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Continuum Robots and Tactile Sen-
sors
Preface
In Load Cells and Force Sensors in Robotics, we introduced recent advancements in
robotics technology through the use of load cells and force transducers. Here we
continue by looking at a specific example of robotics that employs these sensors:
continuum robots.
What is a Continuum Robot?
A continuum robot is a continuously curving manipulator like the arm of an octopus
or the trunk of an elephant but at a much smaller scale.
It is an invertebrate in that it is incredibly maneuverable in navigating restricted
spaces without doing damage to the passage walls, materials, or tissue. It may have
devices attached to the head of the manipulator, such as a camera, grasping claw,
or a cutting mechanism.[1] Some typical applications of a continuum robot are:
Robotic surgery procedures such as endoscopy, colonoscopy, or neurosurgery;
2. Hazardous operations such as bomb disposal or search and rescue at natural disas-
ter sites;
Actions in hazardous atmospheres such as a nuclear site;
Maintenance applications in industrial situations where space is restricted;
Transport mechanisms for inserting embedded sensors such as load sensors, force
sensors, or tactile sensors.
Tactile Sensors vs. Other Force Transducers
Load cells/strain gauge sensors, piezoelectric force sensors, and tactile sensors
have much in common.
All of these sensors perform the same function of measuring force. However, they
differ in the types of applications where they are suited.
The following sections quickly explain the functions of different sensors and their ap-
plications in robotics.
Load Cells/Strain Gauge Sensors
Load cells/strain gauge sensors measure force primarily through the use of strain
gauges. The strain gauge is connected to a printed circuit board (PCB) that converts
a mechanical force into a proportional electrical signal.
Load cell sensors are the most mature technology for the measurement of force;
however they have disadvantages as applications advance.
They are larger and bulkier than other methods of measuring force, as you can see
by its construction shown in Figure 1. Their size makes them impractical in contin-
uum robot applications, which are generally micro-scale.
Figure 1: A Disk Configuration Load Cell
Piezoelectric Force Sensors
3. Figure 2: Piezoelectric Force Sensor
A piezoelectric force sensor (Figure 2) responds to forces through a direct electri-
cal process that eliminates the cost of designing a PCB as part of the device. It’s
also smaller for applications like embedded sensors in a manufacturing production
line using industrial robots.
The technology is a piezoelectric process using compression of the device to gener-
ate a proportional electrical signal based on the force applied and the change in the
resistance of the sensor. It’s also a very mature technology and has the advantage
of being thin, lightweight, and requires no excitation voltage. (See Comparing Strain
Gauges to Piezoelectric Sensors.)
Tactile Sensors
There are many types of tactile sensors. Of these, the force/torque variety is the
one we will compare with the other sensors we’ve discussed. A tactile force/torque
sensor is a much more sophisticated device than the load cell/strain gauge sensor
and piezoelectric force sensor discussed so far.
Whereas all are force transducers, the tactile sensor must also be able to measure
rotational moments of force (torque) for use in a continuum robot. The objects this
robot handles are easily damaged by excessive force or twisting beyond their capac-
ity to return to their natural shape once released. [2]
These sensors are yet to have the manipulation skills of a human hand; they are far
more primitive. This manipulation technology is still in its infancy, and the control al-
gorithms don’t exist. For now, designers must know the geometry of the grasped ob-
ject to estimate the constraints that must be put on factors like torsional moments.
Advances in Tactile Sensor Design
The gaps in the technological maturity of tactile sensor design are being addressed
via the development of machine learning algorithms. These algorithms gather data
on the shape, weight, stiffness and friction coefficient of the objects/surfaces the con-
tinuum robot encounters.
From this data, the learning algorithms produce a tactile (physical) map of these ob-
jects/surfaces. This physical map then trains the robot to exert the appropriate force
and torque on these objects as it manipulates them.
4. The sensor consists of three components. A PCB (Figure 3) that collects, stores, and
analyzes the data and a rigid grid that holds a deformable cap (Figure 4) that gener-
ates the data.
(bottom)
(top)
(detail)
Figure 3: Tactile Sensor PCB
Figures 3 and 4 [2] show a miniature force/tactile sensor design that can measure
distributed contacts and estimate contact force and torsional moments. It is to be
used for robotic dexterous manipulation tasks. The mechanical interface of the de-
vice is a soft pad of silicone that can adapt to different object shapes and hold high
torsional moments.
5. The sensitive part, the PCB, is based on optoelectronic technology. This technology
not only estimates the total contact wrench, but also detects the orientation of the
contact surface. This is essential to correctly identify friction force, a relevant quantity
in any dexterous manipulation control algorithm.
Figure 4: Tactile Sensor Deformable Cap
Conclusion
The design of the advanced tactile sensor presented is only one of several being ex-
plored for use in continuum robots, and the force/torque sensor is only one of several
types of sensors that will be developed.
However none of these technologies would be possible without their technological
origins in basic load cells. Evolving technologies such as robotics have driven, and
will continue to drive, advancements in force sensing instruments for many years.
References
[1] Costanzo, Marco (2019), Design and Calibration of a Force/Tactile Sensor for
Dexterous Manipulation, Sensors 19(4), pp 966,
[2] Mahvash, Mohsen (2011), Stiffness Control of Surgical Continuum Manipula-
tors, IEEE Transactions on Robotics pp 334-346, DOI 10.1109/TRO.2011.2105410