1. Medical Robots History; From Modeling through
Path planning to Motion Control
Survey Paper for Dr. IBRAHIM ABU-HIBA
SHADI N. ALBARQOUNI
Electrical and Computer Engineering Department
The Islamic University of Gaza
Gaza, Palestine
sbaraqouni@iugaza.edu
ABSTRACT
This survey paper illustrates the modeling of a
medical robots used in surgical operations with six
degree of freedom (DOF). The paper describes the
difficulties in the inability of a robot to “plan its path”
through a predefined 3D environment, and how to
build its local maps. Several algorithms will be
mentioned here in this paper either for planning
algorithms or mapping. This paper will be submitted
as a requirement for Robot Modeling and Control
course in the Graduate Degree1
I. INTRODUCTION
The emerging of intelligent medical robots research
has shown rapid development in recent years and
offers a great number of researches in this field. The
medical robots can be considered as a stationary
robot, moving it in the desired position then applying
the desired trajectories in order to do the surgical
operation.
The most common medical robot used in
hospitals is the articulated manipulator connected by
spherical wrist and medical tool as an end-effector;
such as RONAF described in (1)
The main problem in these robots is the accuracy
in path planning and trajectories. Almost of these
robots used the geometric of a robot manipulator
1
This course is taught and supervised by Dr.
IBRAHIM ABU-HIBA, the Associated Professor in
ECE Department, The Islamic University of Gaza,
Gaza, Palestine.
with rotational 6 DOF in 3D environment with static
obstacles and the cut trajectories as specified in (2).
The result will be a collision free path trajectory.
Several algorithms and approaches have been
developed for path planning problem either for
mobile robots or stationary robots as will shown
later.
My goal is to show the differences, advantages and
disadvantages of them, then try to create a new
algorithm or approach in order to improve their
performance.
This paper is organized as follows: section 2 gives
some details about the requirements of a surgical
operation. Section 3 talks about the modeling of
medical robot and the geometric approach. Section 4
handles the path planning problems and the generated
algorithms. The paper closes with conclusion and
future work in section 5.
II. REQUIREMNTS FOR SURGICAL
OPERATIONS
The safety of patient plays the main role in the
requirements of any surgical operation, so the
accuracy in positioning the medical robot is the first
step towards the safety.
To achieve the safety of patient, (2) defined the
following criterions whish are to be met in every
point in the operations:
• The point is reachable
• There are no collisions

2.  Medical Robots History; From Modeling through Path planning to Motion Control
• The robot joints are near to their middle positions
• The trajectory is oriented upwardly
The first and second criterions are obtained from
path planning problems, the rest of criterions are
optional.
III. MODELING
I have considered the medical robot described in (2)
as a case study of path planning and motion control,
the medical robot with 6 DOF consists of segments
that connected by joints. These joints are actuated be
servomotors.
Gathering information about the patient
environment is required in order to model his 3D
environment and the obstacles locations. This
information could be obtained using several tools and
medical instruments such as ultrasound imaging (US),
computer tomography (CT) and the magnetic
resonance imaging (MRI).
The previous tools and instruments could be used
in guided surgery as described in (3) and (4), which
handles the Computer-Integrated Surgery (CIS)
system, its architecture shown in Figure 1.
Figure 2: Medical Robot Manipulator
According to the Deneavit-Hartenberg conventions
for sex links manipulator “left upper arm” have to
define seven coordinates system.
The DH Transformation matrices in (2) which
described the previous medical robot RX90 had
mistakes according to some faults in their DH table
or in computing the transformation matrices.
Table 1: DH Parameters
Link
a
d
α
1
0
0
-90
2
3
Figure 1: CIS System's Archticture
0
0
4
90
-90
5
0
6
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0
0
0
90
0
θ

3.  Medical Robots History; From Modeling through Path planning to Motion Control
The correct DH parameters for the previous
robot are shown in Table 1, Transformation Matrices
show relative positioning of links, which allows to
make inverse kinematic of the robot before surgical
operation, I found theses matrices by using
Mathematica® which different than in (2) according
to the mistake in DH parameters.
Where
The other angles could be found using Euler angle
representation for the spherical wrist described in (5)
IV. PATH PLANNING ALGORITHMS
Several approaches have been developed to solve the
path planning problems for manipulators. Most of
them used the Probabilistic Roadmap Method which
used A*-search algorithm described in (5) and (6).
Where
Using the previous
matrices and the geometric
approach for the medical robot described in (2) and
rewritten here, the inverse kinematic could be found
easily.
Figure 3: Left arm configuration
This algorithm distributes several random nodes in
the environment uniformly, then constructing a
collision free path between two nodes or
configurations. The local planner finds the shortest
path from the start to goal configurations through
simple network.
Figure 4: PRM Algorithm
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4.  Medical Robots History; From Modeling through Path planning to Motion Control
The PRM algorithm works in two phases; offline
and online phases. In the offline phase; a roadmap
graph is generated for the area of interests which
obtained from imaging systems (3), the obstacles are
defined and labeled in the 3D map by OBBTreealgorithm mentioned in (6) Then the local planner
tries to find the shortest and a collision free path in
order to reach the goal position with respect to the
kinematic and dynamic constraints of the medical
robot.
The previous algorithm has several problems or
disadvantages such as the disjoints segments (5) and
the non-holonomic robots which can’t move sharp
angles suddenly and should turn it through splinecurves.
Currently, many improvement and enhancements
are added to PRM algorithm such as adding several
random nodes to the roadmap mentioned in (5) and
Path smoothing using cubic functions in order to
solve the non-holonomic motions. The author of (6)
created two modifications to the PRM algorithm
which have been incorporated in the planner; the first
modification is the Multi-level non-holonomic
roadmap planning, which solves a relaxed problem
using only the holonomic constraints, and then refines
the solution by adding non-holonomic constraints one
at a time. The second modification called the Delayed
Constraint Handling which deals with constraints that
are not known during roadmap construction such as
moving objects or obstacles.
The previous modifications for PRM are effective
for non-holonomic robots and mobile robots, in our
case of medical robot the obstacles are static, and so
we didn’t need these modifications.
The second algorithm will be discussed here for
path planning is the idea of using Fuzzy Logic with the
distance transform method in order to improve it.
The distance transform method in (7) is the most
popular one and very effective for either known or
unknown field topologies.
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The problem with this transform is that it does not
use any speed inference technique and thus the robot
cannot modify its speed in terms of the obstacles
present in its path.
The algorithm which described in (7) determines
the velocity of robot or the trajectory plan using the
fuzzy logic. The input of the fuzzy system is the
number of free blocks in front of it called clearbox,
which passed to the inference called Mamdani’s
Method, the output fuzzy sets will be high, low, and
medium speeds as shown in Figure 5 and Figure 6.
Figure 5: Fuzzy Sets of clearbox variable
Figure 6: Fuzzy Sets of speed variable

5.  Medical Robots History; From Modeling through Path planning to Motion Control
They described in (7) the rule base of fuzzy
implementation; they mentioned some rules such as:
•
•
•
If clearbox is high then speed is high
If clearbox is medium then speed is medium
If clearbox is low then speed is low
Their design demonstrated its efficiency, minimizing
the distance travelled towards the goal and varying
the speed contextually.
The last algorithm will be mentioned here is one
described in (1), which assumed that the patient’s
environment is unknown, and one of the robot’s
tasks is building the map and therefore the path in
order to do the surgical operation. They used several
sensors such as audio and ultrasound sensors.
V. MOTION CONTROL
When the
Bibliography
1. Building Local Maps in Surgical Robotics. Philipp J.
STOLKA and Dominik HENRICH. Universität
Bayreuth, D-95447 Bayreuth, Germany : s.n., 2005.
Lehrstuhl für Angewandte Informatik III, (Robotik und
Eingebettete Systeme).
2. Simulation for Path Planning and Motion Control for
Medical Robot. L. E. Gonchar, D. Engel, J.
Raczkowsky, and H. Wörn. Karlsruhe, Germany :
s.n., 2000. Workshop on Computer Science and
Information Technologies CSIT'99.
3. Computers in Imaging and Guided Surgery. LEO
JOSKOWICZ and RUSSELL H. TAYLOR. The
Hebrew University of Jerusalem, The Johns Hopkins
University : IEEE, SEPTEMBER/OCTOBER 2001, pp.
65-72. 1521-9615.
4. Spong, Hutchinson, Vidyasagar. Robot
Modeling and Control. s.l. : Wiley, 2006. pp. 98-108.
ISBN: 0-471-64990-2.
5. Probabilistic Roadmap Based Path Planning for an
Autonomous Unmanned. Per Olof Pettersson and
Patrick Doherty. Linkoping University, 581 83
Linkoping, Sweden : s.n., 2005.
6. Path Planning for Mobile Robots using Fuzzy Logic.
Indranil Gupta and Denis Riordan. Dalhousie
University, Dalhousie, Canada : s.n., 2003.
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