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Industrial robot Comau SMART-3 S with force/torque sensor ATI FT30-100 and built end effector available in the PRISMA Lab.
Source publication
A great many control schemes for a robot manipulator interacting
with the environment have been developed in the literature. This paper
is aimed at presenting a survey of robot interaction control schemes for
a manipulator, the end effector of which comes in contact with a
compliant surface. A salient feature of the work is the implementation
of th...
Contexts in source publication
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... picture illustrating the robot with the wrist force sensor and the built end effector is given in Fig. 1, while a schematic of the open control architecture is depicted in Fig. ...
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... position as for the previous case studies. The end-effector task was the same as for the case study described in Section IV-A, while the same force trajectory as in Section IV-B started when contact was detected. The gains of the control action in (36)- (39) were set to N/m, N s/m, kg, and N s/m. The results are presented in the upper part of Fig. 11 in terms of the desired (dashed) and the actual (solid) end-effector path, together with the time history of the desired (dashed) and the actual (solid) contact force. As above, the approximate location (dotted) of the surface is illustrated on the plot of the end-effector path, while the instant of contact (dotted line) and the ...
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... for the case of impedance control with inner position loop in Section V-B, to investigate robustness of the scheme with respect to changes in the environment location, the task was repeated with the same control parameters as above, but the cardboard box was raised by about 0.025 m. From the results presented in the lower part of Fig. 11, it can be recognized that, despite the different location of the surface, the desired force set point is still achieved; however, larger values of contact force are obtained during the transient due to the larger impact ...
Citations
... Implementation of the feedback control paradigm is often performed using those simulators. Position, impedance and admittance control [9,27] follows this path. On the contrary, predictive control requires an accurate and computationally undemanding internal model. ...
Visual servoing enables tracking and grasping of static and moving objects and locating regions of interest. Its implementation requires the coordination of low-level control tasks with high-level supervision and planning. The use of the PID-based manipulation control addresses only the dynamical response and requires the use of optimization-based techniques at the supervisory level. On the contrary, model predictive control (MPC) simplifies the design as it combines both tasks within a single algorithm. However, successful MPC implementation depends on the quality of the internal model utilized by the algorithm. Robots are intrinsically and significantly nonlinear control objects, so their models are inherently complex. This leads to computational problems. Standard solutions are to shorten the MPC horizons or to parallelize calculations. The approach suggested here returns to the problem roots, i.e. to the model. This study proposes simplified models that allow the use of the MPC. General Hammerstein–Wiener-like model is applied to an arm, which is further simplified into the linear dynamics used as the predictive supervisory control over the torque control. The head uses linear dynamics used by the supervisory MPC working with the base servomotors. It is shown that they are computationally efficient and sufficiently accurate. It is shown that the linear MPC controllers, which use the suggested simplified dynamic robot models, can successfully support visual servoing with accurate control. They are compared with standard PID-based structures and show their superiority. The proposed approach is successfully validated using Matlab and Gazebo robot simulator and is ultimately confirmed by experiments on a real robot.
... methods in the motion control of rehabilitation robots. Impedance control is an effective method in which the patient performs active movements, and its advantage is the coordinated control of the dynamic equilibrium relationship between the position and force of the rehabilitation robot through the intention of the human body [96], [97], which ensures the safety of human-machine interaction. Zhang et al. [98] proposed a fuzzy impedance control, which is different from the traditional impedance control with fixed parameters, and they used a fuzzy control for the impedance control with respect to the rotational inertia, damping coefficients, and spring coefficients to achieve impedance control with variable parameters, thus showing better flexibility and more minor error. ...
The development of intelligent rehabilitation robots has greatly reduced the workload of rehabilitation physicians. Human-machine interaction (HMI) control methods are a critical technology for intelligent rehabilitation robots. Therefore, we systematically review the HMI methods and control strategies for upper and lower limb rehabilitation robots and summarizing the HMI methods with different sensors. The integration of rehabilitation robots and HMI control methods has grown significantly in recent years. For this reason, this paper takes the sensing methods as the entry point to giving readers a quick overview of the current status of HMI research. We present different sensing methods, interactive control strategies, applications, and evaluation methods, and discuss the limitations and future development directions in the field. The results show that the mainstream control methods of HMI are based on motion signals, surface electromyography (sEMG), ultrasound (US), and electroencephalogram (EEG). In the field of rehabilitation robotics, human intention recognition-based interaction strategy is the mainstream HMI strategy, which mainly collects bio-signals, force/moment, spatial angle and other information for human intention recognition. Future research may focus on the use of multi-modal sensing interactions, flexible control strategies, and generalized rehabilitation assessment mechanism.
... [11] se limiteà l'exploration des stratégies de contact pour les microcomposants. [12] se focalise sur l'analyse des schémas de contrôle des effecteurs finaux. Parmi les exemples modernes de préhenseurs, on peut citer le Kuka KR 1000 Titan, le bras robotique Dexter de la station spatiale internationale, le manipulateur d'élimination des explosifs iRobot 510 et le système chirurgical da Vinci [13], [14]. ...
Cette recherche bibliographique est centrée sur l'état de l'art de la conception des mécanismes de préhension destinés à manipuler des objets électroniques. Dans un premier temps, une revue générale a été réalisée pour mettre en lumière les travaux existants sur les micro et nano-pinces, en raison de la petite taille prédominante des objets électroniques. Ensuite, l'attention s'est focalisée sur l'état de l'art des mécanismes de préhension dont la conception s'avère compatible avec la manipulation d'objets électroniques. À partir de cette analyse, il a été déduit que la majorité des recherches en matière de préhension s'orientent vers le développement de mains robotiques adaptées à cette tâche particulière.
... Impedance control As mentioned in our previous study, the force-based impedance control is one of the efficient methods for the exoskeleton systems on both upper and lower limbs (Tran et al. (2016); Lee et al. (2018)). This is because the control method seeks to realize a specific impedance between the upper exoskeleton robot and its environment, i.e., the operators hand, rather than enforcing strict pre-defined exercises upon the robot (Zeng et al. (1997); Chiaverini et al. (1999)). From the viewpoint of impedance/admittance approaches, positionbased impedance control is commonly applied for robotic systems interacting with high stiffness environments while force-based impedance control is more suitable for the exoskeleton systems interacting with biomechanic environments significantly affected by inertial element (Tran et al. (2014)). ...
... For safe contact, the robot control system must maintain contact stability and ensure excessive force is not applied. Combined motion and contact control can be accomplished with closed-loop force control, where force/torque sensors are utilized to measure the interaction forces for feedback control, since it is difficult to model the contact forces a priori [101][102][103]. However, achieving high performance motion control while maintaining stability during contact is a big challenge in force-controlled systems [47,104]. ...
In this thesis, advanced model-based robot force control algorithms are developed exploiting the availability of multisensor information towards attenuating the sensor noises and suppressing the effects of force disturbances. For joint-space single-dof application, a reduced-order multisensor-based force observer (RMFOB) for accurately estimating the force exerted on a load is developed. To suppress the
effects of force disturbances in a robust way, a disturbance observer known as model-based force disturbance observer (FDOB) is proposed. Then, the RMFOB and FDOB are combined in a closed-loop setting to form a twofold observer-based force control system. Design methodology and systematic parameter tuning criteria for this double observer-based force control is developed. And lastly, towards high-performance motion control and contact stability, a novel integrated DOB (IDOB) is also developed and its effectiveness evaluated. The IDOB design concept is applied to a multi-dof system where an outer-loop integrated DOB-based admittance control method in task space (OIDOBt) is developed. This is implemented in task-space and outside the inner position/velocity control loop for the 6-DOF industrial manipulator.
... For safe contact, the robot control system must maintain contact stability and ensure excessive force is not applied. Combined motion and contact control can be accomplished with closed-loop force control, where force/torque sensors are utilized to measure the interaction forces for feedback control, since it is difficult to model the contact forces a priori [6], [7], [8]. However, achieving high-performance motion control while maintaining stability during contact is a big challenge in force-controlled systems [9], [10]. ...
For robotic tasks that involve combined transmitting and contact force control, achieving high-performance motion control while ensuring stable environment contact is difficult. Among the factors that affect the quality of this force control, we may account vibrations due to misalignment in the mechanical components, actuator inaccuracies, non-linear effects of friction, and backlash. All the above factors can be collectively considered as force disturbances. Towards high-performance motion control and contact stability, a novel integrated disturbance observer (IDOB) is proposed. The IDOB uses force sensor measurements with position measurements and a plant model to isolate and robustly suppress the effects of force disturbances within the plant without compromising contact stability. This is applied here to a force control system to demonstrate the enhanced force control performance in free space and in contact. The passivity, robust stability, and disturbance rejection of the proposed IDOB are compared with those of existing force controllers, with and without force-based DOBs. Finally, actual experiments are conducted in free space and contact under various interaction conditions, showing that the IDOB improves transmitting force control and disturbance suppression performances. Moreover, peak collision force is reduced while maintaining contact stability with stiff environments.
... This is the goal of Interaction Control (IC) strategies, which arbitrarily shape the dynamical relation between the system end-point variables such as position (or velocity) and force, rather than directly control one of the two [17]. This control paradigm has been successfully used in robotics (see, e.g., the survey papers [18], [19], or recent contributions [19], [20]). IC laws can be used to effectively compensate DE nonlinearities and dissipative phenomena, thus increasing speed and accuracy of controllable stiffness devices. ...
... However, the mixed performance-control gain tradeoff formulation proposed here has some important peculiarities. While standard approaches for IC of nonlinear systems rely on direct elimination of system nonlinearities [18], our approach is based on a linear law that requires much smaller implementation and online computation efforts. However, differently from other linear strategies, such as PID design based on linearized models, our approach provides guaranteed performance in the entire operating range. ...
... By replacing (18) in (15), the resulting closed loop system is obtained. The gain K has to be designed such that the closed loop system satisfies the following conflicting specifications: 1) Keep the L2 gain from the exogenous input w to z smaller than a desired upper bound λ > 0, to minimize the effects of exogenous inputs on filtered interaction error. ...
This paper presents an interaction control algorithm for a dielectric elastomer membrane actuator. The proposed method permits efficient exploitation of the controllable stiffness of the material, allowing to use the membrane as a "programmable spring" in applications such as robotic manipulation or haptic devices. To achieve this goal, we propose a design algorithm based on robust control theory and linear matrix inequalities. The resulting controller permits to arbitrarily shape the stiffness of the elastomer, while providing robust stability and performance with respect to model nonlinearities. A self-sensing displacement estimation algorithm allows implementation of the method without the need of a deformation sensor, thus reducing cost and size of the system. The approach is validated on an experimental prototype consisting of an elastomer membrane preloaded with a bistable biasing spring.
... Impedance control can be considered as a suitable candidate for interaction control strategy as it does not relay on the precise knowledge of external parameters [191]. Unlike other control strategies, where the force and position are controlled separately, impedance controller controls the dynamic relationship between the force and velocity, also known as impedance of the CDRR's end-effector [192], [193]. The impedance control does not have significant difference with position control except it offers less interaction torque and large spatial variety [190]. ...
Significant attention has been paid to robotic rehabilitation using various types of actuator and power transmission. Amongst those, cable-driven rehabilitation robots (CDRRs) are relatively newer and their control strategies have been evolving in recent years. CDRRs offer several promising features, such as low inertia, lightweight, high payload-to-weight ratio, large work-space and configurability. In this paper, we categorize and review the cable-driven rehabilitation robots in three main groups concerning their applications for upper limb, lower limb, and waist rehabilitation. For each group, target movements are identified, and promising designs of CDRRs are analyzed in terms of types of actuators, controllers and their interactions with humans. Particular attention has been given to robots with verified clinical performance in actual rehabilitation settings. A large part of this paper is dedicated to comparing the control strategies and techniques of CDRRs under five main categories of: Impedance-based, PID-based, Admittance-based, Assist-as-needed (AAN) and Adaptive controllers. We have carefully contrasted the advantages and disadvantages of those methods with the aim of assisting the design of future CDRRs
... Impedance control can be considered as a suitable candidate for interaction control strategy as it does not relay on the precise knowledge of external parameters [191]. Unlike other control strategies, where the force and position are controlled separately, impedance controller controls the dynamic relationship between the force and velocity, also known as impedance of the CDRR's end-effector [192], [193]. The impedance control does not have significant difference with position control except it offers less interaction torque and large spatial variety [190]. ...
Significant attention has been paid to robotic rehabilitation using various types of actuator and power transmission. Amongst those, cable driven rehabilitation robots (CDRRs) are relatively newer and their control strategies have been evolving in recent years. CDRRs offer several promising features, such as low inertia, lightweight, high payload-to-weight ratio, large work-space and configurability. In this paper, we categorize and review the cable driven rehabilitation robots in three main groups concerning their applications for upper limb, lower limb, and waist rehabilitation. For each group, target movements are identified, and promising designs of CDRRs are analyzed in terms of types of actuators, controllers and their interactions with human. Particular attention has been given to robots with verified clinical performance in actual rehabilitation settings. A large part of this paper is dedicated to comparing the control strategies and techniques of CDRRs under five main categories of: Impedance-based, PID-based, Admittance-based, Assist-as-needed (AAN) and Adaptive controllers. We have carefully contrasted the advantages and disadvantages of those methods with the aim of assisting the design of future CDRRs.
... For analysis purposes, the environment is modeled as a frictionless and elastically compliant plane, which is very common in force control [14]. One contact point is considered, and the contact force is expressed as, ( ) e e f K x x = − (11) where x is the end-effector position, e x is the position at the contact point, and e K is the constant symmetric stiffness matrix of the environment. ...
