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Mallapragada, Vishnu, Duygun Erol, and Nilanjan Sarkar. "A New Method of Force Control for Unknown Environments." International Journal of Advanced Robotic Systems 4, no. 3 (September 1, 2007): 34. http://dx.doi.org/10.5772/5684.
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Current robotic systems are expected to interact with unknown environment where controlling the interaction forces becomes an important issue. We propose a new control technique for force control on unknown environments that tunes the force controller based on online estimation of the environment parameters. However, the proposed approach overcomes the need for precise estimation of environment parameters, which are needed in many system identification-based force control approaches. This framework uses an artificial neural network (ANN)-based proportional-integral (PI)-gain scheduling force controller to track the desired force by adjusting control gains such that error in parameter estimation can be accommodated. Experimental results are presented to demonstrate the efficacy of the proposed control framework. Finally, the advantages and limitations of the proposed controller are discussed.
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Körding, Konrad P., Shih-pi Ku, and Daniel M. Wolpert. "Bayesian Integration in Force Estimation." Journal of Neurophysiology 92, no. 5 (November 2004): 3161–65. http://dx.doi.org/10.1152/jn.00275.2004.
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When we interact with objects in the world, the forces we exert are finely tuned to the dynamics of the situation. As our sensors do not provide perfect knowledge about the environment, a key problem is how to estimate the appropriate forces. Two sources of information can be used to generate such an estimate: sensory inputs about the object and knowledge about previously experienced objects, termed prior information. Bayesian integration defines the way in which these two sources of information should be combined to produce an optimal estimate. To investigate whether subjects use such a strategy in force estimation, we designed a novel sensorimotor estimation task. We controlled the distribution of forces experienced over the course of an experiment thereby defining the prior. We show that subjects integrate sensory information with their prior experience to generate an estimate. Moreover, subjects could learn different prior distributions. These results suggest that the CNS uses Bayesian models when estimating force requirements.
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Roveda, Loris, and Dario Piga. "Sensorless environment stiffness and interaction force estimation for impedance control tuning in robotized interaction tasks." Autonomous Robots 45, no. 3 (March 2021): 371–88. http://dx.doi.org/10.1007/s10514-021-09970-z.
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AbstractIndustrial robots are increasingly used to perform tasks requiring an interaction with the surrounding environment (e.g., assembly tasks). Such environments are usually (partially) unknown to the robot, requiring the implemented controllers to suitably react to the established interaction. Standard controllers require force/torque measurements to close the loop. However, most of the industrial manipulators do not have embedded force/torque sensor(s) and such integration results in additional costs and implementation effort. To extend the use of compliant controllers to sensorless interaction control, a model-based methodology is presented in this paper. Relying on sensorless Cartesian impedance control, two Extended Kalman Filters (EKF) are proposed: an EKF for interaction force estimation and an EKF for environment stiffness estimation. Exploiting such estimations, a control architecture is proposed to implement a sensorless force loop (exploiting the provided estimated force) with adaptive Cartesian impedance control and coupling dynamics compensation (exploiting the provided estimated environment stiffness). The described approach has been validated in both simulations and experiments. A Franka EMIKA panda robot has been used. A probing task involving different materials (i.e., with different - unknown - stiffness properties) has been considered to show the capabilities of the developed EKFs (able to converge with limited errors) and control tuning (preserving stability). Additionally, a polishing-like task and an assembly task have been implemented to show the achieved performance of the proposed methodology.
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Shushtari, Mohammad, and Arash Arami. "Human–Exoskeleton Interaction Force Estimation in Indego Exoskeleton." Robotics 12, no. 3 (May 1, 2023): 66. http://dx.doi.org/10.3390/robotics12030066.
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Accurate interaction force estimation can play an important role in optimizing human–robot interaction in an exoskeleton. In this work, we propose a novel approach for the system identification of exoskeleton dynamics in the presence of interaction forces as a whole multibody system without imposing any constraints on the exoskeleton dynamics. We hung the exoskeleton through a linear spring and excited the exoskeleton joints with chirp commands while measuring the exoskeleton–environment interaction force. Several structures of neural networks were trained to model the exoskeleton passive dynamics and estimate the interaction force. Our testing results indicated that a deep neural network with 250 neurons and 10 time–delays could obtain a sufficiently accurate estimation of the interaction force, resulting in an RMSE of 1.23 on Z–normalized applied torques and an adjusted R2 of 0.89.
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Ohishi, Kiyoshi, Masaru Miyazaki, and Masahiro Fujita. "Hybrid Position and Force Control Without Force Sensor." Journal of Robotics and Mechatronics 8, no. 3 (June 20, 1996): 226–34. http://dx.doi.org/10.20965/jrm.1996.p0226.
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Generally, hybrid control is realized by sensor signal feedback of position and force. However, some robot manipulators do not have a force sensor due to the environment. Moreover, a precise force sensor is very expensive. In order to overcome these problems, we propose the estimation system of reaction force without using a force sensor. This system consists of the torque observer and the inverse dynamics calculation. Using both this force estimation system and <I>H</I>∞ acceleration controller which is based on <I>H</I>∞ control theory, it takes into account the frequency characteristics of both sensor noise effect and disturbance rejection. The experimental results in this paper illustrate the fine hybrid control of the three tested degrees-of-freedom DD robot manipulator without force sensor.
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Azimifar, Farhad, Saman Ahmadkhosravi Rozi, Ahmad Saleh, and Iman Afyouni. "Transparency performance improvement for multi-master multi-slave teleoperation systems with external force estimation." Transactions of the Institute of Measurement and Control 40, no. 13 (December 7, 2017): 3851–59. http://dx.doi.org/10.1177/0142331217740178.
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Cooperative teleoperation combines two traditional areas of robotics, that is, teleoperation and collaborative manipulation. Cooperative telerobotic systems consist of multiple pairs of master and slave robotic manipulators operating in a shared environment. The most common control frameworks for nonlinear systems, that is, Proportional Derivative (PD) controllers, possess considerable deficiency in contact motion. In this paper, a novel control scheme is proposed for a nonlinear bilateral cooperative teleoperation system with time delay. In addition to position and velocity signals, force signals are employed in the control strategy. This modification significantly enhances the poor transparency when the slave robots are in collision with the environment. To cope with external forces measurement, a modified force estimation algorithm is proposed to estimate human and environment forces. The closed loop stability of the nonlinear cooperative teleoperation system with the proposed control scheme is investigated using the Lyapunov theory. The main achievement of this research is the stability of the closed loop cooperative teleoperation system in the presence of estimated operator and environmental forces. In addition, it is theoretically and experimentally proved that force reflection occurs and transparency is improved at the same time. Experimental results demonstrate the efficiency of the presented control strategy in free motion as well as when the slave robots are in contact with the environment.
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Nagpal, Neelu, Bharat Bhushan, and Vijyant Agarwal. "Estimation of stochastic environment force for master–slave robotic system." Sādhanā 42, no. 6 (April 26, 2017): 889–99. http://dx.doi.org/10.1007/s12046-017-0643-7.
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Trębiński, Radosław, Ryszard Woźniak, Damian Szupieńko, and Bartosz Fikus. "Estimation of Priming Mixture Force." Energies 15, no. 16 (August 17, 2022): 5952. http://dx.doi.org/10.3390/en15165952.
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This paper presents the results of the estimation of the priming mixture force for primers used in 12.7 mm ammunition. The method of estimation is based on the results of pressure measurements in a closed chamber into which the products of the combustion of the priming mixture flowed. The capacity of the chamber was changed by inserting sleeves of various volumes. Another estimation of the force value was performed using the results of earlier investigations in which the chamber was filled with glass balls. The determined values of the force were compared with the values estimated based on the accessible literature data. The obtained estimations of the force value are of the same order of magnitude as the force value of black powder. This justifies the use of black powder characteristics for the assessment of the thermodynamic properties of priming mixture combustion products in interior ballistics calculations. The time of action of the investigated primer was determined using the optical recording of the outflow of the priming mixture combustion products into an open space. To facilitate the interpretation of the results of the experiments, a theoretical model was used.
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Kallu, Karam Dad, Amad Zafar, Muhammad Umair Ali, Shahzad Ahmed, and Min Cheol Lee. "Robust Controller for Pursuing Trajectory and Force Estimations of a Bilateral Tele-Operated Hydraulic Manipulator." Remote Sensing 13, no. 9 (April 23, 2021): 1648. http://dx.doi.org/10.3390/rs13091648.
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In hazardous/emergency situations, public safety is of the utmost concern. In areas where human access is not possible or is restricted due to hazardous situations, a system or robot that can be distantly controlled is mandatory. There are many applications in which force cannot be applied directly while using physical sensors. Therefore, in this research, a robust controller for pursuing trajectory and force estimations while deprived of any signals or sensors for bilateral tele-operation of a hydraulic manipulator is suggested to handle these hazardous, emergency circumstances. A terminal sliding control with a sliding perturbation observer (TSMCSPO) is considered as the robust controller for a coupled leader and hydraulic follower system. The ultimate use of this controller is as a sliding perturbation observer (SPO) that can estimate the reaction force without any physical force sensors. Robust and perfect position tracking is attained with terminal sliding mode control (TSMC) in addition to control of the hydraulic follower manipulator. The force estimation and pursuing trajectory for the leader–follower system is built upon a bilateral tele-operation control approach. The difference between the reaction forces (caused by the remote environment) and the operating forces (applied by the human operator) required the involvement of an impedance model. The impedance model is implemented in the leader manipulator to provide human operators with an actual sense of the reaction force while the manipulator connects with the remote environment. A camera is used to ensure the safety of the workplace through visual feedback. The experimental results showed that the controller was robust at pursuing trajectory and force estimations for the bilateral tele-operation control of a hydraulic manipulator.
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Ferretti, G., C. Maffezzoni, G. Magnani, and P. Rocco. "Joint Stiffness Estimation Based on Force Sensor Measurements in Industrial Manipulators." Journal of Dynamic Systems, Measurement, and Control 116, no. 1 (March 1, 1994): 163–67. http://dx.doi.org/10.1115/1.2900673.
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The problem of estimating the stiffness constants of the joints in industrial manipulators is addressed in this paper. It is shown that the model of the robot constrained by a rigid environment yields a simple relationship between variations of the motor coordinates and the forces arising at the contact with the environment. By exploiting the measurements of the motor positions sensors and of a force sensor located at the end effector a method is proposed to simply compute good estimates of the stiffness constants. Experiments have been made on the industrial robot SMART and the results are given and discussed.
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Liu, Huaimin, Xiangjiang Wang, and Meng Li. "External force estimation for robotic manipulator base on particle swarm optimization." International Journal of Advanced Robotic Systems 18, no. 6 (November 1, 2021): 172988142110637. http://dx.doi.org/10.1177/17298814211063744.
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The safe disposal of nuclear waste in radioactive environment urgently needs cost-effective approaches. Toward this goal, this article developed a method to external force estimation based on the identified model without force sensors. Firstly, the mathematical model including joint friction was obtained and transformed into the linear combination of unknown parameter to be estimated. Secondly, the unknown parameters were identified based on the improved particle swarm optimization algorithm, the identification procedure was implemented by optimizing the excitation trajectories to excite joint motion and sampling relevant data. Identified results were compared with the biogeography-based optimization algorithm and the cuckoo search algorithm. Then, the identified dynamic parameter was applied to external force estimation. Finally, the verification of external force estimation has been carried out using the Kinova Jaco2 robot manipulator, and the experimental results showed that the external forces by the proposed method could be estimated with an root mean square error of 0.7 N.
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KUSAKABE, Tsukasa, Masahide OIKAWA, Kyo KUTSUZAWA, Sho SAKAINO, and Toshiaki TSUJI. "Impedance estimation of high stiffness environment based on robotic force sensation." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2020 (2020): 2A1—M08. http://dx.doi.org/10.1299/jsmermd.2020.2a1-m08.
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Minh, Nhat Nguyen, and DaeYi Jung. "Integrated Estimation Strategy of Brake Force Cooperated with Artificial Neural Network Based Road Condition Classifier and Vehicle Mass Identification Using Static Suspension Deflections." Applied Sciences 12, no. 19 (September 27, 2022): 9727. http://dx.doi.org/10.3390/app12199727.
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Brake forces and maximum static road friction coefficients for each wheel of the vehicle are essential information for vehicle safety systems including adaptive cruise control, electronic stability control (ESC), and collision avoidance system, etc. Many studies have been performed to estimate brake force and road friction using well-known model-based approaches, but none have unambiguously guaranteed an accurate performance in all ranges of driving conditions and road ones. In addition, the investigation of the integrated estimation approach of road friction and brake force including mass estimation has not been clearly addressed so far. Therefore, in this study, a novel integrated estimation strategy based on a data-driven technique and artificial neural network (ANN) classifier along with a compact mass identification has been proposed to acquire the accurate road friction and brake force of individual wheel. Specifically, it includes an instant mass estimation by monitoring static suspension deflections, an artificial neural network (ANN) classifier for road friction coefficient based on the average data set from available standard sensors, and a brake force estimation using the data-driven technique. The performance of the proposed technique is validated by a co-simulation environment between Carsim and MATLAB/Simulink. It is found that the integrated estimation strategy guaranteed an accurate estimation of brake forces and road friction for a wide range of variations of road frictions, vehicle velocities, and masses. This work will be a valuable asset for those who wish to develop an integrated estimation system for such crucial parameters of the vehicle system.
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Kružić, Stanko, Josip Musić, Roman Kamnik, and Vladan Papić. "End-Effector Force and Joint Torque Estimation of a 7-DoF Robotic Manipulator Using Deep Learning." Electronics 10, no. 23 (November 28, 2021): 2963. http://dx.doi.org/10.3390/electronics10232963.
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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.
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Zakia, Umme, and Carlo Menon. "Estimating Exerted Hand Force via Force Myography to Interact with a Biaxial Stage in Real-Time by Learning Human Intentions: A Preliminary Investigation." Sensors 20, no. 7 (April 8, 2020): 2104. http://dx.doi.org/10.3390/s20072104.
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Force myography (FMG) signals can read volumetric changes of muscle movements, while a human participant interacts with the environment. For collaborative activities, FMG signals could potentially provide a viable solution to controlling manipulators. In this paper, a novel method to interact with a two-degree-of-freedom (DoF) system consisting of two perpendicular linear stages using FMG is investigated. The method consists in estimating exerted hand forces in dynamic arm motions of a participant using FMG signals to provide velocity commands to the biaxial stage during interactions. Five different arm motion patterns with increasing complexities, i.e., “x-direction”, “y-direction”, “diagonal”, “square”, and “diamond”, were considered as human intentions to manipulate the stage within its planar workspace. FMG-based force estimation was implemented and evaluated with a support vector regressor (SVR) and a kernel ridge regressor (KRR). Real-time assessments, where 10 healthy participants were asked to interact with the biaxial stage by exerted hand forces in the five intended arm motions mentioned above, were conducted. Both the SVR and the KRR obtained higher estimation accuracies of 90–94% during interactions with simple arm motions (x-direction and y-direction), while for complex arm motions (diagonal, square, and diamond) the notable accuracies of 82–89% supported the viability of the FMG-based interactive control.
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Walters, Margaret, Allison Eubanks, Elizabeth Weissbrod, John Fischer, Barton Staat, and Shad Deering. "Visual Estimation of Force Applied During Simulated Deliveries Complicated by Shoulder Dystocia." American Journal of Perinatology Reports 08, no. 04 (October 2018): e206-e211. http://dx.doi.org/10.1055/s-0038-1673377.
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Background Shoulder dystocia occurs when the fetal head delivers, but the shoulder is lodged behind the pubic symphysis. Training for these emergency deliveries is not optimized, and litigation can occur around a shoulder dystocia delivery. Objective Evaluate the ability of an outside observer to visually estimate the amount of traction applied to the fetal head during simulated deliveries complicated by shoulder dystocia. Study Design Simulated deliveries with an objective measurement of traction were randomly organized for estimation of traction applied. Videos show providers applying a “normal” (75 N) and “excessive” (150 N) amount of force in both a “calm” and “stressed” delivery. Results Fifty participants rated the amount of force applied. Observers estimated traction, on a scale from 1 to 5, higher in the 150-N deliveries as compared with 75-N deliveries (“calm” environment: 3.1 vs. 2.8, p < 0.001; and “stressed” environment: 3.2 vs. 2.8, p < 0.001). Only 15% of observers rated force “above average” or “excessive” in a “calm” environment, as opposed to 30% of observers in the “stressed” environment. Conclusion Observers are not able to determine when “excessive force” is used and are twice as likely to overestimate the force applied to a fetal head when an average amount of force is used and the delivery environment is stressful. Precis Observers are unable to determine when excessive traction is applied to the fetal head during simulated deliveries complicated by shoulder dystocia.
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Ahmed, Mushtaq, Zafarullah Nizamani, Akihiko Nakayama, and Montasir Osman. "Generation of Offshore Environments in the Numerical Wave Tank to Model Metocean Conditions Interaction with Offshore Structure Near the Free Surface." IOP Conference Series: Earth and Environmental Science 945, no. 1 (December 1, 2021): 012018. http://dx.doi.org/10.1088/1755-1315/945/1/012018.
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Abstract Offshore structures play a vital role in the economy of offshore oil-producing countries, where mostly fixed jacket type structures are used to produce oil and gas installed in shallow water. In an offshore environment where structures are installed, there exist met ocean forces such as wind, waves, and currents. These met ocean conditions when interacting with offshore structures near the free surface, generate loads. The estimation of such loads is very much important for the proper design of these structures. The primary aim of this study is to investigate the interaction of waves with a jacket platform by generating offshore environments in the numerical wave tank (NWT). To achieve this goal, ANSYS Fluent is used for the flow analysis by using continuity and Navier Stokes equation. Results are verified and validated with the analytical work. Wave crests under operating condition generate a force of 1.3 MN which is the lowest in magnitude as compared to wave crest which produces 4.5 MN force under extreme conditions. Unlike operating wave crest, the operating wave trough generates a higher force of 1 MN than extreme conditions which account for 1.5 MN forces. Forces produced by the extreme offshore environment are 30% higher than those generated under operating conditions. It is concluded from the results that a positive force is exerted onto the structure during the water entry phase while a negative force is observed when the water leaves the structure.
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Zhang, Ting, Li Jiang, Shaowei Fan, Xinyu Wu, and Wei Feng. "Development and experimental evaluation of multi-fingered robot hand with adaptive impedance control for unknown environment grasping." Robotica 34, no. 5 (August 27, 2014): 1168–85. http://dx.doi.org/10.1017/s0263574714002161.
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SUMMARYThis paper presents adaptive impedance controllers with adaptive sliding mode friction compensation for anthropomorphic artificial hand. A five-fingered anthropomorphic artificial hand with multi-sensory and Field-Programmable Gate Arra (FPGA)-based control hardware and software architecture is designed to fulfill the requirements of the grasping force controller. In order to improve the force-tracking precision, the indirect adaptive algorithm was applied to estimate the parameters of the environment. The generalized momentum-based disturbance observer was applied to estimate the contact force from the torque sensor. Based on the sensors of the finger, an adaptive sliding mode friction compensation algorithm was utilized to improve the accuracy of the position control. The performances of the force-tracking impedance controller and position-based joint impedance control for the five-fingered anthropomorphic artificial hand are analyzed and compared in this paper. Furthermore, the performances of the force-tracking impedance controller with environmental parameters adaptive estimation and without environmental parameters estimation are analyzed and compared. Experimental results prove that accurate force-tracking and stable torque/force response under uncertain environments of unknown stiffness and position can be achieved with the proposed adaptive force-tracking impedance controller with friction compensation on five-finger artificial hand.
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Ghafarirad, H., S. M. Rezaei, M. Zareinejad, and A. Abdullah. "Adaptive Robust Control for Micropositioning of Piezoelectric Actuators with Environment Force Estimation." IFAC Proceedings Volumes 44, no. 1 (January 2011): 12715–20. http://dx.doi.org/10.3182/20110828-6-it-1002.03761.
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Azimifar, Farhad, Kamran Hassani, Amir Hossein Saveh, and Farhad Tabatabai Ghomshe. "Performance analysis in delayed nonlinear bilateral teleoperation systems by force estimation algorithm." Transactions of the Institute of Measurement and Control 40, no. 5 (February 1, 2017): 1637–44. http://dx.doi.org/10.1177/0142331216688751.
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This paper establishes a novel control strategy for a nonlinear bilateral teleoperation system with time delay. Besides position and velocity signals, force signals are additionally utilized in the control scheme. This modification significantly improves the poor transparency during contact with the environment. To eliminate the external force measurement, a force estimation algorithm is proposed for the master and slave robots. The closed loop stability of the nonlinear teleoperation system with the proposed control scheme is investigated through the Lyapunov theory. Furthermore, it is theoretically and experimentally proved that force reflection occurs and transparency is enhanced simultaneously. Consequently, experimental results verify the efficiency of the new control scheme in free motion and during collision of the slave robot with the environment.
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Batty, Taran, Armin Ehrampoosh, Bijan Shirinzadeh, Yongmin Zhong, and Julian Smith. "A Transparent Teleoperated Robotic Surgical System with Predictive Haptic Feedback and Force Modelling." Sensors 22, no. 24 (December 13, 2022): 9770. http://dx.doi.org/10.3390/s22249770.
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In recent years, robotic minimally invasive surgery has transformed many types of surgical procedures and improved their outcomes. Implementing effective haptic feedback into a teleoperated robotic surgical system presents a significant challenge due to the trade-off between transparency and stability caused by system communication time delays. In this paper, these time delays are mitigated by implementing an environment estimation and force prediction methodology into an experimental robotic minimally invasive surgical system. At the slave, an exponentially weighted recursive least squares (EWRLS) algorithm estimates the respective parameters of the Kelvin–Voigt (KV) and Hunt–Crossley (HC) force models. The master then provides force feedback by interacting with a virtual environment via the estimated parameters. Palpation experiments were conducted with the slave in contact with polyurethane foam during human-in-the-loop teleoperation. The experimental results indicated that the prediction RMSE of error between predicted master force feedback and measured slave force was reduced to 0.076 N for the Hunt–Crossley virtual environment, compared to 0.356 N for the Kelvin–Voigt virtual environment and 0.560 N for the direct force feedback methodology. The results also demonstrated that the HC force model is well suited to provide accurate haptic feedback, particularly when there is a delay between the master and slave kinematics. Furthermore, a haptic feedback approach that incorporates environment estimation and force prediction improve transparency during teleoperation. In conclusion, the proposed bilateral master–slave robotic system has the potential to provide transparent and stable haptic feedback to the surgeon in surgical robotics procedures.
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Filipozzi, Louis, Francis Assadian, Ming Kuang, Rajit Johri, and Jose Velazquez Alcantar. "Estimation of Tire Normal Forces including Suspension Dynamics." Energies 14, no. 9 (April 22, 2021): 2378. http://dx.doi.org/10.3390/en14092378.
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Tire normal forces are difficult to measure, but information on the vehicle normal force can be used in many automotive engineering applications, e.g., rollover detection and vehicle and wheel stability. Previous papers use algebraic equations to estimate the tire normal force. In this article, the estimation of tire normal force is formulated as an input estimation problem. Two observers are proposed to solve this problem by using a quarter-car suspension model. First, the Youla Controller Output Observer framework is presented. It converts the estimation problem into a control problem and produces a Youla parameterized controller as observer. Second, a Kalman filter approach is taken and the input estimation problem is addressed with an Unbiased Minimum Variance Filter. Both methods use accelerometer and suspension deflection sensors to determine the vehicle normal force. The design of the observers is validated in simulation and a sensitivity analysis is performed to evaluate their robustness.
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Roveda, Loris, Nicola Pedrocchi, Federico Vicentini, and Lorenzo Molinari Tosatti. "Industrial compliant robot bases in interaction tasks: a force tracking algorithm with coupled dynamics compensation." Robotica 35, no. 8 (July 7, 2016): 1732–46. http://dx.doi.org/10.1017/s0263574716000461.
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SUMMARYLight-weight manipulators are used in industrial tasks mounted on mobile platforms to improve flexibility. However, such mountings introduce compliance affecting the tasks. This work deals with such scenarios by designing a controller that also takes into account compliant environments. The controller allows the tracking of a target force using the estimation of the environment stiffness (EKF) and the estimation of the base position (KF), compensating the robot base deformation. The closed-loop stability has been analyzed. Observers and the control law have been validated in experiments. An assembly task is considered with a standard industrial non-actuated mobile platform. Control laws with and without base compensation are compared.
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Tsaprounis, C. J., and N. A. Aspragathos. "Sliding mode with adaptive estimation force control of robot manipulators interacting with an unknown passive environment." Robotica 17, no. 4 (July 1999): 447–58. http://dx.doi.org/10.1017/s0263574799001502.
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In this paper, a force control algorithm for robot manipulators is introduced, where the dynamics of non-rigid environment interacting with the robot is assumed unknown. The controller design is based on the combination of sliding mode control techniques and the adaptive estimation theory, so the introduced controller compensates the structured or unstructured uncertainty of the environment. The main source of feedback information is received from a wrist force sensor. The designed controller includes additional absorption terms in order to minimise end-point velocity error and to suppress the impact effects at the beginning of the force application.
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Chen, Nutan, Göran Westling, Benoni B. Edin, and Patrick van der Smagt. "Estimating Fingertip Forces, Torques, and Local Curvatures from Fingernail Images." Robotica 38, no. 7 (September 26, 2019): 1242–62. http://dx.doi.org/10.1017/s0263574719001383.
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SUMMARYThe study of dexterous manipulation has provided important insights into human sensorimotor control as well as inspiration for manipulation strategies in robotic hands. Previous work focused on experimental environment with restrictions. Here, we describe a method using the deformation and color distribution of the fingernail and its surrounding skin to estimate the fingertip forces, torques, and contact surface curvatures for various objects, including the shape and material of the contact surfaces and the weight of the objects. The proposed method circumvents limitations associated with sensorized objects, gloves, or fixed contact surface type. In addition, compared with previous single finger estimation in an experimental environment, we extend the approach to multiple finger force estimation, which can be used for applications such as human grasping analysis. Four algorithms are used, c.q., Gaussian process, convolutional neural networks, neural networks with fast dropout, and recurrent neural networks with fast dropout, to model a mapping from images to the corresponding labels. The results further show that the proposed method has high accuracy to predict force, torque, and contact surface.
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Song, Ke, and Heyu Hu. "Dynamic parameter identification and adaptive control with trajectory scaling for robot-environment interaction." PLOS ONE 18, no. 7 (July 13, 2023): e0287484. http://dx.doi.org/10.1371/journal.pone.0287484.
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To improve the force/position control performance of robots in contact with the environment, this paper proposes a control scheme comprising dynamic parameter identification, trajectory scaling, and computed-torque control based on adaptive parameter estimation. Based on the Newton–Euler method, the dynamic equation and its regression matrix is obtained, which is helpful to reduce the order of the model. Subsequently, the least-square method is implemented to calculate the values of the basic parameters of the dynamics. The identified dynamic parameters are used as initial values in the adaptive parameter estimation to obtain the torque, and trajectory scaling is applied to control the contact force between the robot and the environment. Finally, the dynamic parameter identification method and control algorithm are verified by conducting a simulation. The results show that the comprehensive application can help improve the control performance of robots.
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Wang, Jinfeng, Muye Pang, Peixuan Yu, Biwei Tang, Kui Xiang, and Zhaojie Ju. "Effect of Muscle Fatigue on Surface Electromyography-Based Hand Grasp Force Estimation." Applied Bionics and Biomechanics 2021 (February 15, 2021): 1–12. http://dx.doi.org/10.1155/2021/8817480.
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Surface electromyography- (sEMG-) based hand grasp force estimation plays an important role with a promising accuracy in a laboratory environment, yet hardly clinically applicable because of physiological changes and other factors. One of the critical factors is the muscle fatigue concomitant with daily activities which degrades the accuracy and reliability of force estimation from sEMG signals. Conventional qualitative measurements of muscle fatigue contribute to an improved force estimation model with limited progress. This paper proposes an easy-to-implement method to evaluate the muscle fatigue quantitatively and demonstrates that the proposed metrics can have a substantial impact on improving the performance of hand grasp force estimation. Specifically, the reduction in the maximal capacity to generate force is used as the metric of muscle fatigue in combination with a back-propagation neural network (BPNN) is adopted to build a sEMG-hand grasp force estimation model. Experiments are conducted in the three cases: (1) pooling training data from all muscle fatigue states with time-domain feature only, (2) employing frequency domain feature for expression of muscle fatigue information based on case 1, and 3) incorporating the quantitative metric of muscle fatigue value as an additional input for estimation model based on case 1. The results show that the degree of muscle fatigue and task intensity can be easily distinguished, and the additional input of muscle fatigue in BPNN greatly improves the performance of hand grasp force estimation, which is reflected by the 6.3797% increase in R2 (coefficient of determination) value.
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Wang, Feilu, Yanan Jiang, Yang Song, Shanna Lv, Mingkun Li, and Rungen Ye. "Visual Interaction Force Estimation Based on Time-Sensitive Dual-Resolution Learning Network." Journal of Sensors 2022 (February 8, 2022): 1–14. http://dx.doi.org/10.1155/2022/4302179.
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Haptic force feedback is an important perception method for humans to understand the surrounding environment. It can estimate tactile force in real time and provide appropriate feedback. It has important research value for robot-assisted minimally invasive surgery, interactive tactile robots, and other application fields. However, most of the existing noncontact visual power estimation methods are implemented using traditional machine learning or 2D/3D CNN combined with LSTM. Such methods are difficult to fully extract the contextual spatiotemporal interaction semantic information of consecutive multiple frames of images, and their performance is limited. To this end, this paper proposes a time-sensitive dual-resolution learning network-based force estimation model to achieve accurate noncontact visual force prediction. First, we perform continuous frame normalization processing on the robot running the video captured by the camera and use the hybrid data augmentation to improve the data diversity; secondly, a deep semantic interaction model is constructed based on the time-sensitive dual-resolution learning network, which is used to automatically extract the deep spatiotemporal semantic interaction information of continuous multiframe images; finally, we construct a simplified prediction model to realize the efficient estimation of interaction force. The results based on the large-scale robot hand interaction dataset show that our method can estimate the interaction force of the robot hand more accurately and faster. The average prediction MSE reaches 0.0009 N, R 2 reaches 0.9833, and the average inference time for a single image is 6.5532 ms; in addition, our method has good prediction generalization performance under different environments and parameter settings.
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Liu, Guanghui, Lijin Fang, Bing Han, and Hualiang Zhang. "Frequency-division based hybrid force/position control of robotic arms manipulating in uncertain environments." Industrial Robot: the international journal of robotics research and application 47, no. 3 (March 15, 2020): 445–52. http://dx.doi.org/10.1108/ir-11-2019-0228.
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Purpose This paper aims to propose a hybrid force/position control algorithm based on the stiffness estimation of the unknown environment. A frequency-division control scheme is developed to improve the applicability and reliability of the robot in welding, polishing and assembly. Design/methodology/approach The stiffness estimation algorithm with time-varying forgetting factors is used to improve the speed and accuracy of the unknown environmental estimation. The sensor force control and robot position control are adopted in different frequencies to improve system stability and communication compatibility. In the low frequency of sensor force control, the Kalman state observer is used to estimate the robot’s joints information, whereas the polynomial interpolation is used to ensure the smoothness of the high frequency of robot position control. Findings Accurate force control, as well as the system stability, is attained by using this control algorithm. Practical implications The entire algorithm is applied to a six-degrees-of-freedom industrial robot, and experiments are performed to confirm its applicability. Originality/value The frequency-division control strategy guarantees the control stability and improves the smoothness of the robot movement.
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Uchimura, Yutaka, and H. Kazerooni. "A μ-Synthesis Based Control for Compliant Maneuvers." Journal of Dynamic Systems, Measurement, and Control 128, no. 4 (April 12, 2006): 914–21. http://dx.doi.org/10.1115/1.2362810.
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This paper deals with a system, which is subjected to very uncertain factors: human and environment. These independent uncertainties are dealt with explicitly on the framework of μ-synthesis. We also describe a controller design, which enables a robust force feedback without using a force sensor. The model of human dynamics, environments, and actuators are modeled associated with uncertainties described in the form of weighting functions. A controller is designed based on the μ-synthesis so that it maintains robust performance against uncertainties in both environment and human dynamics, which contributes to dexterous manipulation. The controller described here is implemented on the human power extender, which is worn by a human and amplifies the human’s physical strength, while the human’s intelligence remains as the central control system for manipulation. Experimental results conducted on the extender showed that the force estimation worked fine and the control system performed as desired.
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Hu, Heyu, Xiaoqi Wang, and Lerui Chen. "Impedance with Finite-Time Control Scheme for Robot-Environment Interaction." Mathematical Problems in Engineering 2020 (May 25, 2020): 1–18. http://dx.doi.org/10.1155/2020/2796590.
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For the robot system with the uncertain model and unknown environment parameters, a control scheme combining impedance and finite time is proposed. In order to obtain accurate force control performance indirectly by using position tracking, the control scheme is divided into two parts: an outer loop for force impedance control and an inner loop for position tracking control. In the outer loop, in order to eliminate the force tracking error quickly, the impedance control based on force is adopted; when the robot contacts with the environment, the satisfactory force tracking performance can be obtained. In the inner loop, the finite-time control method based on the homogeneous system is used. Through this method, the desired virtual trajectory generated by the outer loop can be tracked, and the contact force tracking performance can be obtained indirectly in the direction of force. This method does not need the dynamics model knowledge of the robot system, thus avoiding the online real-time calculation of the inverse dynamics of the robot. The unknown uncertainty and external interference of the system are obtained online by using the time-delay estimation, and the control process is effectively compensated, so the algorithm is simple, the convergence speed is fast, and the practical application is easy. The theory of finite-time stability is used to prove that the closed-loop system is finite-time stable, and the effectiveness of the algorithm is proved by simulations.
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Ostrom, Charles W., and Brian L. Job. "The President and the Political Use of Force." American Political Science Review 80, no. 2 (June 1986): 541–66. http://dx.doi.org/10.2307/1958273.
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Throughout the post–World War II period the president has been called upon to make decisions concerning the use of force as a political instrument. The explanation that is offered is based upon a characterization of the president as a cybernetic human decision maker facing limitations. These limitations, in conjunction with the complexity of the environment, lead presidents to develop and use a relatively simple decision rule. The dependent variable, which is the probability of the use of force at any point in time, is explained in terms of enduring and essential concerns, which are operationalized as coming from the international, domestic, and personal environments. Data are taken from Blechman and Kaplan's Force Without War. On the basis of our estimation and evaluation, presidential decisions to use force are based on factors in all three arenas.
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Oh, Seungin, Hanmin Lee, Jai-Kyung Lee, Hyungchul Yoon, and Jin-Gyun Kim. "Real-Time Response Estimation of Structural Vibration with Inverse Force Identification." Structural Control and Health Monitoring 2023 (April 6, 2023): 1–23. http://dx.doi.org/10.1155/2023/2691476.
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Virtual sensing is an effective method to identify the inaccessible state of the structural systems by compensating the limitations of the conventional physical sensing techniques. Recently, it becomes popular in structural vibration field and their relevant physical domains such as civil, mechanical, aerospace engineering, thermal dynamics, and acoustics. This study aimed to develop a virtual sensing algorithm of structural vibration for the real-time identification of unmeasured information. First, certain local point vibration responses (such as displacement and acceleration) are measured using physical sensors, and the data sets are extended using a numerical model to cover the unmeasured quantities through the entire spatial domain in the real-time computation process. A modified time integrator is then proposed to synchronize the physical sensors and the numerical model using inverse dynamics. In particular, an efficient inverse force identification method is derived using implicit time integration. The second-order ordinary differential formulation and its projection-based reduced-order modeling are used to avoid two times larger degrees of freedom within the state-space form. Then, the Tikhonov regularization noise-filtering algorithm is employed instead of Kalman filtering. The performance of the proposed method is investigated on both numerical and experimental testbeds under sinusoidal and random excitation loading conditions. In the numerical test, the system could identify the status of the motor housing structure in the speed of 16,181 S a m p l e s / s . The FDE and RMSE values are bounded under 0.1816 and 1503.2. In the case of the experimental test, the algorithm is implemented to the beam structure using a single-board computer, including inverse force identification and unmeasured response prediction. Even in the limited computational environment, the system could identify the applied forces in real time in the speed of 2,173 S a m p l e s / s . Through all experimental cases, FDE and RMSE values are bounded under 0.2925 and 0.1660. The results show that the virtual sensing algorithm can accurately identify unmeasured information, forces, and displacements throughout the vibration model in real time in a very limited computing environment.
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Ghafarirad, H., SM Rezaei, M. Zareinejad, and M. Hamdi. "A robust adaptive control for micro-positioning of piezoelectric actuators with environment force estimation." Transactions of the Institute of Measurement and Control 34, no. 8 (December 14, 2011): 956–65. http://dx.doi.org/10.1177/0142331211426821.
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Namdar Ghalati, Mohammad Hadi, and Hamed Ghafarirad. "Continuous deformation analysis and contact force estimation for pneumatic bending actuators interacting with environment." Comptes Rendus. Mécanique 351, G1 (January 24, 2023): 43–58. http://dx.doi.org/10.5802/crmeca.167.
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Weidmann, Alexander, Bertram Taetz, Matthias Andres, Felix Laufer, and Gabriele Bleser. "Force Shadows: An Online Method to Estimate and Distribute Vertical Ground Reaction Forces from Kinematic Data." Sensors 20, no. 19 (October 8, 2020): 5709. http://dx.doi.org/10.3390/s20195709.
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Kinetic models of human motion rely on boundary conditions which are defined by the interaction of the body with its environment. In the simplest case, this interaction is limited to the foot contact with the ground and is given by the so called ground reaction force (GRF). A major challenge in the reconstruction of GRF from kinematic data is the double support phase, referring to the state with multiple ground contacts. In this case, the GRF prediction is not well defined. In this work we present an approach to reconstruct and distribute vertical GRF (vGRF) to each foot separately, using only kinematic data. We propose the biomechanically inspired force shadow method (FSM) to obtain a unique solution for any contact phase, including double support, of an arbitrary motion. We create a kinematic based function, model an anatomical foot shape and mimic the effect of hip muscle activations. We compare our estimations with the measurements of a Zebris pressure plate and obtain correlations of 0.39≤r≤0.94 for double support motions and 0.83≤r≤0.87 for a walking motion. The presented data is based on inertial human motion capture, showing the applicability for scenarios outside the laboratory. The proposed approach has low computational complexity and allows for online vGRF estimation.
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Roveda, Loris, Andrea Bussolan, Francesco Braghin, and Dario Piga. "6D Virtual Sensor for Wrench Estimation in Robotized Interaction Tasks Exploiting Extended Kalman Filter." Machines 8, no. 4 (October 27, 2020): 67. http://dx.doi.org/10.3390/machines8040067.
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Industrial robots are commonly used to perform interaction tasks (such as assemblies or polishing), requiring the robot to be in contact with the surrounding environment. Such environments are (partially) unknown to the robot controller. Therefore, there is the need to implement interaction controllers capable of suitably reacting to the established contacts. Although standard force controllers require force/torque measurements to close the loop, most of the industrial manipulators do not have installed force/torque sensor(s). In addition, the integration of external sensors results in additional costs and implementation effort, not affordable in many contexts/applications. To extend the use of compliant controllers to sensorless interaction control, a model-based methodology is presented in this paper for the online estimation of the interaction wrench, implementing a 6D virtual sensor. Relying on sensorless Cartesian impedance control, an Extended Kalman Filter (EKF) is proposed for the interaction wrench estimation. The described approach has been validated in simulations, taking into account four different scenarios. In addition, experimental validation has been performed employing a Franka EMIKA panda robot. A human–robot interaction scenario and an assembly task have been considered to show the capabilities of the developed EKF, which is able to perform the estimation with high bandwidth, achieving convergence with limited errors.
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Huh, K., J. Kim, and K. Yi. "Monitoring system design for estimating the lateral tyre force." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 217, no. 4 (April 1, 2003): 247–56. http://dx.doi.org/10.1243/09544070360613219.
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Longitudinal and lateral forces acting on tyres are known to be closely related to tractive ability, braking characteristics, handling stability and manoeuvrability of ground vehicles. However, it is not feasible in the operating vehicles to measure the tyre forces directly because of the high cost of sensors, the limitations in sensor technology, interference with tyre rotation and the harsh environment. Another approach is the indirect monitoring technique that can estimate the tyre forces based on remote sensor outputs and vehicle dynamics models. In this paper, in order to develop tyre force-monitoring systems, a monitoring model is proposed utilizing not only the yawing motion but also the roll motion. Based on the monitoring model, a monitoring system is designed to estimate the lateral tyre force acting on each tyre. The monitoring system is constructed on the basis of a new scaled Kalman filter with model error compensator (SKFMEC) technique that is developed in this study to improve the robustness performance of Kalman filter methods. The SKFMEC technique adopts both the well-conditioned observer and the model error compensator concepts. Tyre force estimation performance of the monitoring system is evaluated in the MATLAB simulations where true tyre force data are generated from a 14-degree-of-freedom vehicle model with the combined-slip ‘magic formula’ tyre model.
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Wu, TX, and HL Xing. "Assessment of the performance and effects of metro-induced ground-borne vibration for mitigation measures of resilient tracks." Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit 232, no. 5 (September 15, 2017): 1448–63. http://dx.doi.org/10.1177/0954409717731248.
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A methodology that judges the performance and estimates the effects of metro-induced ground-borne vibration is presented for the mitigation measures of resilient tracks. Two criteria are proposed for the purpose of judgment and estimation, respectively. One is the force ratio obtained by comparing the forces transmitted to the track bed of the resilient track with those of a reference track. As the force ratio of the track bed does not vary with the excitation and environment conditions, it can be used for judging the performance of the ground-borne vibration, i.e. the inherent ability to mitigate vibration, of the resilient track. Another criterion is the overall frequency-weighted root-mean-square acceleration at the receivers. This criterion is used for the estimation of the real effects of the ground-borne vibration of a resilient track in practical conditions. Calculation results demonstrate that the practical effects of ground-borne vibration of the mitigation measures studied vary with the excitation and environment factors and tend to be more effective when the unevenness excitation is rougher or the monitoring points are closer to the source of vibration. The proposed criteria, the corresponding models developed, and the methodology presented exhibit both high efficiency in computation and great convenience for assessing the mitigation measures of resilient tracks.
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Li, Zhan Ming, and Er Chao Li. "Adaptive Impedance Control for Robot Based on Estimation of Environmental Parameters." Advanced Materials Research 328-330 (September 2011): 1713–16. http://dx.doi.org/10.4028/www.scientific.net/amr.328-330.1713.
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In order to realize precise contact tasks with an unknown environment, robotic force controllers have to adapt themselves to the unknown environment. Some impedance controllers are designed for several representative environmental parameters, A BP neural network is proposed to determine the one-to-one mapping relations between the environmental parameters and the impedance parameters. However, it is difficult to accurately know the environmental parameters in the case of a changing environment, RLS is proposed to estimate environmental parameters, then determine the impedance coefficients to control the robot. Simulations prove that the controller designed is feasible and effective.
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Ghiani, Luca, Alberto Sassu, Francesca Palumbo, Luca Mercenaro, and Filippo Gambella. "In-Field Automatic Detection of Grape Bunches under a Totally Uncontrolled Environment." Sensors 21, no. 11 (June 5, 2021): 3908. http://dx.doi.org/10.3390/s21113908.
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An early estimation of the exact number of fruits, flowers, and trees helps farmers to make better decisions on cultivation practices, plant disease prevention, and the size of harvest labor force. The current practice of yield estimation based on manual counting of fruits or flowers by workers is a time consuming and expensive process and it is not feasible for large fields. Automatic yield estimation based on robotic agriculture provides a viable solution in this regard. In a typical image classification process, the task is not only to specify the presence or absence of a given object on a specific location, while counting how many objects are present in the scene. The success of these tasks largely depends on the availability of a large amount of training samples. This paper presents a detector of bunches of one fruit, grape, based on a deep convolutional neural network trained to detect vine bunches directly on the field. Experimental results show a 91% mean Average Precision.
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Fu, Zhi-Jun, Wei-Dong Xie, and Xiao-Bin Ning. "Adaptive Nonlinear Tire-Road Friction Force Estimation for Vehicular Systems Based on a Novel Differentiable Friction Model." Mathematical Problems in Engineering 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/201062.
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A novel adaptive nonlinear observer-based parameter estimation scheme using a newly continuously differentiable friction model has been developed to estimate the tire-road friction force. The differentiable friction model is more flexible and suitable for online adaptive identification and control with the advantage of more explicit parameterizable form. Different from conventional estimation methods, the filtered regression estimation parameter is introduced in the novel adaptive laws, which can guarantee that both the observer error and parameter error exponentially converge to zero. Lyapunov theory has been used to prove the stability of the proposed methods. The effectiveness of the estimation algorithm is illustrated via a bus simulation model in the Trucksim software and simulation environment. The relatively accurate tire-road friction force was estimated just by the easily existing sensors signals wheel rotational speed and vehicle speed and the proposed method also displays strong robustness against bounded disturbances.
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Wang, Zhuo, Shenghong Liu, Bo Huang, Haowu Luo, and Feiyan Min. "Forced Servoing of a Series Elastic Actuator Based on Link-Side Acceleration Measurement." Actuators 12, no. 3 (March 15, 2023): 126. http://dx.doi.org/10.3390/act12030126.
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Joint stiffness of an elastic-joint robot can be changed according to joint stiffness requirements. A series elastic actuator (SEA) can reduce the contact stiffness between the body and the environment or human, which can further ensure interactive operation in a human–machine-compatible environment. However, the introduction of the SEA improves the complexity of the robot dynamics model. In this paper, we propose a control schema based on link-side acceleration measurement to eliminate the overshoot and vibration in the transient process of force control. An extended Kalman filter (EKF) algorithm that fuses photoelectric encoders and accelerometers is first presented based on the link-side acceleration measurement. Following this, based on the external torque estimation, the vibration reduction control algorithm is designed. The simulation model is built, and the algorithm design and simulation of position control and force control are carried out and finally tested on the real robot platform. The effectiveness of the control algorithm is proved. The experimental results show that the dynamic response of the external force estimation is about 2 ms faster than that of the force sensor, and the error between the estimated external torque and the real external torque is within ±0.16 N·m.
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Fazeli, Nima, Roman Kolbert, Russ Tedrake, and Alberto Rodriguez. "Parameter and contact force estimation of planar rigid-bodies undergoing frictional contact." International Journal of Robotics Research 36, no. 13-14 (April 1, 2017): 1437–54. http://dx.doi.org/10.1177/0278364917698749.
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This paper addresses the identification of the inertial parameters and the contact forces associated with objects making and breaking frictional contact with the environment. Our goal is to explore under what conditions, and to what degree, the observation of physical interaction, in the form of motions and/or applied external forces, is indicative of the underlying dynamics that governs it. In this study we consider the cases of passive interaction, where an object free-falls under gravity, and active interaction, where known external perturbations act on an object at contact. We assume that both object and environment are planar and rigid, and exploit the well-known complementarity formulation for contact resolution to establish a constrained optimization-based problem to estimate inertial parameters and contact forces. We also show that when contact modes are known, or guessed, the formulation provides a closed-form relationship between inertial parameters, contact forces, and observed motions, that turns into a least squares problem. Consistent with intuition, the analysis indicates that without the application of known external forces, the identifiable set of parameters remains coupled, i.e. the ratio of mass moment of inertia to mass and the ratio of contact forces to the mass. Interestingly the analysis also shows that known external forces can lead to decoupling and identifiability of mass, mass moment of inertia, and normal and tangential contact forces. We evaluate the proposed algorithms both in simulation and with real experiments for the cases of a free falling square, ellipse, and rimless wheel interacting with the ground, as well as a disk interacting with a manipulator.
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Park, Jaeheung, and Oussama Khatib. "Robot multiple contact control." Robotica 26, no. 5 (September 2008): 667–77. http://dx.doi.org/10.1017/s0263574708004281.
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SUMMARYThis paper addresses the problem of contact force control for multiple contacts distributed over multiple links in a robot. This is of importance when performing complex tasks in unstructured environment, particularly in humanoid robot applications. The proposed multicontact control framework provides a new way of defining the operational space coordinates, which facilitates the specification of multiple contact control. The contact force space on multiple links is constructed as an operational space for the highest priority task. Motion control, given lower priority, can be executed using the rest of degree of freedom within the null-space of the force control. The dynamic control structure, then, provides a means to control each contact force and motion independently. This dynamic decoupling enables each contact force controller to utilize linear control theories. In particular, the contact force controllers adopt full state feedback control and estimation methods to produce robust performance with respect to modeling and parameter uncertainties. The effectiveness of the multiple contact control framework was demonstrated using a PUMA560 manipulator, with multiple contacts on the end-effector and third link. The demonstrated tasks involved controlling each of the contact forces with null-space motion.
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Tomić, Teodor, Philipp Lutz, Korbinian Schmid, Andrew Mathers, and Sami Haddadin. "Simultaneous contact and aerodynamic force estimation (s-CAFE) for aerial robots." International Journal of Robotics Research 39, no. 6 (March 23, 2020): 688–728. http://dx.doi.org/10.1177/0278364920904788.
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In this article, we consider the problem of multirotor flying robots physically interacting with the environment under influence of wind. The results are the first algorithms for simultaneous online estimation of contact and aerodynamic wrenches acting on the robot based on real-world data, without the need for dedicated sensors. For this purpose, we investigated two model-based techniques for discriminating between aerodynamic and interaction forces. The first technique is based on aerodynamic and contact torque models, and uses the external force to estimate wind speed. Contacts are then detected based on the residual between estimated external torque and expected (modeled) aerodynamic torque. Upon detecting contact, wind speed is assumed to change very slowly. From the estimated interaction wrench, we are also able to determine the contact location. This is embedded into a particle filter framework to further improve contact location estimation. The second algorithm uses the propeller aerodynamic power and angular speed as measured by the speed controllers to obtain an estimate of the airspeed. An aerodynamics model is then used to determine the aerodynamic wrench. Both methods rely on accurate aerodynamics models. Therefore, we evaluate data-driven and physics-based models as well as offline system identification for flying robots. For obtaining ground-truth data, we performed autonomous flights in a 3D wind tunnel. Using this data, aerodynamic model selection, parameter identification, and discrimination between aerodynamic and contact forces could be performed. Finally, the developed methods could serve as useful estimators for interaction control schemes with simultaneous compensation of wind disturbances.
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Ye, Lan, Genliang Xiong, Cheng Zeng, and Hua Zhang. "Trajectory tracking control of 7-DOF redundant robot based on estimation of intention in physical human-robot interaction." Science Progress 103, no. 3 (July 2020): 003685042095364. http://dx.doi.org/10.1177/0036850420953642.
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Collaborative robot has been widespread application prospect, such as homes, manufacturing, and health-care etc. In physical human-robot interaction, the external force appears inevitably in contact with environment or human, especially the interactive tasks such as trajectory tracking requirements and force compliance control. In this article, a method based on interaction intention estimation, which solve the problem of trajectory tracking accuracy and force compliance control in the same direction for the 7-DOF robot, is proposed. The increased virtual force depended on the manipuility performance index and inverse kinematic solution used the kinematic decoupling method based on the redundant angle avoid the singularity of redundant robot. Then, based on interactive intention estimation, a control strategy of variable impedance sliding mode theory in the presence of virtual force and contact force is proposed to achieve the trajectory tracking. We adopted hyperbolic tangent function to alleviate the chattering problem caused by switch function and validated the control system stability by Lyapunov theorem. Finally, Matlab simulations exhibit a 97.8% of high tracking accuracy amid the external force is 43% less than variable impedance parameters. It is therefore proved that the proposed method can achieve asymptotic tracking and the compliant behavior in physical human-robot interaction.
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Rivera-Dueñas, Juan C., and Marco A. Arteaga-Pérez. "Robot force control without dynamic model: theory and experiments." Robotica 31, no. 1 (April 20, 2012): 149–71. http://dx.doi.org/10.1017/s026357471200015x.
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SUMMARYAmong the many challenges to deal with, when a robot is interacting with its environment, friction at the contact surface and/or at the joints is one of the most important to be considered. In this paper we propose a control algorithm for the tracking of position and force (unconstrained orientation case only) of a manipulator end-effector that does not require the robot model for implementation. This characteristic has the advantage of making it capable to compensate friction effects without any previous estimation. Furthermore, no velocity measurements are needed, and the unit quaternion is employed for orientation control. Experimental and simulation results are provided.
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Madau, Riccardo, Daniele Colombara, Addison Alexander, Andrea Vacca, and Luigi Mazza. "An online estimation algorithm to predict external forces acting on a front-end loader." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 235, no. 9 (March 27, 2021): 1678–97. http://dx.doi.org/10.1177/09596518211005583.
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One of the most significant goals of earthmoving equipment is to maximize productivity during loading cycles. A real-time knowledge of the forces exchanged between the machine implement and the surrounding, that is, while digging, can be used in different ways to increase productivity. It can be used to determine the amount of material moved by the machine; or to find the optimal bucket trajectory; moreover, as input to traction control systems. This article presents an online force estimation algorithm able to predict vertical and horizontal forces exchanged between the front-loader and the surrounding environment, as well as the reaction forces through the implement itself. Taking the case of a 14-ton wheel loader as reference, this article illustrates the development of a simulation model for the analysis of the machine digging system, along with the instrumentation and testing of the proposed estimation algorithm. The model is divided into two sections describing, respectively, system kinematic and system dynamics. The kinematic model of the front-loader is compared against measurements, and results show an average error lower than 1%. The dynamic model predicts both hydraulic and dynamic features of the machine implement, achieving an accuracy on the payload mass within 2%–3%, even during dynamic conditions. The estimated pushing force reflects the expected behavior when tested for various pushing efforts and in different ground conditions. Eventually, the algorithm was tested on a complete loading cycle and the results show good consistency considering the measured front-loader trajectory and vehicle speed. The proposed model overcomes some drawbacks of the currently used technologies. For example, it allows for an online estimation of the bucket forces for any position of the implement. Although the results discussed in this article pertain to a specific reference machine, the proposed method can be extended to most wheel loaders equipped with standard digging equipment.
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Roveda, Loris, and Dario Piga. "Robust state dependent Riccati equation variable impedance control for robotic force-tracking tasks." International Journal of Intelligent Robotics and Applications 4, no. 4 (November 15, 2020): 507–19. http://dx.doi.org/10.1007/s41315-020-00153-0.
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AbstractIndustrial robots are increasingly used in highly flexible interaction tasks, where the intrinsic variability makes difficult to pre-program the manipulator for all the different scenarios. In such applications, interaction environments are commonly (partially) unknown to the robot, requiring the implemented controllers to take in charge for the stability of the interaction. While standard controllers are sensor-based, there is a growing need to make sensorless robots (i.e., most of the commercial robots are not equipped with force/torque sensors) able to sense the environment, properly reacting to the established interaction. This paper proposes a new methodology to sensorless force control manipulators. On the basis of sensorless Cartesian impedance control, an Extended Kalman Filter (EKF) is designed to estimate the interaction exchanged between the robot and the environment. Such an estimation is then used in order to close a robust high-performance force loop, designed exploiting a variable impedance control and a State Dependent Riccati Equation (SDRE) force controller. The described approach has been validated in simulations. A Franka EMIKA panda robot has been considered as a test platform. A probing task involving different materials (i.e., with different stiffness properties) has been considered to show the capabilities of the developed EKF (able to converge with limited errors) and controller (preserving stability and avoiding overshoots). The proposed controller has been compared with an LQR controller to show its improved performance.