Wearable Robots: Biomechatronic Exoskeletons A wearable robot is a mechatronic system that is designed around the shape and function of. Exoskeletons: an instance of wearable robots. 5. The role of bioinspiration and biomechatronics in wearable robots. 6. Bioinspiration in the design. Wearable robots: biomechatronic exoskeletons. Responsibility: edited by José L. Pons. Imprint: Chichester, West Sussex, England ; Hoboken, NJ: John Wiley.
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Wearable robots: biomechatronic exoskeletons. [José L Pons;] -- "A wearable robot is a mechatronic system that is designed around the shape and function of . Request PDF on ResearchGate | On Mar 5, , J. L. Pons and others published In book: Wearable Robots: Biomechatronic Exoskeletons, pp.1 - In particular, wearable robots have been a focus of the robot. researches in [5 ] J. L. Pons, Wearable Robots: Biomechatronic Exoskeletons.
In the case of the human arm, the graphics was obtained using the three movements of the shoulder and the flections of the elbow. The arm was moved with movements of flexion-extension and, finally, the rotation of the elbow.
The Fig. Exists a space that the arm cannot reach, this space correspond to the sector near to the back, where is impossible to reach due to the physics restrictions for the body.
Is important to say that in ideal conditions, the workspace in both arms is the same. Therefore, the workspace of a wearer that carries an exoskeleton could be affected by the mechanical restrictions that the system imposes, for this reason, better mechanical designs allows the performance of movements more obedient and with a wider rank.
Dynamics While the kinematics analysis gives an approach of the system, the dynamic model generates the necessary information about the forces needed to umbalnce the segments an initiate the movement, and posteriorly the behavior of the system across the differents possible positions that the arm could reach.
Refers to other autors Rosen et al. In other cases, the mechanical system is more heavier than the human arm Croshaw, , in these models the structure must be included into the analysis, this cause that parameters like intertial center and estructure change drastically. Nevertheless, with a adecuate code this changes could be easilly modifcated, because this information could be parametrized or previouslly changed to the simulation code.
Wearable robots : biomechatronic exoskeletons
Figure 6 illustrated the model of the arm, C1 and C2 are the possition of the center of mass on the arm segments, the rotations consdired into the study are the shoulder movements and the flexion-extension of the foream. The initial evaluation are focused into the minimal forces necessaries to move an human arm, in order to do that, is necessary to known its composition.
In this way, parameters like mass, length and position of the inertial center that depends of the wearer stature, are introduced into the model and simulated to obtain a further analysis. With the kinematics model, the information of the position of the ends of segments and inertial centers could be added into the model.
The final result is a nonlinear mechanical model that simulates the arm behavior in different movements. Human arm model For the evaluation of the forces that involves the movement of the system, a PD controller is use to move the arm from an initial position to a desired position. Movements like lift the arm while the forearm is static or move only the forearm could be simulated.
Initiating in different positions and with various wearer statures, the code, gives the necessary information about the maximal and minimal forces involved, the influence of the Coriollis and centrifugal forces and the gravitational forces into the arm, and also plausible reductions into the model to reduce the computational time in the simulation.
Each movement was controlled using a feedback rule 8 , it gives to the system the force needed to move the segments to de desired position, given by qnd, its equivalent to a PD controller, its applications are seen in industrial robots and lower parts exoskeletons Carignan, et al.
Wn are the forces that the actuators have to support when the segments are in static equilibrium. The implementation of these controllers adds a problem called overshoot a high input that occurs in a small portion of time into the beginning of the simulation Figure 6.
Presence of overshoots at the beginning of the simulation In order to resolve this problem, a trajectory generator is added to the system control, this generator increments the position of the segment considering the initial an finally positions, in this way is possible to create trajectory that descript a desired behavior applying interpolation methods, like cubical, quadratic or another equation.
Considering that the model initiate and finish with zero speed, the form that could be implemented into the model is a cubical equation Figure 7 , which is implemented into 8 replacing the term qnd. Variation of the angular position in a cubical way The movement is generated around the movement determined by the trajectory equation, the Figures 8 and 9, shows a common displacement between two points in the shoulder and elbow respectively, this movement is called plane movement because only actuate in one degree of freedom of the shoulder.
The dot line represents the movement interpolated by the cubic equation, and the solid line is the movement generated by the degrees of freedom of the system that are actuated, moving according by the path and controlled by equation 8. Comparison between the movement generated by the controller into the shoulder and the elbow respects the desired path www. The final position also shows a displacement, the error in this position is near to 0,5 degrees in both movements.
Difference between the shoulder and elbow movement with the desired path To evaluate the controller, different movements were generated and the result is showed in Fig. The internal forces between the segments cause this variations, the forearm in this position generates the biggest moment to the arm in movement.
Bigger values into the constants reduces the difference in the displacement generated by the internal forces of the arm, the controller supplies the necessary force and consecutively the error of displacements change according to the variation of the constant. The maximal forces generated by the controller are illustrated in Fig. The blue line effectuates the same movement in a time that is proportional with the space travelled, in other words, faster than the first move.
This movement generates bigger forces because effectuate the same movement with less time, the augmentation of the velocities into the generalized coordinate generates an augmentation of the Coriollis forces between the segments, generating in the controller a bigger force to counteract the impulse generated by the movement and the mass of the arm. Deviation of the final position of the wrist depending of the position of the forearm while the arm is rotating into flexion movement Also this forces changes depending of the height in the individual, as is show in 6 and Table 2, the mass on the arms differ depending of the stature of the individual and his BMI, in this case, the maximal forces in the arm which actuator have to support the weight of the complete system are illustrated in Fig 16, where in a range of heights from 1.
Variation into the maximal forces for movements in the arm with different velocities Fig. Variation into the maximal forces for movements in the forearm arm with different velocities www. Variation into the maximal forces with different individual heights 1 2 4 3 Fig. Movement in the generalized coordinates generated by the controller 1 , trajectory in the space described by the arm 2 , projection in the transversal plane 3 and the sagittal plane 4 www.
Wearable robots : biomechatronic exoskeletons
The implementation of the PD controller with a compensation of the gravitational forces in the arm position allows to made the previously forces analysis, the constants have an important role in the system behaviour, adding stabilities and reducing the error in the positioning of the arm wrist.
Differential flatness Introduced by Fliess Fliess et al. In order to do that, the flat outputs are parameterized into functions, and posterior introduced into the system; the result is a non linear equation system that is transformed into a polynomial, so, the solution of the problem is reduced significantly because is not necessary the use of integration or numerical solutions.
Many systems are defined as flat and some authors have studied its implications Murray et al. In this kind of problems the first step consist in the specification of a sequel of points in the manipulator space work, this points will become desires positions across the path, and posterior are integrated using an interpolation function which is typically in a polynomial form.
Actually, different techniques exist for the trajectory planning: i when is given each final and ending position, talk about a point to point motion, ii when is working with a finite point sequence, talk about motion through a sequence of points. Both techniques give a time function that describes the desired behavior Siciliano, , van Nieuwstadt, a.
In this part, is necessary to take a decision, if the problem is going to be worked into the operational space, or in each joint. Everything joint parameterized trajectory is defined by a function q t. This function is the source of a joint desired position collections for the manipulators movement across the time.
Then, using different control techniques: like the PD control with gravity compensation, IDC Inverse Dynamic Control or adaptive control, is possible accomplish a manipulator movement on the defined path with the minimal deviation Marguitu, , Spong, , Wang, The application of the flat theories allows the transformation of the differential equation system 12 into a polynomial of order n, making a simplification into the problem solution and transforming it to a completely algebraic system.
The next step is to applying the differential flat theories into the model; the general objective is control the behavior of the system in function of a desired action. This behavior will be parameterized into a trajectory.
With knowledge of the flat outputs of the system, these trajectories could be easily implemented.
The model in 12 shows that is possible to interpret the systems inputs knowing its flat outputs behaviors until the second derivative, therefore, is necessary add this functions and its derivatives into the model. The equations 13 and 14 and its respective derivatives are introduced in the dynamic model The response provided by the Fig. This response is evaluated into the dynamic model 8 , the system response is verified in Fig.
Due to the proximity in each equation, Fig. Set of forces obtained through equation 8 in each segment Fig. Response of the system according of the forces The obtained response Fig. Tracking error in each generalized coordinate Fig. It is necessary then, to change the focus, in order to take advantage of the benefits that flatness offers. This scheme implementing a controller that feedback information from real estate and compare it with the desired output, generated from this difference a control action that finally allows the system track the trajectory desired with a minimum error, this is made with the implementation of a controller into the system.
Called two degrees of freedom control Fig. The controller modifies the output in function of existing error between the actual output and nominated desired output Van Nieuwstadt, b. This system had actually three degrees of freedom, such system contains a higher level that generate the output depending of the desired paths output trajectory.
An intermediate www. The uncertainties were variations caused by the gravitational, friction forces, and nonlinear in the models. Two degree of Freedom Control System Van Nieuswtadt, b The solution gives an input trajectory that could be used into the real model.
This assumption works in theory, however some interferences, could separate the real behaviour of the desired, thus, is necessary to apply a PD feedback control into each generalized coordinate, with this, is possibly to enforce the manipulator to describe a desired performance. For the controller in Fig. The derivative constant kd, removes variations oscillations caused by the corrections in the proportional part.
In Fig. The results of the implementation of these values can be seen in Fig.
With this control, is easier to move the system using others trajectories, any position that is within the manipulator workspace, can be parameterized by a polynomial for then of differentially flatness systems theory, appropriate requirements. Trajectory tracking using the controller Fig. System behavior using the inputs defined by flatness Fig. Force behaviour obtained according with the cosine function implemented in the arm www. System behaviour using the desired trajectories Fig. System behaviour using the entries defined by differential flatness The concept is also applicable to systems with more degrees of freedom, although the fact that the equations of motion are more complex it is possible to obtain path tracking and behaviors.
But this implementation presents difficulties, inputs equations in function of four outputs are extensive and prevent the possibility of achieving an acceptable result for extensive times. Precisely this error, forced to resort to the use of a controller. Open loop system presented difficulties during the follow-up. The controller makes modifications imperceptibles on the inputs, but enough so that there is a stable trajectory.
The ability to follow-up on trajectories using differential flatness systems on robotics manipulators depends on the quality of input values. Was founded that small changes in these settings offer better performance, proving that the resolution of inputs is an important part of the results.
The tests carried out previously, the models were analyzed over a time period in increments of 0. But testing with greater increases do not provide appropriate behavior, as it may be identified in Fig.
This restriction to use short times make it difficult for the calculation of inputs due to the high consumption of computing resources, therefore, in to reconfigure a strategy for solution of this situation. Behavior of system for each trajectory, the dash line describes the actual movement while continuous line describes the desired trajectory Fig.
Behavior model with higher time period, are evidence a difficulty in the follow-up to the desired output 5. Conclusions This study gives to us the initial phases into the consecution of a trajectory control for an upper arm exoskeleton. Whit the forward kinematics study is possibly to generate a fist view on the system, this mathematical model achieved by means of a morphological study of the human arm gives a first view of the system behaviour, being this workspace a tool to predict the system capabilities in function of the mechanical restrictions and also the human movements.
This one is an important implementation of the code made. Every consideration on the physical model of the system must be considered in the dynamical design. This dynamical analysis generates a mathematical model that is implementing into the problem of the trajectory tracking on the system. It was demonstrated that the arm is differential flat, and with this assumption, was possibly to generate any behaviour to be implemented into the system, it means a path in each generalized coordinate.
In order to ensure a correct tracking, a PD controller was added into each generalized coordinate, the simulations shows that the model of the upper arm get a closely approach to the desired path, and with that is possibly the imposition of behaviours more specific, only defined by polynomial functions. References Carignan, C. Proceedings of 12th International Conference of Advanced Robotics , pp.
Croshaw, P. General Electric Co. Shcenectady NY. Specialty Materials Handling Products Operation. The main topics are demonstrated through two detailed case studies; one on a lower limb active orthosis for a human leg, and one on a wearable robot that suppresses upper limb tremor.
These examples highlight the difficulties and potentialities in this area of technology, illustrating how design decisions should be made based on these. As well as discussing the cognitive interaction between human and robot, this comprehensive text also covers:. Wearable Robotics: Biomechatronic Exoskeletons will appeal to lecturers, senior undergraduate students, postgraduates and other researchers of medical, electrical and bio engineering who are interested in the area of assistive robotics.
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Wearable Robots: Biomechatronic Exoskeletons Editor s: First published: Print ISBN: About this book A wearable robot is a mechatronic system that is designed around the shape and function of the human body, with segments and joints corresponding to those of the person it is externally coupled with. Teleoperation and power amplification were the first applications, but after recent technological advances the range of application fields has widened. Increasing recognition from the scientific community means that this technology is now employed in telemanipulation, man-amplification, neuromotor control research and rehabilitation, and to assist with impaired human motor control.ExoNET F.
Goldberg guides you through your own personal or group retreat, let us start by describing the propagation of waves in a sufficiently ordered or regular environment. Moreno, L. In : Pons, J.
Turowska and A. This behavior will be parameterized into a trajectory. These examples highlight the difficulties and potentialities in this area of technology, illustrating how design decisions should be made based on these.
Open loop system presented difficulties during the follow-up. Bigger values into the constants reduces the difference in the displacement generated by the internal forces of the arm, the controller supplies the necessary force and consecutively the error of displacements change according to the variation of the constant.
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