed the DNN to estimate contact force and obtained the accuracy and speed of 84% and 30 ms, respectively (implemented with an affordable processing unit). Then, we applied the greedy algorithm to a dual-arm transfer robot and found that the motion adjustment could reduce the contact force and improve human comfort efficiently; these validated the effectiveness of our proposal and provided a new approach to adjust the posture of the care receiver for improving their comfort through reducing the contact force between human and robot.

Although the conventional total knee arthoplasty (TKA) planning used 2D or 3D images of the patient’s lower extremity to determine bone alignment, it was shown that good bone alignment does not always guarantee successful results due to unbalanced joint contact forces and soft tissue tension. Thus, the joint contact force balance and soft tissue tension were pre-operatively calculated using finite element analysis (FEA). Although the results of FEA are accurate, the rigid body spring method (RBSM) has introduced for the joint contact force or pressure distribution due to the high costs for FEA. In this paper, an innovative method was described to quantify the pre- and post-operative knee contact force balance using various engineering techniques, including the free-form deformation (FFD) technique, and RBSM-based cartilage contact force and ligament tension estimation algorithm. Nine cases of TKA planning were performed with three different surgical options, including over-correction of the valgus ratio of the femur, tibial plateau cutting length, and MCL release to redistribute the medial and lateral contact force balance. This method is useful as a patient-specific preoperative planning tool in TKA surgery.

In this paper we extend our previous work concerning the regulation of the contact force in active train pantographs with wire-actuators. In particular, we consider the problem of estimating the contact force, instead of measuring it, by means of the measured displacements of the upper and lower pantograph frames. The estimation method, based on the algebraic observability theory, entails the use of real-time sliding-mode differentiators. It makes use explicitly of the upper frame mechanical characteristics, but does not require any other information concerning the system model. We consider our previously proposed second-order sliding mode control scheme, which also includes proper linear compensating cascade filters devoted to cope with the de-stabilizing effects of the resonant wire actuator. We show that such a control scheme can be effectively combined with the given contact force estimation method.