Planning in surgery and surgical Planning in surgery and surgical simulationsimulation

Comp 790-058 course presentationComp 790-058 course presentationMert SedefMert Sedef

Planning in robotic radiosurgeryPlanning in robotic radiosurgery

R. Tombropoulos, J.R. Adler, and J.C. Latombe. R. Tombropoulos, J.R. Adler, and J.C. Latombe. CARABEAMER: A CARABEAMER: A Treatment Planner for a Robotic Radiosurgical System with Treatment Planner for a Robotic Radiosurgical System with General Kinematics.General Kinematics. Accepted for publication in Medical Image Accepted for publication in Medical Image Analysis, Oxford University Press, 1998. Analysis, Oxford University Press, 1998. Rhea Tombropoulos et al., Rhea Tombropoulos et al., Treatment Planning for Image-Guided Treatment Planning for Image-Guided Robotic Radiosurgery,Robotic Radiosurgery, Computer Vision, Virtual Reality and Computer Vision, Virtual Reality and Robotics in Medicine, 1997Robotics in Medicine, 1997R.Z. Tombropoulos, J.C. Latombe, and J.R. Adler. R.Z. Tombropoulos, J.C. Latombe, and J.R. Adler. A General A General Algorithm for Beam Selection in Radiosurgery.Algorithm for Beam Selection in Radiosurgery. In Preprints of In Preprints of the IARP Workshop on Medical Robotics, 91--98, Vienna, Austria, the IARP Workshop on Medical Robotics, 91--98, Vienna, Austria, 1996. 1996. A. Schweikard, J.R. Adler, and J.C. Latombe. A. Schweikard, J.R. Adler, and J.C. Latombe. Motion Planning in Motion Planning in Stereotaxic Radiosurgery.Stereotaxic Radiosurgery. IEEE Tr. on Robotics and Automation, IEEE Tr. on Robotics and Automation, 9(6):764--774, 1993 9(6):764--774, 1993

RadiosurgeryRadiosurgery

Non-invasive procedureNon-invasive procedureMoving beam of radiation to Moving beam of radiation to ablate (destroy) brain tumorsablate (destroy) brain tumorsThe problemThe problem is delivering is delivering

EnoughEnough dose of radiation to dose of radiation to the tumor to the tumor to destroydestroy the the tumortumor

MinimumMinimum dose of radiation to dose of radiation to the healthy and dose-sensitive the healthy and dose-sensitive tissue (e.g., brain stem and tissue (e.g., brain stem and optic nerves) optic nerves) not to destroynot to destroy themthem

The solution isThe solution is Crossfiring at the tumor: Crossfiring at the tumor:

several weaker beams from several weaker beams from different directionsdifferent directions

Tombropoulos, Adler, and Latombe, 1998Tombropoulos, Adler, and Latombe, 1998

Treatment planning in radiosurgeryTreatment planning in radiosurgery

Determination of a series of beam Determination of a series of beam configuration (position and orientation)configuration (position and orientation)

Constraints:Constraints: The beams should intersect to form a region The beams should intersect to form a region

of high-dose on the tumorof high-dose on the tumor The dose distribution should match the shape The dose distribution should match the shape

of the tumorof the tumor Healthy or critical tissues should get minimum Healthy or critical tissues should get minimum

or no radiation or no radiation

A treatment planning systemA treatment planning system

6-dof robotic manipulator arm6-dof robotic manipulator arm Positions the radiation sourcePositions the radiation source

Real-time imaging systemReal-time imaging system Monitors patient’s motion Monitors patient’s motion

continuouslycontinuously

A treatment planning algorithmA treatment planning algorithm Allows the surgeon to specify Allows the surgeon to specify

particular region of interest particular region of interest (e.g., tumors, dose-sensitive (e.g., tumors, dose-sensitive tissue) and range of dosetissue) and range of dose

Uses Uses linear programminglinear programming to to optimize the plans and satisfy optimize the plans and satisfy constraintsconstraints

Tombropoulos, Adler, and Latombe, 1998Tombropoulos, Adler, and Latombe, 1998

Steps of the treatment planning Steps of the treatment planning system - 1system - 1

The surgeon specifies The surgeon specifies regions of interest on the regions of interest on the CTs (e.g., the tumor and CTs (e.g., the tumor and critical structures) critical structures)

the system makes a 3D the system makes a 3D reconstruction of the reconstruction of the geometrygeometry

and imposes constraints and imposes constraints on the amount of on the amount of radiation that these radiation that these regions should receive.regions should receive.

Eg., Tumor should get Eg., Tumor should get 2000 rads min and brain 2000 rads min and brain stem should get 500 rads stem should get 500 rads maxmax Tombropoulos, Adler, and Latombe, 1998Tombropoulos, Adler, and Latombe, 1998

Beam selectionBeam selection Target point selection:Target point selection: Evenly space targets on Evenly space targets on

the surface of the 3D tumor model coming from the the surface of the 3D tumor model coming from the CTCT

Source point selection:Source point selection: Select source points Select source points making use of pre-recorded robot configurations. making use of pre-recorded robot configurations. Record the target point and robot configuration.Record the target point and robot configuration.

Path generation:Path generation: Connect all beam configurations Connect all beam configurations into a path such that the robot traverses in a into a path such that the robot traverses in a collision-free path in the environment.collision-free path in the environment.

Steps of the treatment planning Steps of the treatment planning system - 2system - 2

Plan refinementPlan refinement Problem!Problem! Beam selection Beam selection does notdoes not consider consider

the location of critical tissues and the location of critical tissues and does notdoes not guarantee a highly homogeneous dose guarantee a highly homogeneous dose distribution on the tumordistribution on the tumor

Given these constraints, Given these constraints, Linear Linear ProgrammingProgramming adjusts and finds the adjusts and finds the optimal optimal valuesvalues of the dose and diameter of individual of the dose and diameter of individual beams.beams.

Steps of the treatment planning Steps of the treatment planning system - 3system - 3

Plan EvaluationPlan Evaluation The surgeon is provided with the results of The surgeon is provided with the results of

planningplanning3D iso-dose surfaces, dose-volume histograms, 3D iso-dose surfaces, dose-volume histograms, etc.etc.

If the surgeon is not satisfied, planning is If the surgeon is not satisfied, planning is restarted from the desired steprestarted from the desired step

Steps of the treatment planning Steps of the treatment planning system - 4system - 4

Motion planning in maxillofacial Motion planning in maxillofacial robotic surgeryrobotic surgery

Burghart et al., Burghart et al., On-line motion planning On-line motion planning for medical applications, for medical applications, Proceedings of Proceedings of the 24th Annual Conference of the IEEE, the 24th Annual Conference of the IEEE, 19981998

Maxillofacial robotic surgeryMaxillofacial robotic surgery

Maxillofacial surgery: Maxillofacial surgery: Surgery in the maxilla Surgery in the maxilla and face areaand face areaMotion of the surgical Motion of the surgical robot should be planned robot should be planned forfor

Bone cuttingBone cutting

Planned motion shouldPlanned motion should be be safe and adequatesafe and adequate Have Have online capabilitiesonline capabilities

to to react dynamical react dynamical changes changes (i.e. movements (i.e. movements of the patient and surgical of the patient and surgical instruments)instruments)

Planner – Overall conceptPlanner – Overall conceptA volume and surface model of the A volume and surface model of the patient data is constructed beforehandpatient data is constructed beforehandSurgery setup:Surgery setup:

6-dof surgical robot for6-dof surgical robot forBone cutting, hole creating in patient’s Bone cutting, hole creating in patient’s skullskull

Infrared navigation system forInfrared navigation system forDetecting and monitoring the positions Detecting and monitoring the positions of of

Patient’s skull, robot’s tools, surgeons Patient’s skull, robot’s tools, surgeons instrumentsinstruments

Environment modelingEnvironment modeling 3D modeling of the whole environment 3D modeling of the whole environment

including patient data and surgical including patient data and surgical tools and screws attached to the skulltools and screws attached to the skull

Online collision-free motion planning Online collision-free motion planning for the 6-dof robotfor the 6-dof robot

The planner reacts according to the The planner reacts according to the current state of the environmentcurrent state of the environment

Burghart et al., 1999Burghart et al., 1999

Planner – Environment modeling Planner – Environment modeling

Convex hull of the surgical wound, patient, and the Convex hull of the surgical wound, patient, and the hooks are generated at different levels dynamicallyhooks are generated at different levels dynamically

Burghart et al., 1999Burghart et al., 1999

Planner – Robot motion planningPlanner – Robot motion planning

The planner searches a The planner searches a solution in the implicit robot solution in the implicit robot joint-value space (c-space) joint-value space (c-space) and checks for collisions in the and checks for collisions in the workspace (environment workspace (environment space)space)C-space: A* search algorithmC-space: A* search algorithm

A*: a graph-tree search A*: a graph-tree search algorithm. Example of best-algorithm. Example of best-search algorithmsearch algorithm

Eg. Depth-first, breath-first, Eg. Depth-first, breath-first, djkstradjkstra

Collision-detection by distance Collision-detection by distance computation in the workspacecomputation in the workspace

Burghart et al., 1999Burghart et al., 1999

Motion planning for performance Motion planning for performance assessment in Minimally Invasive assessment in Minimally Invasive

SurgerySurgery

Haniffa et al., Haniffa et al., Motion Planning System Motion Planning System for Minimally Invasive Surgery,for Minimally Invasive Surgery, 14th 14th Annual IEEE International Conference and Annual IEEE International Conference and Workshops on the Engineering of Workshops on the Engineering of Computer-Based Systems (ECBS'07),  pp. Computer-Based Systems (ECBS'07),  pp. 609-610, 2007 609-610, 2007

Minimally invasive surgery – Minimally invasive surgery – laparoscopic surgerylaparoscopic surgery

monitor

surgeon

laparoscopicinstruments

Basdogan, Ho, and Sirinivasan, 2001Basdogan, Ho, and Sirinivasan, 2001

A box-training system mimicking A box-training system mimicking MIS settingsMIS settings

An enclosure (box) with openings for surgical An enclosure (box) with openings for surgical instrumentsinstruments Surgical tasks are performed within the boxSurgical tasks are performed within the box

Surgical instruments are mounted with motion Surgical instruments are mounted with motion sensorssensors The maneuvers of the trainee are recorded during the The maneuvers of the trainee are recorded during the

performanceperformance

Given the task, the optimal traverse of the Given the task, the optimal traverse of the surgical tools calculatedsurgical tools calculated The optimal traverse is compared with the maneuvers The optimal traverse is compared with the maneuvers

of the trainee for performance assessmentof the trainee for performance assessment

Motion planning for replacement of Motion planning for replacement of a rubber band across two hooksa rubber band across two hooks

Potential field methodPotential field method Instrument tips = point Instrument tips = point

robots in c-spacerobots in c-space Artificial potential field: Artificial potential field:

attractive towards the attractive towards the goal state and goal state and repulsive towards repulsive towards forbidden regions forbidden regions

Haniffa et al., 2007Haniffa et al., 2007

Performance AssessmentPerformance Assessment

Steepest descent pathSteepest descent path Movement towards Movement towards

steepest descent earns steepest descent earns creditcredit

Movement towards Movement towards obstacles imposes obstacles imposes penaltiespenalties

AlsoAlso Completion timeCompletion time # of collisions with # of collisions with

obstacles and obstacles and instrumentsinstruments

# of c-space violations# of c-space violations

Haniffa et al., 2007Haniffa et al., 2007

Planning for needle insertionPlanning for needle insertion

Alterovitz, R et al., Alterovitz, R et al., ““Sensorless planning for medical needle Sensorless planning for medical needle insertion procedures,”insertion procedures,” IEEE/RSJ International Conference on IEEE/RSJ International Conference on Intelligent Robots and Systems, volume 4, pp. 3337 – 3343, 2003.Intelligent Robots and Systems, volume 4, pp. 3337 – 3343, 2003.R. Alterovitz, K. Goldberg, and A. Okamura, R. Alterovitz, K. Goldberg, and A. Okamura, "Planning for "Planning for steerable bevel-tip needle insertion through 2D soft tissue with steerable bevel-tip needle insertion through 2D soft tissue with obstacles,"obstacles," in Proc. IEEE Int. Conf. on Robotics and Automation, in Proc. IEEE Int. Conf. on Robotics and Automation, Apr. 2005, pp. 1652--1657 Apr. 2005, pp. 1652--1657 Alterovitz, R et al., Alterovitz, R et al., “Steering flexible needles under Markov “Steering flexible needles under Markov motion uncertainty,”motion uncertainty,” IEEE/RSJ International Conference on IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1570- 1575, 2005. Intelligent Robots and Systems, pp. 1570- 1575, 2005. R. Alterovitz, M. Branicky, and K. Goldberg, R. Alterovitz, M. Branicky, and K. Goldberg, "Constant-Curvature "Constant-Curvature Motion Planning Under Uncertainty with Applications in Image-Motion Planning Under Uncertainty with Applications in Image-Guided Medical Needle Steering,"Guided Medical Needle Steering," in Proc. Workshop on the in Proc. Workshop on the Algorithmic Foundations of Robotics, July 2006. Algorithmic Foundations of Robotics, July 2006.

Needle insertionNeedle insertion

Medical applications:Medical applications: Brachytheraphy (seed implantation):Brachytheraphy (seed implantation):

radiotherapy in which the source of radiation radiotherapy in which the source of radiation is placed (as by implantation) in or close to is placed (as by implantation) in or close to the area being treatedthe area being treated

BiopsiesBiopsies:: the removal and examination of the removal and examination of tissue, cells, or fluids from the living bodytissue, cells, or fluids from the living body

Treatment injections:Treatment injections: inserting a needle to a inserting a needle to a specific target location inside the body to specific target location inside the body to inject a druginject a drug

Needle insertionNeedle insertion

AimAim needle tip should be needle tip should be as as

close as possibleclose as possible to an to an internal target when the internal target when the procedure is performed procedure is performed

ChallengeChallenge needle insertion causes needle insertion causes

soft tissues to soft tissues to displace displace and deformand deform

difficult or impossible to difficult or impossible to obtain precise imaging obtain precise imaging data during insertion data during insertion

Incorrect placement of a Incorrect placement of a radioactive seed radioactive seed cannot cannot treat tumortreat tumor and and can can damage healthy tissuedamage healthy tissue

Alterovitz et al., 2003Alterovitz et al., 2003

Ultrasound images of human prostate

Sensorless planning algorithm for Sensorless planning algorithm for radioactive seed implantation – rigid needleradioactive seed implantation – rigid needle

The system computes needle The system computes needle insertion offsets that insertion offsets that compensatecompensate for for tissue tissue deformationsdeformations. It. It

uses uses 2D FEM model2D FEM model (simulation) of the soft tissues (simulation) of the soft tissues surrounding the target implant surrounding the target implant location (hence sensorless!)location (hence sensorless!)

performs performs dynamic simulationdynamic simulation of needle insertion to compute of needle insertion to compute tissue deformations tissue deformations

iteratively testsiteratively tests different different insertion locations and depths insertion locations and depths to compute the optimal needle to compute the optimal needle offset offset

Alterovitz et al., 2003Alterovitz et al., 2003

Problem definitionProblem definition

Deformable body in 2D plane, Deformable body in 2D plane, attached target at Ptattached target at PtInsert needle from a specified Insert needle from a specified height until a specified depth, height until a specified depth, PrPrRelease seed at that depth = Release seed at that depth = PrPrRetract needleRetract needleFinal actual seed position = PaFinal actual seed position = PaNote that Pa≠Pr due to tissue Note that Pa≠Pr due to tissue defrmationdefrmationError = ||Pa-Pt|| Error = ||Pa-Pt||

Alterovitz et al., 2003Alterovitz et al., 2003

PlanningPlanningFor a target, define a set of For a target, define a set of insertion heights and insertion insertion heights and insertion depths. A virtual 2D grid consisting depths. A virtual 2D grid consisting of different (height, depth) couplesof different (height, depth) couplesInset needle with constant velocity Inset needle with constant velocity from a chosen height up to a from a chosen height up to a chosen depthchosen depthDeform 2D model with FEMDeform 2D model with FEMRelease seed, retract needle, wait Release seed, retract needle, wait for the model to stop its dynamic for the model to stop its dynamic deformationsdeformationsCalculate error between the final Calculate error between the final position of the seed and target for position of the seed and target for that (height, depth) couplethat (height, depth) couple(height, depth) couple with (height, depth) couple with minimum error is the solutionminimum error is the solution

Alterovitz et al., 2003Alterovitz et al., 2003

Steering Flexible Needles Under Steering Flexible Needles Under Markov Motion UncertaintyMarkov Motion Uncertainty

Unlike rigid needles, flexible bevel tip Unlike rigid needles, flexible bevel tip needles can be steered needles can be steered aroundaround obstacles. obstacles. Flexible needles with Flexible needles with bevel tipsbevel tips follow a follow a path of path of constant curvatureconstant curvature in the in the direction of the bevel. direction of the bevel. Controlling 2 DOF at the needle base Controlling 2 DOF at the needle base (rotation or bevel direction and insertion (rotation or bevel direction and insertion distance), the needle can be steered distance), the needle can be steered around obstacles to around obstacles to reachreach targets targets inaccessibleinaccessible to rigid needles. to rigid needles. Planning motion for such a needle is Planning motion for such a needle is difficult due to difficult due to uncertaintyuncertainty constraintsconstraints, , i.e. uncertainty ini.e. uncertainty in

tissue propertiestissue properties needle mechanicsneedle mechanics interaction forces.interaction forces.

Alterovitz, Goldberg, Okamura 2005Alterovitz, Goldberg, Okamura 2005

Alterovitz et al., 2005Alterovitz et al., 2005

Motion planning for flexible bevel Motion planning for flexible bevel tip needles in uncertaintytip needles in uncertainty

Motion planning problem as a Motion planning problem as a Markov Decision Markov Decision Process (MDP)Process (MDP) based on based on Dynamic ProgrammingDynamic ProgrammingThe plannerThe planner

Computes discrete control sequence of insertion & direction Computes discrete control sequence of insertion & direction changes in needlechanges in needle

Minimizes expected cost (tissue deformation & damage) due to Minimizes expected cost (tissue deformation & damage) due to insertion distanceinsertion distancedirection changesdirection changesobstacle collisionsobstacle collisions

ConsidersConsiders Deterministic case (needle response to controls known)Deterministic case (needle response to controls known) Uncertain case (probability distribution of needle response Uncertain case (probability distribution of needle response

known)known)

Problem definitionProblem definition

NeedleNeedle Flexible & bevel tip Flexible & bevel tip Stiff soft tissue relative to needleStiff soft tissue relative to needle Rotation, i.e. bevel direction (right or left, 180 degree turn)Rotation, i.e. bevel direction (right or left, 180 degree turn) Insertion only (no retraction)Insertion only (no retraction)

2D rectangular workspace2D rectangular workspace Specified by segmenting 2D cross-section of patient anatomy via MRI or Specified by segmenting 2D cross-section of patient anatomy via MRI or

ultrasoundultrasoundTwo actions for needle:Two actions for needle:

Insert a distance (constant velocity)Insert a distance (constant velocity) Change direction (180 degree turn, no insertion) and insert a distance (constant Change direction (180 degree turn, no insertion) and insert a distance (constant

velocity)velocity)Needle movement damages tissueNeedle movement damages tissue

Cost for both insertion and rotation (occurs as long as needle moves)Cost for both insertion and rotation (occurs as long as needle moves) Prohibitive cost for obstacle colliisionProhibitive cost for obstacle colliision

Planning goal: Find a set of discrete needle controls to reach a target from a Planning goal: Find a set of discrete needle controls to reach a target from a starting point with minimum coststarting point with minimum cost

Alterovitz et al., 2005Alterovitz et al., 2005

Problem formulationProblem formulationDynamic programming requires discrete stateDynamic programming requires discrete stateDiscrete state space:Discrete state space:

2D workspace discretized as a grid2D workspace discretized as a grid Needle movement is discretized based onNeedle movement is discretized based on

Needle tip positionNeedle tip positionRotationRotationControl circle variablesControl circle variables

These two states are mergedThese two states are merged

State transitions:State transitions: DeterministicDeterministic: Next state calculated using current : Next state calculated using current

state values. P = 1state values. P = 1 UncertainUncertain: Uncertainty due to tissue inhomogeneity: Uncertainty due to tissue inhomogeneity

80% deterministic80% deterministic10% needle tip deviates from input orientation by some 10% needle tip deviates from input orientation by some positive amountpositive amount10% needle tip deviates from input orientation by some 10% needle tip deviates from input orientation by some negative amountnegative amount

Cost function based onCost function based on Amount of path traversed from current to next stateAmount of path traversed from current to next state If next state is the target – C = 0If next state is the target – C = 0 If next state collides with any obstacle – C = high, If next state collides with any obstacle – C = high,

terminateterminate

Total cost: Expected value of sum of state Total cost: Expected value of sum of state transition coststransition costs

Alterovitz et al., 2005Alterovitz et al., 2005

Motion planning optimizationMotion planning optimization

Compute a sequence of controls that minimizes total expected cost of Compute a sequence of controls that minimizes total expected cost of needle insertionneedle insertion

Stochastic shortest path problemStochastic shortest path problem Solved using infinite horizon dynamic programmingSolved using infinite horizon dynamic programming

Path planning of catheters in Liver Path planning of catheters in Liver ChemoembolizationChemoembolization

Gayle et al., Gayle et al., Path Planning for Path Planning for Deformable Robots in Complex Deformable Robots in Complex Environments,Environments, Proceedings of  Robotics: Proceedings of  Robotics: Systems and Science, 2005 Systems and Science, 2005

Liver ChemoembolizationLiver Chemoembolization

Under x-ray guidance, catheter Under x-ray guidance, catheter (tube-like cylinder) is inserted (tube-like cylinder) is inserted into femoral artery and into femoral artery and advanced through a set of advanced through a set of arteries to reach near the arteries to reach near the tumortumorWhen reached to the artery When reached to the artery supplying a tumor, it injects supplying a tumor, it injects chemotherapy drugschemotherapy drugsCareful catheter manipulation Careful catheter manipulation is is criticalcritical::

SpasmsSpasms in small vessels in small vessels RefluxReflux of chemoembolization of chemoembolization

into other arteriesinto other arteries due to size due to size similaritysimilarity

Gayle et al., 2005Gayle et al., 2005

A constrained-based planning A constrained-based planning algorithm algorithm

Constrained dynamic simulationConstrained dynamic simulation Motion planning = solving a list of constraintsMotion planning = solving a list of constraints

Geometric constraintsGeometric constraints Obstacle avoidance, non-penetrationObstacle avoidance, non-penetration

Physical constraintsPhysical constraints Volume preservation, energy minimizationVolume preservation, energy minimization

Catheter is designed as a Catheter is designed as a deformabledeformable robot robot Deformation model = mass-spring systemDeformation model = mass-spring system

Planning problem: finding sequential robot configurations such thatPlanning problem: finding sequential robot configurations such that No configuration intersects any obstacleNo configuration intersects any obstacle All configurations satisfy constraints and minimize the energy of the All configurations satisfy constraints and minimize the energy of the

systemsystemConstraintsConstraints

Hard: Hard: non-penetration (collision detection and response)non-penetration (collision detection and response)

Soft:Soft:Goal-seeking (initial path = medial axis)Goal-seeking (initial path = medial axis)Volume preservationVolume preservationObstacle avoidanceObstacle avoidance

Energy minimizationEnergy minimization

A constrained-based planning A constrained-based planning algorithm algorithm

A constrained-based planning A constrained-based planning algorithmalgorithm

Update robot state given the constraints, Fc constraint Update robot state given the constraints, Fc constraint force, Fe external forceforce, Fe external force

Check if constraints are satisfied subject to energy Check if constraints are satisfied subject to energy minimizationminimizationIf not:If not:

Set the last valid milestone as the next destinationSet the last valid milestone as the next destination Back trace one step on the current roadmapBack trace one step on the current roadmap Find a new path from the last valid milestone to the goal Find a new path from the last valid milestone to the goal

configurationconfiguration Compute new constraint forces and solve the ODE, using the Compute new constraint forces and solve the ODE, using the

previous state of the robot R and Feprevious state of the robot R and Fe Set the next robot state to be the new ODE solutionSet the next robot state to be the new ODE solution

A constrained-based planning A constrained-based planning algorithm - demoalgorithm - demo

Additional referencesAdditional references

Basdogan, C., Ho, C., Srinivasan, M.A., Basdogan, C., Ho, C., Srinivasan, M.A., 2001, "Virtual Environments for Medical 2001, "Virtual Environments for Medical Training: Graphical and Haptic Simulation Training: Graphical and Haptic Simulation of Common Bile Duct Exploration", of Common Bile Duct Exploration", accepted to the IEEE/ASME Transactions accepted to the IEEE/ASME Transactions on Mechatronics on Mechatronics