A continuum robot is a type of robot that is characterised by infinite degrees of freedom and number of joints. These characteristics allow continuum manipulators to adjust and modify their shape at any point along their length, granting them the possibility to work in confined spaces and complex environments where standard rigid-link robots cannot operate.[1] In particular, we can define a continuum robot as an actuatable structure whose constitutive material forms curves with continuous tangent vectors.[2] This is a fundamental definition that allows to distinguish between continuum robots and snake-arm robots or hyper-redundant manipulators: the presence of rigid links and joints allows them to only approximately perform curves with continuous tangent vectors.

The design of continuum robots is bioinspired, as the intent is to resemble biological trunks, snakes and tentacles. Several concepts of continuum robots have been commercialised and can be found in many different domains of application, ranging from the medical field to undersea exploration.

Classification

Continuum robots can be categorised according to two main criteria: structure and actuation.[2]

Structure

The main characteristic of the design of continuum robots is the presence of a continuously curving core structure, named backbone, whose shape can be actuated. The backbone must also be compliant, meaning that the backbone yields smoothly to external loads.[3]

According to the design principles chosen for the continuum manipulator, we can distinguish between:

  • single-backbone: these continuum manipulators have one central elastic backbone through which actuation/transmission elements can run.
  • multi-backbone: the structure of these continuum robots has two or more elastic elements (either rods or tubes) parallel to each other and constrained with one another in some way.[4]
  • concentric-tube: the backbone is made of concentric tubes that are free to rotate and translate between each other, depending on the actuation happening at the base of the robot.[3]

Actuation

The actuation strategy of continuum manipulators can be distinguished between extrinsic or intrinsic actuation, depending on where the actuation happens:

  • extrinsic actuation: the actuation happens outside the main structure of the robot and the forces are transmitted via mechanical transmission; among these techniques, there are cable/tendon driven actuators and multi-backbone strategies.
  • intrinsic actuation: the actuation mechanism operates within the structure of the robot; these strategies include pneumatic[5] or hydraulic chambers[6] and the shape memory effect.[7]

Advantages

The particular design of continuum robots offers several advantages with respect to rigid-link robots. First of all, as already said, continuum robots can more easily operate in environments that require a high level of dexterity, adaptability and flexibility. Moreover, the simplicity of their structure makes continuum robots more prone to miniaturisation. The rise of continuum robots has also paved the way for the development of soft continuum manipulators. These continuum manipulators are made of highly compliant materials that are flexible and can adapt and deform according to the surrounding environment. The "softness" of their material grants higher safety in human-robot interactions.[8]

Disadvantages

The particular design of continuum robots also introduces many challenges. To properly and safely use continuum robots, it is crucial to have an accurate force and shape sensing system. Traditionally, this is done using cameras that are not suitable for some of the applications of continuum robots (e.g. minimally invasive surgery), or using electromagnetic sensors that are however disturbed by the presence of magnetic objects in the environment. To solve this issue, in the last years fiber-Bragg-grating sensors have been proposed as a possible alternative and have shown promising results.[9][10] It is also necessary to notice that while the mechanical properties of rigid-link robots are fully understood, the comprehension of the behaviour and properties of continuum robots is still subject of study and debate.[1] This poses new challenges in developing accurate models and control algorithms for this kind of robots.

Modelling

Creating an accurate model that can predict the shape of a continuum robot allows to properly control the robot's shape.[11] There are three main approaches to model continuum robots:

  • Cosserat rod theory: this approach is an exact solution to the static of a continuum robot, as it is not subject to any assumption. It solves a set of equilibrium equations between position, orientation, internal force and torque of the robot. This method requires to be solved numerically and it is therefore computationally expensive, due to its high complexity.[11][12]
  • Constant curvature: this technique assumes the backbone to be made of a series of mutually tangent sections that can be approximated as arcs with constant curvature. This approach is also known as piecewise constant-curvature. This assumption can be applied to the entire segment of the backbone or to its subsegments.[13] This model has shown promising results, however it must be taken into account that the segment/subsegments of the backbone may not comply to the constant curvature assumption and therefore the model's behaviour may not entirely reflect the behaviour of the robot.
  • Rigid-link model: this approach is based on the assumption that the continuum robot can be divided in small segments with rigid links. This is a strong assumption, since if the number of segments is too low, the model hardly behaves like the continuum robot, while increasing the number of segments means increasing the number of variables, and thus complexity. Despite this limitation, rigid-link modelling allows the use of the standard control techniques that are well known for rigid-link robots. It has been proven that this model can be coupled with shape and force sensing to mitigate its inaccuracy and can lead to promising results.[14]

Sensing

To develop accurate control algorithms, it is necessary to complement the presented modelling techniques with real time shape sensing. The following options are currently available:

  • Electromagnetic (EM) sensing: shape is reconstructed thanks to the mutual induction between a magnetic field generator and a magnetic field sensor.[15] The most common external EM tracking system is the commercially available NDI Aurora: small sensors can be placed on the robot and their position is tracked in an external generated magnetic field. The validity of this method has been extensively assessed,[16][17] however its performance is hindered by the limited workspace, whose dimension depends on the magnetic field. Another alternative is to embed the sensors internally in the continuum robot, combining magnetic sensors with Hall effect sensors:[18][19] the magnetic field is measured at the level of the Hall effect sensors in order to estimate the deflection of the robot. However, it has been noticed that the higher the bending of the manipulator, the higher is the estimation error, due to crosstalk between sensors and magnets.
  • Optical sensing: fiber Bragg grating sensors incorporated in an optical fiber can be embedded into the backbone of the continuum robot to estimate its shape; these sensors can only reflect a small range of the input light spectrum depending on their strain; therefore, by measuring the strain on each sensor it is possible to obtain the shape of the robot. This type of sensor is however expensive and is more prone to breaking in case of excessive strain, and this can happen in robots that can perform high deflections.

Control strategies

The control strategies can be distinguished in static and dynamic; the first one is based on the steady-state assumption, while the latter also considers the dynamic behaviour of the continuum robot. We can also differentiate between model-based controllers, that depend on a model of the robot, and model-free, that learn the robot's behaviour from data.[20]

  • Model-based static controllers: they rely on one of the modelling approaches presented above; once the model is defined, the kinematics must be inverted to obtain the desired actuator or configuration space variables. There are several ways to do this, like differential inverse kinematics, direct inversion or optimization.
  • Model-free static controllers: these approaches learn directly, via machine learning techniques (e.g. regression methods and neural networks), the inverse kinematic or the direct kinematic representation of the continuum robot from collected data, and they are also known as data-driven methods. Even though these controllers present the advantage of not having to establish an accurate model of the continuum robot, they perform worse than their model-based counterpart.
  • Model-based dynamic controllers: they need the formulation of the kinematic model and an associated dynamic formulation. As of 2021, they are in the early stage, as they require high computational power and high-dimensional sensory feedback. With improvements in computational power and sensing capabilities they could be crucial in industrial applications of continuum robots, where time and cost are also relevant along with accuracy.
  • Model-free dynamic controllers: they are still a relatively unexplored approach. Some works that propose machine learning techniques to learn the dynamic behaviour of continuum robots have been presented, but their performance is limited by high training time and instability of the machine learning model.

Hybrid approaches, that combine model-free and model-based controllers, can also present a valid alternative.

Applications

Continuum robots have been applied in many different fields.

Medical

Continuum robots have been widely applied in the medical field, in particular for minimally invasive surgery.[1] For example, Ion by Intuitive is a robotic-assisted endoluminal platform for minimally invasive peripheral lung biopsy, that allows to reach nodules located in peripheral areas of the lungs that cannot be reached by standard instrumentations; this allows to perform early-stage diagnoses of cancer.

Hazardous places

Continuum robots offer the possibility of completing tasks in hazardous and hostile environments. For example, a quadruped robot with continuum limbs has been developed: it can walk, crawl, trot and propel to whole arm grasping to negotiate difficult obstacles.[21]

Space

NASA has developed a continuum manipulator, named Tendril, that can extend into crevasses and under thermal blankets to access areas that would be otherwise inaccessible with conventional means.[22]

Subsea

The AMADEUS project developed a dextrous underwater robot for grasping and manipulation tasks, while the FLAPS project created propulsion systems that replicate the mechanisms of fish swimming.[23]

See also

References

  1. 1 2 3 da Veiga, Tomas; Chandler, James H; Lloyd, Peter; Pittiglio, Giovanni; Wilkinson, Nathan J; Hoshiar, Ali K; Harris, Russell A; Valdastri, Pietro (2020-08-03). "Challenges of continuum robots in clinical context: a review". Progress in Biomedical Engineering. 2 (3): 032003. doi:10.1088/2516-1091/ab9f41. ISSN 2516-1091. S2CID 225400772.
  2. 1 2 Burgner-Kahrs, Jessica; Rucker, D. Caleb; Choset, Howie (December 2015). "Continuum Robots for Medical Applications: A Survey". IEEE Transactions on Robotics. 31 (6): 1261–1280. doi:10.1109/TRO.2015.2489500. ISSN 1552-3098. S2CID 9660483.
  3. 1 2 Walker, Ian D. (2013-07-16). "Continuous Backbone "Continuum" Robot Manipulators". ISRN Robotics. 2013: 1–19. doi:10.5402/2013/726506.
  4. ↑ Bajo, Andrea; Simaan, Nabil (April 2016). "Hybrid motion/force control of multi-backbone continuum robots". The International Journal of Robotics Research. 35 (4): 422–434. doi:10.1177/0278364915584806. ISSN 0278-3649. S2CID 206500774.
  5. ↑ Chen, Gang; Pham, Minh Tu; Redarce, Tanneguy (2008), Lee, Sukhan; Suh, Il Hong; Kim, Mun Sang (eds.), "A Guidance Control Strategy for Semi-autonomous Colonoscopy Using a Continuum Robot", Recent Progress in Robotics: Viable Robotic Service to Human: An Edition of the Selected Papers from the 13th International Conference on Advanced Robotics, Lecture Notes in Control and Information Sciences, Berlin, Heidelberg: Springer, vol. 370, pp. 63–78, doi:10.1007/978-3-540-76729-9_6, ISBN 978-3-540-76729-9
  6. ↑ Ikuta, K.; Ichikawa, H.; Suzuki, K.; Yajima, D. (2006). "Multi-degree of freedom hydraulic pressure driven safety active catheter". Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006. Orlando, FL, USA: IEEE. pp. 4161–4166. doi:10.1109/ROBOT.2006.1642342. ISBN 978-0-7803-9505-3. S2CID 1646994.
  7. ↑ Jayender, J.; Patel, R.V.; Nikumb, S. (2009-09-01). "Robot-assisted Active Catheter Insertion: Algorithms and Experiments". The International Journal of Robotics Research. 28 (9): 1101–1117. doi:10.1177/0278364909103785. ISSN 0278-3649. S2CID 206500027.
  8. ↑ Rus, Daniela; Tolley, Michael T. (May 2015). "Design, fabrication and control of soft robots". Nature. 521 (7553): 467–475. Bibcode:2015Natur.521..467R. doi:10.1038/nature14543. hdl:1721.1/100772. ISSN 1476-4687. PMID 26017446. S2CID 217952627.
  9. ↑ Roesthuis, Roy J.; Janssen, Sander; Misra, Sarthak (November 2013). "On using an array of fiber Bragg grating sensors for closed-loop control of flexible minimally invasive surgical instruments". 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (PDF). Tokyo: IEEE. pp. 2545–2551. doi:10.1109/IROS.2013.6696715. ISBN 978-1-4673-6358-7. S2CID 13551100.
  10. ↑ Ryu, Seok Chang; Dupont, Pierre E. (May 2014). "FBG-based shape sensing tubes for continuum robots". 2014 IEEE International Conference on Robotics and Automation (ICRA). Hong Kong, China: IEEE. pp. 3531–3537. doi:10.1109/ICRA.2014.6907368. ISBN 978-1-4799-3685-4. S2CID 15064329.
  11. 1 2 Jones, Bryan A.; Gray, Ricky L.; Turlapati, Krishna (October 2009). "Three dimensional statics for continuum robotics". 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems. St. Louis, MO, USA: IEEE. pp. 2659–2664. doi:10.1109/IROS.2009.5354199. ISBN 978-1-4244-3803-7. S2CID 17031584.
  12. ↑ Ghafoori, Morteza; Keymasi Khalaji, Ali (2020-12-01). "Modeling and experimental analysis of a multi-rod parallel continuum robot using the Cosserat theory". Robotics and Autonomous Systems. 134: 103650. doi:10.1016/j.robot.2020.103650. ISSN 0921-8890. S2CID 225025768.
  13. ↑ Rao, Priyanka; Peyron, Quentin; Lilge, Sven; Burgner-Kahrs, Jessica (2021). "How to Model Tendon-Driven Continuum Robots and Benchmark Modelling Performance". Frontiers in Robotics and AI. 7: 630245. doi:10.3389/frobt.2020.630245. ISSN 2296-9144. PMC 7885639. PMID 33604355.
  14. ↑ Roesthuis, Roy J.; Misra, Sarthak (April 2016). "Steering of Multisegment Continuum Manipulators Using Rigid-Link Modeling and FBG-Based Shape Sensing". IEEE Transactions on Robotics. 32 (2): 372–382. doi:10.1109/TRO.2016.2527047. ISSN 1552-3098. S2CID 17902850.
  15. ↑ Shi, Chaoyang; Luo, Xiongbiao; Qi, Peng; Li, Tianliang; Song, Shuang; Najdovski, Zoran; Fukuda, Toshio; Ren, Hongliang (August 2017). "Shape Sensing Techniques for Continuum Robots in Minimally Invasive Surgery: A Survey". IEEE Transactions on Biomedical Engineering. 64 (8): 1665–1678. doi:10.1109/TBME.2016.2622361. ISSN 0018-9294. PMID 27810796. S2CID 26514168.
  16. ↑ Dore, Alessio; Smoljkic, Gabrijel; Poorten, Emmanuel Vander; Sette, Mauro; Sloten, Jos Vander; Yang, Guang-Zhong (October 2012). "Catheter navigation based on probabilistic fusion of electromagnetic tracking and physically-based simulation". 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems. Vilamoura-Algarve, Portugal: IEEE. pp. 3806–3811. doi:10.1109/IROS.2012.6386139. ISBN 978-1-4673-1736-8. S2CID 17183408.
  17. ↑ Xu, Ran; Asadian, Ali; Naidu, Anish S.; Patel, Rajni V. (May 2013). "Position control of concentric-tube continuum robots using a modified Jacobian-based approach". 2013 IEEE International Conference on Robotics and Automation. Karlsruhe, Germany: IEEE. pp. 5813–5818. doi:10.1109/ICRA.2013.6631413. ISBN 978-1-4673-5643-5. S2CID 580594.
  18. ↑ Guo, Hao; Ju, Feng; Cao, Yanfei; Qi, Fei; Bai, Dongming; Wang, Yaoyao; Chen, Bai (2019-01-01). "Continuum robot shape estimation using permanent magnets and magnetic sensors". Sensors and Actuators A: Physical. 285: 519–530. doi:10.1016/j.sna.2018.11.030. ISSN 0924-4247. S2CID 117531270.
  19. ↑ Ozel, Selim; Skorina, Erik H.; Luo, Ming; Tao, Weijia; Chen, Fuchen; Yixiao Pan; Onal, Cagdas D. (May 2016). "A composite soft bending actuation module with integrated curvature sensing". 2016 IEEE International Conference on Robotics and Automation (ICRA). Stockholm, Sweden: IEEE. pp. 4963–4968. doi:10.1109/ICRA.2016.7487703. ISBN 978-1-4673-8026-3. S2CID 6366153.
  20. ↑ George Thuruthel, Thomas; Ansari, Yasmin; Falotico, Egidio; Laschi, Cecilia (April 2018). "Control Strategies for Soft Robotic Manipulators: A Survey". Soft Robotics. 5 (2): 149–163. doi:10.1089/soro.2017.0007. hdl:11382/521074. ISSN 2169-5172. PMID 29297756.
  21. ↑ Godage, Isuru S.; Nanayakkara, Thrishantha; Caldwell, Darwin G. (October 2012). "Locomotion with continuum limbs". 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems. Vilamoura-Algarve, Portugal: IEEE. pp. 293–298. doi:10.1109/IROS.2012.6385810. ISBN 978-1-4673-1736-8. S2CID 11689025.
  22. ↑ Buckingham, Rob; Graham, Andrew (2003-09-08). "Snake-Arm Robots – A New Tool for the Aerospace Industry". SAE Technical Paper Series. Warrendale, PA: SAE International. 1. doi:10.4271/2003-01-2952.
  23. ↑ Davies, J.B.C.; Lane, D.M.; Robinson, G.C.; O'Brien, D.J.; Pickett, M.; Sfakiotakis, M.; Deacon, B. (1998). "Subsea applications of continuum robots". Proceedings of 1998 International Symposium on Underwater Technology. Tokyo, Japan: IEEE. pp. 363–369. doi:10.1109/UT.1998.670127. ISBN 978-0-7803-4273-6. S2CID 111200462.
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