Feedback control of soft robot actuators via commercial flex bend sensors

Abstract:

Soft robotics is an emerging field, which takes advantage of compliant materials and makes use of non-standard actuators. Flexible Fluid Actuators (FFAs) use fluid pressure to produce high deformation of elastomeric-based structures. However, the closed loop control of such actuators is still very challenging due to the lack of robust, reliable and inexpensive sensors which can be integrated with highly deformable actuator structures, which involve very low-cost materials and manufacturing. This work presents a systematic approach to implement the feedback control of FFA-based soft robotic bending module by using commercial flex bend sensors. A flex bend sensor detects the module curvature in one direction and its response is processed by a wireless micro-controller on board of the module and sent to the central control system. Such sensor integration enables the closed loop control of modular robotic architectures, often used in soft robotics. Once integrated with the soft module, the sensor response was calibrated by the use of ground truth (GT) magnetic tracking system in order to characterize its behavior when combined with the relative FFA. A feedback control using an LP filter and a PI controller was designed and used to evaluate the dynamic response and the position accuracy of the integrated system. With such closed loop control, the module tip is can be positioned with less than 1 mm accuracy, which can be considered a relevant result in the soft robotics field.

EXISTING  SYSTEM:

Embedded sensors for soft robotics must withstand very large deformations without altering the high compliance of the associated actuator. In this framework, the field of stretchable electronics is gaining an increasing interest [10]. This approach tries to develop electronic circuits than can be printed on soft, stretchable, foldable and even biocompatible materials [11]. [12] and [13] describe the development of elastomeric-based curvature and stretching sensors that conform to the actuator deformation without interfering with the natural mechanics of motion. Alternatively, the approach of [14] and [15] uses micro channels filled by liquid conductors placed into the actuator structure. The deformation of the cross-section of the micro channels leads to a change in electrical resistance. A slightly different method is used in [16], which employs a biocompatible sodium chloride (NaCl) solution as conductive fluid to generate sensing channels able to detect actuator structure stretching. A simpler soft actuatorlevel sensing system is described in [17]. In this work the length of pneumatic artificial muscle (PAM) is measured by using a smart braid (i.e. conductive insulated wire), which is wrapped around the actuator and increases its inductance when the actuator shortens. Although all the previously mentioned sensing methods offer high level of shape customization, easing adapting the sensor’s shape to the large variety of soft robot designs, they come at the cost of very complicated fabrication processes, which deeply affect the sensor reliability and repeatability.

PROPOSED  SYSTEM:

We propose the use of commercial resistive flex sensors because they offer a compact, lightweight and low-cost solution for soft robotics. They are a very popular solution for the accurate measurement of fingers’ joints posture and they are often integrated onto wearable devices, such as gloves [22]. Flex bending sensors come in the form of thin flexible membranes that produce a change in electrical resistance when bent in one direction (i.e. increasing bend angle is associated with increased resistance). The sensor body is essentially a flexible membrane composed of a layer of conductive material (ink-coating) combined with a very thin and flexible film substrate. The bending motion of the membrane forces conductive particles in the conductive ink to move apart, thus increasing the resistance to current flow when a differential electric potential is (i.e. voltage) is applied to the sensor’s extremities [23]. This effect is generated when the ink-coating is stretched, thus when the flexible substrate is curved having the ink-coating on the external side of the curvature. This change in resistance allows to detect curvatures in one direction of bending, thus when the ink-coating layer is stretched. At the same time, its structure remains flexible and perfectly functioning if the sensor is bent in the opposite direction. This flexibility combined with the low-cost and shape, enables the use of such sensing technique in antagonistic pair configuration soft actuators.

CONCLUSION:

In recent times, the demand for safe robot-human (or external objects) interaction has deeply increased. Soft robotics is trying to cope with this request by using the shock adsorption proprieties of the materials composing the robot actuators. However, such actuators are currently lacking reliable embedded, or proprioceptive, sensing systems, which would enable the development of effective feedback controllers. This work describes the integration of commercial flex bend sensors within a soft bending module actuated by pressure driven Flexible Fluidic Actuators (FFAs). A various emergent soft robotics sensing techniques, flex bend sensors were selected due to their flexibility, robustness, ease of integration on modular structures and low-cost nature of the materials. The sensor response has been firstly calibrated with respect to the module motion and successively a feedback controller has been designed. The system has been evaluated in terms of accuracy and repeatably. We observed an accuracy error under 1 mm in positioning the tip of the module. The achieved accuracy is suitable for soft robotic surgical manipulators, intended for tasks such as camera navigation in constrained environments [25]. We consider this work as a first milestone towards the implementation of fully embedded feedback control of a soft robotic structure. Future works could involve the use of more complex module architectures, involving, for example, more actuators per module and more sophisticated flex sensors, which can bend in the three-dimensional space and/or are able to detect intermediate longitudinal points of the module curvature. With such integrated servo systems, modular soft robots could potentially represent a valid alternative to rigidlink robots, paving the way to new paradigms of robotic control and enabling tasks of robotic manipulation in complex and unstructured environments with the presence of delicate objects.

 REFERENCES:

[1] D. Rus and M. T. Tolley, “Design, fabrication and control of soft robots,” Nature, vol. 521, no. 7553, pp. 467–475, 2015.

[2] C. Laschi and M. Cianchetti, “Soft robotics: new perspectives for robot bodyware and control,” Bionics and Biomimetics, vol. 2, p. 3, 2014.

[3] M. Cianchetti, T. Ranzani, G. Gerboni, T. Nanayakkara, K. Althoefer, P. Dasgupta, and A. Menciassi, “Soft robotics technologies to address shortcomings in today’s minimally invasive surgery: the stiff-flop approach,” Soft Robotics, vol. 1, no. 2, pp. 122–131, 2014.

[4] G. Gerboni, T. Ranzani, A. Diodato, G. Ciuti, M. Cianchetti, and A. Menciassi, “Modular soft mechatronic manipulator for minimally invasive surgery: overall architecture and development of a fully integrated soft module,” Meccanica, vol. 50, no. 11, pp. 2865–2878, 2015.

[5] C. D. Onal and D. Rus, “Autonomous undulatory serpentine locomotion utilizing body dynamics of a fluidic soft robot,” Bioinspiration & biomimetics, vol. 8, no. 2, p. 026003, 2013.

[6] B. Chang, A. Chew, N. Naghshineh, and C. Menon, “A spatial bending fluidic actuator: fabrication and quasi-static characteristics,” Smart Materials and Structures, vol. 21, no. 4, p. 045008, 2012.

[7] P. Polygerinos, Z. Wang, J. T. Overvelde, K. C. Galloway, R. J. Wood, K. Bertoldi, and C. J. Walsh, “Modeling of soft fiber-reinforced bending actuators,” IEEE Transactions on Robotics, vol. 31, no. 3, pp. 778–789, 2015.

[8] T. Ranzani, G. Gerboni, M. Cianchetti, and A. Menciassi, “A bioinspired soft manipulator for minimally invasive surgery,” Bioinspiration & biomimetics, vol. 10, no. 3, p. 035008, 2015.

[9] C. Lee, M. Kim, Y. J. Kim, N. Hong, S. Ryu, H. J. Kim, and S. Kim, “Soft robot review,” International Journal of Control, Automation and Systems, pp. 1–13, 2015.

[10] N. Lu and S. Yang, “Mechanics for stretchable sensors,” Current Opinion in Solid State and Materials Science, vol. 19, no. 3, pp. 149–159, 2015.