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RoboticRadiotherapy:An optimal,
adaptivecontrol
approach.
OlalekanOgunmolu
PublicationsUntil Now
Research Overview
Treatment Types
Frame-based RT
Cyberknife
Drawbacks
BOO
Overview
Search
Fluence MapOptimization
Results
Solution
Solution:Soft-Robot PositionCorrecting Systems
Experiment Setup
Kalman Filtering
Sensor Fusion
SystemIdentification
3DOFControl
Vision-based HeadPose Estimation
AdaptiveNeuroControl
ControlContributions
Neural NetworkModel
Control gains
Results
References
Robotic Radiotherapy: An optimal,adaptive control approach.
Olalekan Ogunmolu
Department of Electrical Engineering
The University of Texas at Dallas, Richardson, TX
Department of Radiation OncologyUniversity of Texas Southwestern Medical Center, Dallas, TX
January 10, 2019
Olalekan OgunmoluRobotic Radiotherapy: An optimal, adaptive control approach.
RoboticRadiotherapy:An optimal,
adaptivecontrol
approach.
OlalekanOgunmolu
PublicationsUntil Now
Research Overview
Treatment Types
Frame-based RT
Cyberknife
Drawbacks
BOO
Overview
Search
Fluence MapOptimization
Results
Solution
Solution:Soft-Robot PositionCorrecting Systems
Experiment Setup
Kalman Filtering
Sensor Fusion
SystemIdentification
3DOFControl
Vision-based HeadPose Estimation
AdaptiveNeuroControl
ControlContributions
Neural NetworkModel
Control gains
Results
References
Introduction
This page is left blank intentionally.
Olalekan OgunmoluRobotic Radiotherapy: An optimal, adaptive control approach.
RoboticRadiotherapy:An optimal,
adaptivecontrol
approach.
OlalekanOgunmolu
PublicationsUntil Now
Research Overview
Treatment Types
Frame-based RT
Cyberknife
Drawbacks
BOO
Overview
Search
Fluence MapOptimization
Results
Solution
Solution:Soft-Robot PositionCorrecting Systems
Experiment Setup
Kalman Filtering
Sensor Fusion
SystemIdentification
3DOFControl
Vision-based HeadPose Estimation
AdaptiveNeuroControl
ControlContributions
Neural NetworkModel
Control gains
Results
References
Publications Until Now
Publications Under reviewOlalekan Ogunmolu, Xuejun Gu, Steve Jiang, Nicholas Gans. Nonlinear SystemsIdentification Using Deep Dynamic Neural Networks”. Under review at Physical ReviewApplied, American Physical Society, Submitted September 2018.
Olalekan Ogunmolu, Michael Folkerts, Dan Nguyen, Nicholas Gans, Steve Jiang. DeepBOO 2.0: End-to-End Training of Beam Orientation Selection Policies for IntensityModulated Radiation Therapy. Under publication invitation review at International Journalof Robotics Research (IJRR), 2018.
Accepted PublicationsOlalekan Ogunmolu, Michael Folkerts, Dan Nguyen, Nicholas Gans, and Steve Jiang. DeepBOO: Automating Beam Orientation Selection in Intensity Modulated Radiation Therapy.To appear at The 13th International Workshop on the Algorithmic Foundations of Robotics(WAFR), Merida, Mexico. December 2018.
Olalekan Ogunmolu, Nicholas Gans, Tyler Summers. Minimax Iterative Dynamic Game:Application to Nonlinear Robot Control Tasks. IEEE/RSJ International Conference onIntelligent Robots and Systems (IROS), Madrid, Spain. October 2018.
Olalekan Ogunmolu, Dan Nguyen, Xun Jia, Weiguo Lu, Nicholas Gans, and Steve Jiang.Automating Beam Orientation Optimization for IMRT Treatment Planning: A DeepReinforcement Learning Approach. Selected for Oral Presentation at the John R. CameronYoung Investigators Symposium – 60th Annual Meeting of the American Association ofPhysicists in Medicine, Nashville, TN (AAPM). July 2018.
Olalekan OgunmoluRobotic Radiotherapy: An optimal, adaptive control approach.
RoboticRadiotherapy:An optimal,
adaptivecontrol
approach.
OlalekanOgunmolu
PublicationsUntil Now
Research Overview
Treatment Types
Frame-based RT
Cyberknife
Drawbacks
BOO
Overview
Search
Fluence MapOptimization
Results
Solution
Solution:Soft-Robot PositionCorrecting Systems
Experiment Setup
Kalman Filtering
Sensor Fusion
SystemIdentification
3DOFControl
Vision-based HeadPose Estimation
AdaptiveNeuroControl
ControlContributions
Neural NetworkModel
Control gains
Results
References
Publications Until Now
Accepted Publications (Cont’d)Yara Almubarak, Joshi Aniket, Olalekan Ogunmolu, Xuejun Gu, Steve Jiang, Nicholas Gans,and Yonas Tadesse, Design and Development of Soft Robots for Head and Neck CancerRadiotherapy. SPIE: Smart Structures + Nondestructive Evaluation, (SPIE), Denver, CO,U.S.A. March 2018.
Olalekan Ogunmolu, Adwait Kulkarn, Yonas Tadesse, Xuejun Gu, Steve Jiang, and NicholasGans. Soft-NeuroAdapt: A 3-DOF Neuro-Adaptive Pose Correction System For Framelessand Maskless Cancer Radiotherapy. IEEE/RSJ International Conference on IntelligentRobots and Systems (IROS), Vancouver, BC, Canada. September 2017. DOI:10.1109/IROS.2017.8206211.
Olalekan Ogunmolu, Xuejun Gu, Steve Jiang, and Nicholas Gans. Vision-based control of asoft-robot for Maskless Cancer Radiotherapy. IEEE Conference on Automation Science andEngineering (CASE), Fort-Worth, Texas, August 2016. DOI: 10.1109/CoASE.2016.7743378.
Olalekan Ogunmolu, Xuejun Gu, Steve Jiang, and Nicholas Gans. A Real-TimeSoft-Robotic Patient Positioning System for Maskless Head-and-Neck Cancer Radiotherapy.IEEE Conference on Automation Science and Engineering (CASE), Gothenburg, Sweden,August 2015. DOI: 10.1109/CoASE.2015.7294318.
Olalekan OgunmoluRobotic Radiotherapy: An optimal, adaptive control approach.
RoboticRadiotherapy:An optimal,
adaptivecontrol
approach.
OlalekanOgunmolu
PublicationsUntil Now
Research Overview
Treatment Types
Frame-based RT
Cyberknife
Drawbacks
BOO
Overview
Search
Fluence MapOptimization
Results
Solution
Solution:Soft-Robot PositionCorrecting Systems
Experiment Setup
Kalman Filtering
Sensor Fusion
SystemIdentification
3DOFControl
Vision-based HeadPose Estimation
AdaptiveNeuroControl
ControlContributions
Neural NetworkModel
Control gains
Results
References
Background
This page is left blank intentionally.
Olalekan OgunmoluRobotic Radiotherapy: An optimal, adaptive control approach.
RoboticRadiotherapy:An optimal,
adaptivecontrol
approach.
OlalekanOgunmolu
PublicationsUntil Now
Research Overview
Treatment Types
Frame-based RT
Cyberknife
Drawbacks
BOO
Overview
Search
Fluence MapOptimization
Results
Solution
Solution:Soft-Robot PositionCorrecting Systems
Experiment Setup
Kalman Filtering
Sensor Fusion
SystemIdentification
3DOFControl
Vision-based HeadPose Estimation
AdaptiveNeuroControl
ControlContributions
Neural NetworkModel
Control gains
Results
References
Background
Cancers of the head and neck (H&N)
Olalekan OgunmoluRobotic Radiotherapy: An optimal, adaptive control approach.
RoboticRadiotherapy:An optimal,
adaptivecontrol
approach.
OlalekanOgunmolu
PublicationsUntil Now
Research Overview
Treatment Types
Frame-based RT
Cyberknife
Drawbacks
BOO
Overview
Search
Fluence MapOptimization
Results
Solution
Solution:Soft-Robot PositionCorrecting Systems
Experiment Setup
Kalman Filtering
Sensor Fusion
SystemIdentification
3DOFControl
Vision-based HeadPose Estimation
AdaptiveNeuroControl
ControlContributions
Neural NetworkModel
Control gains
Results
References
Treatment Options
Radiotherapy treatment is the most effective
Olalekan OgunmoluRobotic Radiotherapy: An optimal, adaptive control approach.
RoboticRadiotherapy:An optimal,
adaptivecontrol
approach.
OlalekanOgunmolu
PublicationsUntil Now
Research Overview
Treatment Types
Frame-based RT
Cyberknife
Drawbacks
BOO
Overview
Search
Fluence MapOptimization
Results
Solution
Solution:Soft-Robot PositionCorrecting Systems
Experiment Setup
Kalman Filtering
Sensor Fusion
SystemIdentification
3DOFControl
Vision-based HeadPose Estimation
AdaptiveNeuroControl
ControlContributions
Neural NetworkModel
Control gains
Results
References
Radiotherapy types
Conventional Radiation Therapy
Conformal Radiation Therapy (CFRT)
Intensity-Modulated Radiation Therapy (IMRT)
Left: Conventional radiotherapy.Middle: Conformal radiotherapy (CFRT) without intensity modulation.Right: CFRT with intensity modulation. Reprinted from Webb (2001).
Olalekan OgunmoluRobotic Radiotherapy: An optimal, adaptive control approach.
RoboticRadiotherapy:An optimal,
adaptivecontrol
approach.
OlalekanOgunmolu
PublicationsUntil Now
Research Overview
Treatment Types
Frame-based RT
Cyberknife
Drawbacks
BOO
Overview
Search
Fluence MapOptimization
Results
Solution
Solution:Soft-Robot PositionCorrecting Systems
Experiment Setup
Kalman Filtering
Sensor Fusion
SystemIdentification
3DOFControl
Vision-based HeadPose Estimation
AdaptiveNeuroControl
ControlContributions
Neural NetworkModel
Control gains
Results
References
Frame-based Radiotherapy Treatment
Requires head landmarks + mechanical immobilizationmechanisms
Aim ionizing radiation at abnormal tissues
Olalekan OgunmoluRobotic Radiotherapy: An optimal, adaptive control approach.
RoboticRadiotherapy:An optimal,
adaptivecontrol
approach.
OlalekanOgunmolu
PublicationsUntil Now
Research Overview
Treatment Types
Frame-based RT
Cyberknife
Drawbacks
BOO
Overview
Search
Fluence MapOptimization
Results
Solution
Solution:Soft-Robot PositionCorrecting Systems
Experiment Setup
Kalman Filtering
Sensor Fusion
SystemIdentification
3DOFControl
Vision-based HeadPose Estimation
AdaptiveNeuroControl
ControlContributions
Neural NetworkModel
Control gains
Results
References
Frameless (Completely non-invasive) RT
High-dose volumes with complex shapes [Adler and Cox (1995)]
c©Accuray Inc.
Olalekan OgunmoluRobotic Radiotherapy: An optimal, adaptive control approach.
RoboticRadiotherapy:An optimal,
adaptivecontrol
approach.
OlalekanOgunmolu
PublicationsUntil Now
Research Overview
Treatment Types
Frame-based RT
Cyberknife
Drawbacks
BOO
Overview
Search
Fluence MapOptimization
Results
Solution
Solution:Soft-Robot PositionCorrecting Systems
Experiment Setup
Kalman Filtering
Sensor Fusion
SystemIdentification
3DOFControl
Vision-based HeadPose Estimation
AdaptiveNeuroControl
ControlContributions
Neural NetworkModel
Control gains
Results
References
Image Guided Radiotherapy
c© Wiersma et al. (2009)
Olalekan OgunmoluRobotic Radiotherapy: An optimal, adaptive control approach.
RoboticRadiotherapy:An optimal,
adaptivecontrol
approach.
OlalekanOgunmolu
PublicationsUntil Now
Research Overview
Treatment Types
Frame-based RT
Cyberknife
Drawbacks
BOO
Overview
Search
Fluence MapOptimization
Results
Solution
Solution:Soft-Robot PositionCorrecting Systems
Experiment Setup
Kalman Filtering
Sensor Fusion
SystemIdentification
3DOFControl
Vision-based HeadPose Estimation
AdaptiveNeuroControl
ControlContributions
Neural NetworkModel
Control gains
Results
References
Image-Guided SRS and SB Radiotherapy
The Novalis ExacTrac Module
c©Novalis
Olalekan OgunmoluRobotic Radiotherapy: An optimal, adaptive control approach.
RoboticRadiotherapy:An optimal,
adaptivecontrol
approach.
OlalekanOgunmolu
PublicationsUntil Now
Research Overview
Treatment Types
Frame-based RT
Cyberknife
Drawbacks
BOO
Overview
Search
Fluence MapOptimization
Results
Solution
Solution:Soft-Robot PositionCorrecting Systems
Experiment Setup
Kalman Filtering
Sensor Fusion
SystemIdentification
3DOFControl
Vision-based HeadPose Estimation
AdaptiveNeuroControl
ControlContributions
Neural NetworkModel
Control gains
Results
References
Radiation Therapy Drawbacks
Frame-based immobilization
LINAC angle misalignments cause negative dosimetryeffects [Xing (2000)].Does not encourage fractionated treatments
Frameless RT
Incompatible with conventional LINACs used at themajority of cancer centers.
Novalis ExacTrac Robotic Tilt Module
Only uses pre-treatment imagesProvides just rigid motion compensation
Olalekan OgunmoluRobotic Radiotherapy: An optimal, adaptive control approach.
RoboticRadiotherapy:An optimal,
adaptivecontrol
approach.
OlalekanOgunmolu
PublicationsUntil Now
Research Overview
Treatment Types
Frame-based RT
Cyberknife
Drawbacks
BOO
Overview
Search
Fluence MapOptimization
Results
Solution
Solution:Soft-Robot PositionCorrecting Systems
Experiment Setup
Kalman Filtering
Sensor Fusion
SystemIdentification
3DOFControl
Vision-based HeadPose Estimation
AdaptiveNeuroControl
ControlContributions
Neural NetworkModel
Control gains
Results
References
Beam Orientation Optimization
During treatment planning, a beam orientationoptimization problem (BOO) is separately solved
Radiation is delivered from ≈ (5− 15) different beamorientations during IMRT
BOO determines the best beam angle combinations fordelivering radiation.
Process of determining beamlets’ intensities is termedfluence map optimization (FMO)
Olalekan OgunmoluRobotic Radiotherapy: An optimal, adaptive control approach.
RoboticRadiotherapy:An optimal,
adaptivecontrol
approach.
OlalekanOgunmolu
PublicationsUntil Now
Research Overview
Treatment Types
Frame-based RT
Cyberknife
Drawbacks
BOO
Overview
Search
Fluence MapOptimization
Results
Solution
Solution:Soft-Robot PositionCorrecting Systems
Experiment Setup
Kalman Filtering
Sensor Fusion
SystemIdentification
3DOFControl
Vision-based HeadPose Estimation
AdaptiveNeuroControl
ControlContributions
Neural NetworkModel
Control gains
Results
References
Cyberknife Radiation Therapy
c©Accuray Inc.
Olalekan OgunmoluRobotic Radiotherapy: An optimal, adaptive control approach.
RoboticRadiotherapy:An optimal,
adaptivecontrol
approach.
OlalekanOgunmolu
PublicationsUntil Now
Research Overview
Treatment Types
Frame-based RT
Cyberknife
Drawbacks
BOO
Overview
Search
Fluence MapOptimization
Results
Solution
Solution:Soft-Robot PositionCorrecting Systems
Experiment Setup
Kalman Filtering
Sensor Fusion
SystemIdentification
3DOFControl
Vision-based HeadPose Estimation
AdaptiveNeuroControl
ControlContributions
Neural NetworkModel
Control gains
Results
References
Radiation Therapy Treatment Plan
Reprinted from “IMRT Optimization Algorithms. David Shepard. Swedish Cancer Institute. AAPM 2007.”
Olalekan OgunmoluRobotic Radiotherapy: An optimal, adaptive control approach.
RoboticRadiotherapy:An optimal,
adaptivecontrol
approach.
OlalekanOgunmolu
PublicationsUntil Now
Research Overview
Treatment Types
Frame-based RT
Cyberknife
Drawbacks
BOO
Overview
Search
Fluence MapOptimization
Results
Solution
Solution:Soft-Robot PositionCorrecting Systems
Experiment Setup
Kalman Filtering
Sensor Fusion
SystemIdentification
3DOFControl
Vision-based HeadPose Estimation
AdaptiveNeuroControl
ControlContributions
Neural NetworkModel
Control gains
Results
References
Our approach
Automate the beam search problem
Drawing ideas from
pattern recognition;
monte carlo evaluations;
game simulations; and
approximate dynamic programming
Ultimate goal is real-time beam angle prediction given atarget volume
Olalekan OgunmoluRobotic Radiotherapy: An optimal, adaptive control approach.
RoboticRadiotherapy:An optimal,
adaptivecontrol
approach.
OlalekanOgunmolu
PublicationsUntil Now
Research Overview
Treatment Types
Frame-based RT
Cyberknife
Drawbacks
BOO
Overview
Search
Fluence MapOptimization
Results
Solution
Solution:Soft-Robot PositionCorrecting Systems
Experiment Setup
Kalman Filtering
Sensor Fusion
SystemIdentification
3DOFControl
Vision-based HeadPose Estimation
AdaptiveNeuroControl
ControlContributions
Neural NetworkModel
Control gains
Results
References
Game Tree Simulation
For bd possible move sequences for a robot-patient setup
where b = cardinality of discretized angles; and
d = number of beam angles to choose from b.
Suppose b = 180 and d = 5, we have 1805 possible searchdirections
Exhaustive search becomes real-time infeasible.
Olalekan OgunmoluRobotic Radiotherapy: An optimal, adaptive control approach.
RoboticRadiotherapy:An optimal,
adaptivecontrol
approach.
OlalekanOgunmolu
PublicationsUntil Now
Research Overview
Treatment Types
Frame-based RT
Cyberknife
Drawbacks
BOO
Overview
Search
Fluence MapOptimization
Results
Solution
Solution:Soft-Robot PositionCorrecting Systems
Experiment Setup
Kalman Filtering
Sensor Fusion
SystemIdentification
3DOFControl
Vision-based HeadPose Estimation
AdaptiveNeuroControl
ControlContributions
Neural NetworkModel
Control gains
Results
References
State Representation: Network Input
Olalekan OgunmoluRobotic Radiotherapy: An optimal, adaptive control approach.
RoboticRadiotherapy:An optimal,
adaptivecontrol
approach.
OlalekanOgunmolu
PublicationsUntil Now
Research Overview
Treatment Types
Frame-based RT
Cyberknife
Drawbacks
BOO
Overview
Search
Fluence MapOptimization
Results
Solution
Solution:Soft-Robot PositionCorrecting Systems
Experiment Setup
Kalman Filtering
Sensor Fusion
SystemIdentification
3DOFControl
Vision-based HeadPose Estimation
AdaptiveNeuroControl
ControlContributions
Neural NetworkModel
Control gains
Results
References
Separated Target Volume Organs and PTV Masks
Olalekan OgunmoluRobotic Radiotherapy: An optimal, adaptive control approach.
RoboticRadiotherapy:An optimal,
adaptivecontrol
approach.
OlalekanOgunmolu
PublicationsUntil Now
Research Overview
Treatment Types
Frame-based RT
Cyberknife
Drawbacks
BOO
Overview
Search
Fluence MapOptimization
Results
Solution
Solution:Soft-Robot PositionCorrecting Systems
Experiment Setup
Kalman Filtering
Sensor Fusion
SystemIdentification
3DOFControl
Vision-based HeadPose Estimation
AdaptiveNeuroControl
ControlContributions
Neural NetworkModel
Control gains
Results
References
Methods
Suppose the first player is p1, and the second player is p2
p1 chooses its action under a (stochastic) strategy,πp1 = {πp1
0 , πp11 , . . . , π
p1
T } ⊆ Πp1
minimizing the game’s outcome ζ
p2’s actions are governed by a policyπp2 = {πp2
0 , πp21 , . . . , π
p2
T } ⊆ Πp2
maximizing ζ in order to guarantee an equilibrium solutionfor a game without saddle point.
Πpi is the set of all possible nonstationary Markovianpolicies
Olalekan OgunmoluRobotic Radiotherapy: An optimal, adaptive control approach.
RoboticRadiotherapy:An optimal,
adaptivecontrol
approach.
OlalekanOgunmolu
PublicationsUntil Now
Research Overview
Treatment Types
Frame-based RT
Cyberknife
Drawbacks
BOO
Overview
Search
Fluence MapOptimization
Results
Solution
Solution:Soft-Robot PositionCorrecting Systems
Experiment Setup
Kalman Filtering
Sensor Fusion
SystemIdentification
3DOFControl
Vision-based HeadPose Estimation
AdaptiveNeuroControl
ControlContributions
Neural NetworkModel
Control gains
Results
References
Methods
One may write the optimal cost-to-go value function forstate s in stage t, with horizon length T as
V ∗t (s) = infπp1∈Πp1
supπp2∈Πp2
E
[T−1∑i=t
Vt(s0, f (st , πp1 , πp2))
],
s ∈ S , t = 0, . . . ,T − 1
where the terminal value V ∗T (s) = 0, ∀ s ∈ S ;
πp1 and πp2 contain the action/control sequences{ap1
t }0≤t≤T and {ap2t }0≤t≤T
Each player generates a mixed strategy determined byaveraging the outcome of individual plays.
Olalekan OgunmoluRobotic Radiotherapy: An optimal, adaptive control approach.
RoboticRadiotherapy:An optimal,
adaptivecontrol
approach.
OlalekanOgunmolu
PublicationsUntil Now
Research Overview
Treatment Types
Frame-based RT
Cyberknife
Drawbacks
BOO
Overview
Search
Fluence MapOptimization
Results
Solution
Solution:Soft-Robot PositionCorrecting Systems
Experiment Setup
Kalman Filtering
Sensor Fusion
SystemIdentification
3DOFControl
Vision-based HeadPose Estimation
AdaptiveNeuroControl
ControlContributions
Neural NetworkModel
Control gains
Results
References
Methods
The saddle point strategies for an optimal control
sequence pair {ap∗1
t , ap∗2t } can be recursively obtained by
V ∗t (s) = V ∗t (st , πp1t , π
p2t ) = min
πp1∈Πp1max
πp2∈Πp2V ?t (st , π
p1 , πp2)
∀st ∈ S, πp1 ∈ Πp1 , πp2 ∈ Πp2 .
such that
V ?p1≤ V ? ≤ V ?
p2∀ {πp1
t , πp2t }0≤t≤T .
where V ?pi
are the respective optimal values for each player.
Olalekan OgunmoluRobotic Radiotherapy: An optimal, adaptive control approach.
RoboticRadiotherapy:An optimal,
adaptivecontrol
approach.
OlalekanOgunmolu
PublicationsUntil Now
Research Overview
Treatment Types
Frame-based RT
Cyberknife
Drawbacks
BOO
Overview
Search
Fluence MapOptimization
Results
Solution
Solution:Soft-Robot PositionCorrecting Systems
Experiment Setup
Kalman Filtering
Sensor Fusion
SystemIdentification
3DOFControl
Vision-based HeadPose Estimation
AdaptiveNeuroControl
ControlContributions
Neural NetworkModel
Control gains
Results
References
Game Tree Simulation
Network roll-out policy then efficiently guides the tree’sgame, Γ toward a best-first set of beam angle candidates
Best-first leaf node encountered is the child node with thehighest reward in the tree
Essentially, a sampling-based lookout algorithm
Focuses learning on regions of the state space that arelikely to have a good fluence
Olalekan OgunmoluRobotic Radiotherapy: An optimal, adaptive control approach.
RoboticRadiotherapy:An optimal,
adaptivecontrol
approach.
OlalekanOgunmolu
PublicationsUntil Now
Research Overview
Treatment Types
Frame-based RT
Cyberknife
Drawbacks
BOO
Overview
Search
Fluence MapOptimization
Results
Solution
Solution:Soft-Robot PositionCorrecting Systems
Experiment Setup
Kalman Filtering
Sensor Fusion
SystemIdentification
3DOFControl
Vision-based HeadPose Estimation
AdaptiveNeuroControl
ControlContributions
Neural NetworkModel
Control gains
Results
References
Methods: Fluence Map Optimization (FMO)
Suppose X is the total discretized of voxels of interest(VOI ’s) in a target volume
Suppose B1 ∪ B2 ∪ . . . ∪ Bn ⊆ B represents the partitionsubset of a beam B
where n is the total number of beams from which radiationcan be delivered
Suppose further that Dij(θk) is the matrix that describeseach dose influence, di .
Olalekan OgunmoluRobotic Radiotherapy: An optimal, adaptive control approach.
RoboticRadiotherapy:An optimal,
adaptivecontrol
approach.
OlalekanOgunmolu
PublicationsUntil Now
Research Overview
Treatment Types
Frame-based RT
Cyberknife
Drawbacks
BOO
Overview
Search
Fluence MapOptimization
Results
Solution
Solution:Soft-Robot PositionCorrecting Systems
Experiment Setup
Kalman Filtering
Sensor Fusion
SystemIdentification
3DOFControl
Vision-based HeadPose Estimation
AdaptiveNeuroControl
ControlContributions
Neural NetworkModel
Control gains
Results
References
Methods: FMO problem definition
The pre-calculated dose term is given byAx = {
∑swsvsDs
ijxs | Dij ∈ Rn×l , n > l}
Let ws = {w s , ws} be the respective underdosing andoverdosing weights for the OARs and PTVs
We propose the following cost
1
vs
∑s∈OARs
‖(bs − w sDsijxs)+‖2
2 +1
vs
∑s∈PTVs
‖(wsDsijxs − bs)+‖2
2
(1)
Olalekan OgunmoluRobotic Radiotherapy: An optimal, adaptive control approach.
RoboticRadiotherapy:An optimal,
adaptivecontrol
approach.
OlalekanOgunmolu
PublicationsUntil Now
Research Overview
Treatment Types
Frame-based RT
Cyberknife
Drawbacks
BOO
Overview
Search
Fluence MapOptimization
Results
Solution
Solution:Soft-Robot PositionCorrecting Systems
Experiment Setup
Kalman Filtering
Sensor Fusion
SystemIdentification
3DOFControl
Vision-based HeadPose Estimation
AdaptiveNeuroControl
ControlContributions
Neural NetworkModel
Control gains
Results
References
Methods: FMO
The objective, subject to nonnegative bixel intensityconstraints is the minimization problem
min1
2‖Ax− b‖2
2 subject to x ≥ 0.
with Lagrangian
L(x,λ) =1
2‖Ax− b‖2
2 − λTx.
Introduce the auxiliary variable z, we have
minx
1
2‖Ax− b‖2
2, subject to z = x, z ≥ 0,
Olalekan OgunmoluRobotic Radiotherapy: An optimal, adaptive control approach.
RoboticRadiotherapy:An optimal,
adaptivecontrol
approach.
OlalekanOgunmolu
PublicationsUntil Now
Research Overview
Treatment Types
Frame-based RT
Cyberknife
Drawbacks
BOO
Overview
Search
Fluence MapOptimization
Results
Solution
Solution:Soft-Robot PositionCorrecting Systems
Experiment Setup
Kalman Filtering
Sensor Fusion
SystemIdentification
3DOFControl
Vision-based HeadPose Estimation
AdaptiveNeuroControl
ControlContributions
Neural NetworkModel
Control gains
Results
References
Methods: FMO
Solving the x and z sub-problems, we have
x, z-updates
xk+1 =(
ATA + ρI)−1 (
ATb + ρzk − λk)
zk+1 = Sλ/ρ
(xk+1 + λk
)where Sλ/ρ(τ) = (x − λ/ρ)+ − (−τ − λ/ρ)+. λ isupdated as
λk+1 = λk − γ(zk+1 − xk+1), (2)
where γ is a parameter that controls the step length.
Olalekan OgunmoluRobotic Radiotherapy: An optimal, adaptive control approach.
RoboticRadiotherapy:An optimal,
adaptivecontrol
approach.
OlalekanOgunmolu
PublicationsUntil Now
Research Overview
Treatment Types
Frame-based RT
Cyberknife
Drawbacks
BOO
Overview
Search
Fluence MapOptimization
Results
Solution
Solution:Soft-Robot PositionCorrecting Systems
Experiment Setup
Kalman Filtering
Sensor Fusion
SystemIdentification
3DOFControl
Vision-based HeadPose Estimation
AdaptiveNeuroControl
ControlContributions
Neural NetworkModel
Control gains
Results
References
Results
Olalekan OgunmoluRobotic Radiotherapy: An optimal, adaptive control approach.
RoboticRadiotherapy:An optimal,
adaptivecontrol
approach.
OlalekanOgunmolu
PublicationsUntil Now
Research Overview
Treatment Types
Frame-based RT
Cyberknife
Drawbacks
BOO
Overview
Search
Fluence MapOptimization
Results
Solution
Solution:Soft-Robot PositionCorrecting Systems
Experiment Setup
Kalman Filtering
Sensor Fusion
SystemIdentification
3DOFControl
Vision-based HeadPose Estimation
AdaptiveNeuroControl
ControlContributions
Neural NetworkModel
Control gains
Results
References
Results: Dose Wash Plot
Olalekan OgunmoluRobotic Radiotherapy: An optimal, adaptive control approach.
RoboticRadiotherapy:An optimal,
adaptivecontrol
approach.
OlalekanOgunmolu
PublicationsUntil Now
Research Overview
Treatment Types
Frame-based RT
Cyberknife
Drawbacks
BOO
Overview
Search
Fluence MapOptimization
Results
Solution
Solution:Soft-Robot PositionCorrecting Systems
Experiment Setup
Kalman Filtering
Sensor Fusion
SystemIdentification
3DOFControl
Vision-based HeadPose Estimation
AdaptiveNeuroControl
ControlContributions
Neural NetworkModel
Control gains
Results
References
Results: Dose Wash
Olalekan OgunmoluRobotic Radiotherapy: An optimal, adaptive control approach.
RoboticRadiotherapy:An optimal,
adaptivecontrol
approach.
OlalekanOgunmolu
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Frame-based RT
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BOO
Overview
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Fluence MapOptimization
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Solution
Solution:Soft-Robot PositionCorrecting Systems
Experiment Setup
Kalman Filtering
Sensor Fusion
SystemIdentification
3DOFControl
Vision-based HeadPose Estimation
AdaptiveNeuroControl
ControlContributions
Neural NetworkModel
Control gains
Results
References
Results: Clinically Optimized DVH Curve
Olalekan OgunmoluRobotic Radiotherapy: An optimal, adaptive control approach.
RoboticRadiotherapy:An optimal,
adaptivecontrol
approach.
OlalekanOgunmolu
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Frame-based RT
Cyberknife
Drawbacks
BOO
Overview
Search
Fluence MapOptimization
Results
Solution
Solution:Soft-Robot PositionCorrecting Systems
Experiment Setup
Kalman Filtering
Sensor Fusion
SystemIdentification
3DOFControl
Vision-based HeadPose Estimation
AdaptiveNeuroControl
ControlContributions
Neural NetworkModel
Control gains
Results
References
Results: Deep BOO Optimized DVH Curve
Olalekan OgunmoluRobotic Radiotherapy: An optimal, adaptive control approach.
RoboticRadiotherapy:An optimal,
adaptivecontrol
approach.
OlalekanOgunmolu
PublicationsUntil Now
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Treatment Types
Frame-based RT
Cyberknife
Drawbacks
BOO
Overview
Search
Fluence MapOptimization
Results
Solution
Solution:Soft-Robot PositionCorrecting Systems
Experiment Setup
Kalman Filtering
Sensor Fusion
SystemIdentification
3DOFControl
Vision-based HeadPose Estimation
AdaptiveNeuroControl
ControlContributions
Neural NetworkModel
Control gains
Results
References
Results: Deep BOO Optimized DVH Curve
Olalekan OgunmoluRobotic Radiotherapy: An optimal, adaptive control approach.
RoboticRadiotherapy:An optimal,
adaptivecontrol
approach.
OlalekanOgunmolu
PublicationsUntil Now
Research Overview
Treatment Types
Frame-based RT
Cyberknife
Drawbacks
BOO
Overview
Search
Fluence MapOptimization
Results
Solution
Solution:Soft-Robot PositionCorrecting Systems
Experiment Setup
Kalman Filtering
Sensor Fusion
SystemIdentification
3DOFControl
Vision-based HeadPose Estimation
AdaptiveNeuroControl
ControlContributions
Neural NetworkModel
Control gains
Results
References
Results: Deep BOO Optimized DVH Curve
Olalekan OgunmoluRobotic Radiotherapy: An optimal, adaptive control approach.
RoboticRadiotherapy:An optimal,
adaptivecontrol
approach.
OlalekanOgunmolu
PublicationsUntil Now
Research Overview
Treatment Types
Frame-based RT
Cyberknife
Drawbacks
BOO
Overview
Search
Fluence MapOptimization
Results
Solution
Solution:Soft-Robot PositionCorrecting Systems
Experiment Setup
Kalman Filtering
Sensor Fusion
SystemIdentification
3DOFControl
Vision-based HeadPose Estimation
AdaptiveNeuroControl
ControlContributions
Neural NetworkModel
Control gains
Results
References
The Immobilization Problem
This page is left blank intentionally.
Olalekan OgunmoluRobotic Radiotherapy: An optimal, adaptive control approach.
RoboticRadiotherapy:An optimal,
adaptivecontrol
approach.
OlalekanOgunmolu
PublicationsUntil Now
Research Overview
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Frame-based RT
Cyberknife
Drawbacks
BOO
Overview
Search
Fluence MapOptimization
Results
Solution
Solution:Soft-Robot PositionCorrecting Systems
Experiment Setup
Kalman Filtering
Sensor Fusion
SystemIdentification
3DOFControl
Vision-based HeadPose Estimation
AdaptiveNeuroControl
ControlContributions
Neural NetworkModel
Control gains
Results
References
The Immobilization Problem
Cervino et al. (2010) Liu et al. (2015) Ostyn et al. (2017)
Feasibility evaluation 4-D robotic stage couch 6-DoF robotic couch
Olalekan OgunmoluRobotic Radiotherapy: An optimal, adaptive control approach.
RoboticRadiotherapy:An optimal,
adaptivecontrol
approach.
OlalekanOgunmolu
PublicationsUntil Now
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Frame-based RT
Cyberknife
Drawbacks
BOO
Overview
Search
Fluence MapOptimization
Results
Solution
Solution:Soft-Robot PositionCorrecting Systems
Experiment Setup
Kalman Filtering
Sensor Fusion
SystemIdentification
3DOFControl
Vision-based HeadPose Estimation
AdaptiveNeuroControl
ControlContributions
Neural NetworkModel
Control gains
Results
References
Drawbacks of current solutions
Rigid patient’s body assumption
Non-compliant immobilization devices
Lack of embodied intelligence and the morphologicalcomputation property in immobilization devices.
Attenuation of x-rays or irradiation photon beams
Olalekan OgunmoluRobotic Radiotherapy: An optimal, adaptive control approach.
RoboticRadiotherapy:An optimal,
adaptivecontrol
approach.
OlalekanOgunmolu
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Cyberknife
Drawbacks
BOO
Overview
Search
Fluence MapOptimization
Results
Solution
Solution:Soft-Robot PositionCorrecting Systems
Experiment Setup
Kalman Filtering
Sensor Fusion
SystemIdentification
3DOFControl
Vision-based HeadPose Estimation
AdaptiveNeuroControl
ControlContributions
Neural NetworkModel
Control gains
Results
References
Solution: Soft-Robot Position Correcting Systems
Eliminate rigid frames and metallic rings
Maskless and frameless positioning system X
Eliminate attenuation of X-Ray beams
Separate electromechanical components from patientactuation XExplore soft polymer actuators X
Control design
Feedback control + optimal regulation + robustness todisturbance X
Adapt for changing parameters in system’s dynamicswithin control law
Olalekan OgunmoluRobotic Radiotherapy: An optimal, adaptive control approach.
RoboticRadiotherapy:An optimal,
adaptivecontrol
approach.
OlalekanOgunmolu
PublicationsUntil Now
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Frame-based RT
Cyberknife
Drawbacks
BOO
Overview
Search
Fluence MapOptimization
Results
Solution
Solution:Soft-Robot PositionCorrecting Systems
Experiment Setup
Kalman Filtering
Sensor Fusion
SystemIdentification
3DOFControl
Vision-based HeadPose Estimation
AdaptiveNeuroControl
ControlContributions
Neural NetworkModel
Control gains
Results
References
Vision-based 1-DOF Control
Left blank intentionally
Olalekan OgunmoluRobotic Radiotherapy: An optimal, adaptive control approach.
RoboticRadiotherapy:An optimal,
adaptivecontrol
approach.
OlalekanOgunmolu
PublicationsUntil Now
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Frame-based RT
Cyberknife
Drawbacks
BOO
Overview
Search
Fluence MapOptimization
Results
Solution
Solution:Soft-Robot PositionCorrecting Systems
Experiment Setup
Kalman Filtering
Sensor Fusion
SystemIdentification
3DOFControl
Vision-based HeadPose Estimation
AdaptiveNeuroControl
ControlContributions
Neural NetworkModel
Control gains
Results
References
Experiment Setup
Olalekan OgunmoluRobotic Radiotherapy: An optimal, adaptive control approach.
RoboticRadiotherapy:An optimal,
adaptivecontrol
approach.
OlalekanOgunmolu
PublicationsUntil Now
Research Overview
Treatment Types
Frame-based RT
Cyberknife
Drawbacks
BOO
Overview
Search
Fluence MapOptimization
Results
Solution
Solution:Soft-Robot PositionCorrecting Systems
Experiment Setup
Kalman Filtering
Sensor Fusion
SystemIdentification
3DOFControl
Vision-based HeadPose Estimation
AdaptiveNeuroControl
ControlContributions
Neural NetworkModel
Control gains
Results
References
Head Pose Estimation: Sensing Hardware
Olalekan OgunmoluRobotic Radiotherapy: An optimal, adaptive control approach.
RoboticRadiotherapy:An optimal,
adaptivecontrol
approach.
OlalekanOgunmolu
PublicationsUntil Now
Research Overview
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Frame-based RT
Cyberknife
Drawbacks
BOO
Overview
Search
Fluence MapOptimization
Results
Solution
Solution:Soft-Robot PositionCorrecting Systems
Experiment Setup
Kalman Filtering
Sensor Fusion
SystemIdentification
3DOFControl
Vision-based HeadPose Estimation
AdaptiveNeuroControl
ControlContributions
Neural NetworkModel
Control gains
Results
References
Sensors’ Noise Floor
0 500 1000 1500 2000
Time(sec)
680
700
720D
ep
th (
mm
)Kinect Xbox Tracking Noise on a Static Target at 684mm
Ground Truth = 680mm; Covariance = 21.5022
100 200 300 400 500 600 700 800 900 1000
Time(sec)
995
1000
1005
1010
De
pth
(m
m)
Kinect v1 Position Estimation of a Static Object
Ground Truth = 960mm; Covariance = 11.4707
Case for Sensed Observation Filtering
Olalekan OgunmoluRobotic Radiotherapy: An optimal, adaptive control approach.
RoboticRadiotherapy:An optimal,
adaptivecontrol
approach.
OlalekanOgunmolu
PublicationsUntil Now
Research Overview
Treatment Types
Frame-based RT
Cyberknife
Drawbacks
BOO
Overview
Search
Fluence MapOptimization
Results
Solution
Solution:Soft-Robot PositionCorrecting Systems
Experiment Setup
Kalman Filtering
Sensor Fusion
SystemIdentification
3DOFControl
Vision-based HeadPose Estimation
AdaptiveNeuroControl
ControlContributions
Neural NetworkModel
Control gains
Results
References
Optimal head state estimation and sensor fusion
Given the measurement sequence from either sensorz(1), · · · , z(j),
find the observation estimates x(i) that minimize themean-square error from true measurement, i .e. ,
x(i |j) = arg minx(i |j)∈Rn
E{(x(i)− x)T (x(i)− x)|z(1), · · · , z(j)}
Suppose we define the estimate error’s covariance as
P(i |j) , E{(x(i)− x(i |j))T (x(i)− x(i |j)) |Z j}. (3)
Olalekan OgunmoluRobotic Radiotherapy: An optimal, adaptive control approach.
RoboticRadiotherapy:An optimal,
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OlalekanOgunmolu
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BOO
Overview
Search
Fluence MapOptimization
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Solution
Solution:Soft-Robot PositionCorrecting Systems
Experiment Setup
Kalman Filtering
Sensor Fusion
SystemIdentification
3DOFControl
Vision-based HeadPose Estimation
AdaptiveNeuroControl
ControlContributions
Neural NetworkModel
Control gains
Results
References
State estimation with Kalman filters
Assume a state model, F(k), common to either sensor,
And denote the state vector as x(k) = [d(k), d(k)]T
where d(k) is the displacement of the head above theactuator
For a discretized time interval ∆T between measurements,we define the state
x(k) = F(k)x(k − 1) + B(k)uk + Gkwk (4)
with
F =
[1 ∆T0 1
](5)
Olalekan OgunmoluRobotic Radiotherapy: An optimal, adaptive control approach.
RoboticRadiotherapy:An optimal,
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OlalekanOgunmolu
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Fluence MapOptimization
Results
Solution
Solution:Soft-Robot PositionCorrecting Systems
Experiment Setup
Kalman Filtering
Sensor Fusion
SystemIdentification
3DOFControl
Vision-based HeadPose Estimation
AdaptiveNeuroControl
ControlContributions
Neural NetworkModel
Control gains
Results
References
Kalman filters
Since there is no control in this case, (4) becomes
xk = Fkxk−1 + Gkwk (6)
where wk is the effect of an unknown input and Gk appliesthat effect to the state vector, xk .
Gk is modeled as uncontrolled forces accelerating the head
Head’s acceleration, ak , is a random scalar; ak ∼ N (0, σa)
Setting Gk = I2x2 and w(k) ∼ N (0,Q(k))
set Q(k) to a random walk sequence, Wk = [ ∆T 2
2 ,∆T ]T
Olalekan OgunmoluRobotic Radiotherapy: An optimal, adaptive control approach.
RoboticRadiotherapy:An optimal,
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Fluence MapOptimization
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Solution
Solution:Soft-Robot PositionCorrecting Systems
Experiment Setup
Kalman Filtering
Sensor Fusion
SystemIdentification
3DOFControl
Vision-based HeadPose Estimation
AdaptiveNeuroControl
ControlContributions
Neural NetworkModel
Control gains
Results
References
Kalman Filters Design
Such that
Q = WWTσa2 =
∆T 4
4
∆T 3
2∆T 3
2∆T 2
σa2. (7)
Set the transfer matrix from the estimates, x(k), toobservations, z1(k) and z2(k) according to
zs = Hs(k)x(k) + vs(k) s = 1, 2 (8)
where Hs(k) =[1, 0
]Tand vs(k) ∼ N (0, σ2
rs)note that v1(k), v2(k), and w(k) are independent anduncorrelated in time.
Olalekan OgunmoluRobotic Radiotherapy: An optimal, adaptive control approach.
RoboticRadiotherapy:An optimal,
adaptivecontrol
approach.
OlalekanOgunmolu
PublicationsUntil Now
Research Overview
Treatment Types
Frame-based RT
Cyberknife
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BOO
Overview
Search
Fluence MapOptimization
Results
Solution
Solution:Soft-Robot PositionCorrecting Systems
Experiment Setup
Kalman Filtering
Sensor Fusion
SystemIdentification
3DOFControl
Vision-based HeadPose Estimation
AdaptiveNeuroControl
ControlContributions
Neural NetworkModel
Control gains
Results
References
KF Priori and Posteriori State Estimates
Prediction Phase:
xk|k−1 = Fxk−1|k−1 + Bkuk
Pk|k−1 = FkPk−1|k−1FkT + Qk (9)
Update Phase:
K(k) = P(k |k − 1)H(k)T [H(k)P(k |k − 1)H(k)T + R(k)]−1
x(k |k) = x(k |k − 1) + K(k)(z(k)−H(k)x(k|k − 1))
P(k|k) = (I−K(k)H(k))P(k |k − 1) (10)
Olalekan OgunmoluRobotic Radiotherapy: An optimal, adaptive control approach.
RoboticRadiotherapy:An optimal,
adaptivecontrol
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OlalekanOgunmolu
PublicationsUntil Now
Research Overview
Treatment Types
Frame-based RT
Cyberknife
Drawbacks
BOO
Overview
Search
Fluence MapOptimization
Results
Solution
Solution:Soft-Robot PositionCorrecting Systems
Experiment Setup
Kalman Filtering
Sensor Fusion
SystemIdentification
3DOFControl
Vision-based HeadPose Estimation
AdaptiveNeuroControl
ControlContributions
Neural NetworkModel
Control gains
Results
References
Filtering Results
200 400 600 800 1000 1200 1400 1600 1800 2000 2200
Samples
670
680
690
700
710
Ob
se
rva
tio
n in
m
m
Kinect Xbox Tracking Noise on a Static Target at 684mm
Observation
200 400 600 800 1000 1200 1400 1600 1800 2000 2200
Samples
682
684
686
688
Ka
lma
n u
pd
ate
s in
m
m
Kalman Filter Estimates with Kinect Xbox. [Truth: 684mm, R: 70mm2
]
Estimates
100 150 200 250 300 350 400
Time(Secs
645
650
655
De
pth
(m
m)
Kinect v1 Tracking Noise on a Static Target at 651mm
Observation
100 150 200 250 300 350 400
Time(secs)
649
650
651
652
653
KF
E
stim
ate
s
(m
m)
Recursive KF Results of Raw Observation in Real Time
Priori
Posteriori
[Left]: Xbox’ observation filtering results. [Right]: KF results of Kinect v2’s observation
Olalekan OgunmoluRobotic Radiotherapy: An optimal, adaptive control approach.
RoboticRadiotherapy:An optimal,
adaptivecontrol
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OlalekanOgunmolu
PublicationsUntil Now
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Cyberknife
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BOO
Overview
Search
Fluence MapOptimization
Results
Solution
Solution:Soft-Robot PositionCorrecting Systems
Experiment Setup
Kalman Filtering
Sensor Fusion
SystemIdentification
3DOFControl
Vision-based HeadPose Estimation
AdaptiveNeuroControl
ControlContributions
Neural NetworkModel
Control gains
Results
References
Global fusion of local tracks
Communicate each updated prediction via Unix namedpipes
Again, assume a state model common to both sensors
Fuse both updates with a variance-weighted average ofeach local track as follows,
x(F )(k |k) = P(F )(k |k)N∑
s=1
[P(s)−1(k|k)x(s)(k|k)
]
where P(F )(k |k) =
[N∑
s=1
P(s)−1(k |k)
]−1
.
Olalekan OgunmoluRobotic Radiotherapy: An optimal, adaptive control approach.
RoboticRadiotherapy:An optimal,
adaptivecontrol
approach.
OlalekanOgunmolu
PublicationsUntil Now
Research Overview
Treatment Types
Frame-based RT
Cyberknife
Drawbacks
BOO
Overview
Search
Fluence MapOptimization
Results
Solution
Solution:Soft-Robot PositionCorrecting Systems
Experiment Setup
Kalman Filtering
Sensor Fusion
SystemIdentification
3DOFControl
Vision-based HeadPose Estimation
AdaptiveNeuroControl
ControlContributions
Neural NetworkModel
Control gains
Results
References
Sensor Fusion Results
Track-to-track fusion of both sensors’ local track estimates.
Olalekan OgunmoluRobotic Radiotherapy: An optimal, adaptive control approach.
RoboticRadiotherapy:An optimal,
adaptivecontrol
approach.
OlalekanOgunmolu
PublicationsUntil Now
Research Overview
Treatment Types
Frame-based RT
Cyberknife
Drawbacks
BOO
Overview
Search
Fluence MapOptimization
Results
Solution
Solution:Soft-Robot PositionCorrecting Systems
Experiment Setup
Kalman Filtering
Sensor Fusion
SystemIdentification
3DOFControl
Vision-based HeadPose Estimation
AdaptiveNeuroControl
ControlContributions
Neural NetworkModel
Control gains
Results
References
Vision-based Control
This page is left blank intentionally.
Olalekan OgunmoluRobotic Radiotherapy: An optimal, adaptive control approach.
RoboticRadiotherapy:An optimal,
adaptivecontrol
approach.
OlalekanOgunmolu
PublicationsUntil Now
Research Overview
Treatment Types
Frame-based RT
Cyberknife
Drawbacks
BOO
Overview
Search
Fluence MapOptimization
Results
Solution
Solution:Soft-Robot PositionCorrecting Systems
Experiment Setup
Kalman Filtering
Sensor Fusion
SystemIdentification
3DOFControl
Vision-based HeadPose Estimation
AdaptiveNeuroControl
ControlContributions
Neural NetworkModel
Control gains
Results
References
System Identification
From I/O data, estimate a model
Find set of optimal model parameters via the minimization,
G (t) = arg minθ
VN(θ,ZN)
where VN(θ,ZN) =∑K
k=1
∑ni=1
1
2(yi (k)− yi (k))2,
and ZN = {u(1) · · · u(N), y(1) · · · y(N)}After a least squares minimization, we derive a state-spacerealization,
x(k + Ts) = Ax(k) + Bu(k) + Ke(k)
y(k) = Cx(k) + Du(k) + e(k) (11)
Olalekan OgunmoluRobotic Radiotherapy: An optimal, adaptive control approach.
RoboticRadiotherapy:An optimal,
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OlalekanOgunmolu
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Solution
Solution:Soft-Robot PositionCorrecting Systems
Experiment Setup
Kalman Filtering
Sensor Fusion
SystemIdentification
3DOFControl
Vision-based HeadPose Estimation
AdaptiveNeuroControl
ControlContributions
Neural NetworkModel
Control gains
Results
References
LQG Design
Posing the cost
J =K∑
k=0
xT (k) Q x(k) + u(k)T R u(k) + 2x(k)T N u(k)
K is the terminal sampling instant,Q is a symmetric, positive semi-definite matrix thatweights the n-states of the A matrix,N specifies a matrix of appropriate dimensions thatpenalizes the cross-product between the input and statevectors,R is a symmetric, positive definite weighting matrix on thecontrol vector u
we can obtain u as ∆u = arg min∆u
J
∆u is a future control sequence
Olalekan OgunmoluRobotic Radiotherapy: An optimal, adaptive control approach.
RoboticRadiotherapy:An optimal,
adaptivecontrol
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OlalekanOgunmolu
PublicationsUntil Now
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Frame-based RT
Cyberknife
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Overview
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Fluence MapOptimization
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Solution
Solution:Soft-Robot PositionCorrecting Systems
Experiment Setup
Kalman Filtering
Sensor Fusion
SystemIdentification
3DOFControl
Vision-based HeadPose Estimation
AdaptiveNeuroControl
ControlContributions
Neural NetworkModel
Control gains
Results
References
Closed-loop control (Full state observer).
B(k)u(k)
Kopt
y(k)++
+∫
C (k)x(k) +
+
A(k)
w(k)
y(k)
v(k)
−+
B(k)++
+∫
C (k)
y(k)
A(k)
-Kobs
ε = y(k)− y(k)
x(k)
−1
y(k)
x(k + 1) = A(k)x(k)− Kobs [C (k)x(k)− y(k)] + B(k)u(k).
Olalekan OgunmoluRobotic Radiotherapy: An optimal, adaptive control approach.
RoboticRadiotherapy:An optimal,
adaptivecontrol
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OlalekanOgunmolu
PublicationsUntil Now
Research Overview
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Frame-based RT
Cyberknife
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BOO
Overview
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Fluence MapOptimization
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Solution
Solution:Soft-Robot PositionCorrecting Systems
Experiment Setup
Kalman Filtering
Sensor Fusion
SystemIdentification
3DOFControl
Vision-based HeadPose Estimation
AdaptiveNeuroControl
ControlContributions
Neural NetworkModel
Control gains
Results
References
1-DOF Control Results
20 30 40 50 60 70 80 90 100
Time (seconds)
685
690
695
700
705
710
Head P
ositio
n(m
m)
Experiment I Reference
Kinect Fused yhat(kT)
LQG Estimated yhat(kT)
10 20 30 40 50 60 70 80 90 100
Time (seconds)
680
690
700
710
Head P
ositio
n (
mm
)
Experiment IIReference
Kinect Fused yhat(kT)
LQG Estimated yhat(kT)
10 20 30 40 50 60 70 80 90
Time (seconds)
680
685
690
695
He
ad
P
ositio
n (m
m)
Experiment III
Reference
Kinect Fused yhat(kT)
LQG Estimated yhat(kT)
LQG Controller on mannequine head.
Olalekan OgunmoluRobotic Radiotherapy: An optimal, adaptive control approach.
RoboticRadiotherapy:An optimal,
adaptivecontrol
approach.
OlalekanOgunmolu
PublicationsUntil Now
Research Overview
Treatment Types
Frame-based RT
Cyberknife
Drawbacks
BOO
Overview
Search
Fluence MapOptimization
Results
Solution
Solution:Soft-Robot PositionCorrecting Systems
Experiment Setup
Kalman Filtering
Sensor Fusion
SystemIdentification
3DOFControl
Vision-based HeadPose Estimation
AdaptiveNeuroControl
ControlContributions
Neural NetworkModel
Control gains
Results
References
Vision-based 3-DOF Control
Left blank intentionally
Olalekan OgunmoluRobotic Radiotherapy: An optimal, adaptive control approach.
RoboticRadiotherapy:An optimal,
adaptivecontrol
approach.
OlalekanOgunmolu
PublicationsUntil Now
Research Overview
Treatment Types
Frame-based RT
Cyberknife
Drawbacks
BOO
Overview
Search
Fluence MapOptimization
Results
Solution
Solution:Soft-Robot PositionCorrecting Systems
Experiment Setup
Kalman Filtering
Sensor Fusion
SystemIdentification
3DOFControl
Vision-based HeadPose Estimation
AdaptiveNeuroControl
ControlContributions
Neural NetworkModel
Control gains
Results
References
Vision-based 3-DOF Control
Hardware Description
Olalekan OgunmoluRobotic Radiotherapy: An optimal, adaptive control approach.
RoboticRadiotherapy:An optimal,
adaptivecontrol
approach.
OlalekanOgunmolu
PublicationsUntil Now
Research Overview
Treatment Types
Frame-based RT
Cyberknife
Drawbacks
BOO
Overview
Search
Fluence MapOptimization
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Solution
Solution:Soft-Robot PositionCorrecting Systems
Experiment Setup
Kalman Filtering
Sensor Fusion
SystemIdentification
3DOFControl
Vision-based HeadPose Estimation
AdaptiveNeuroControl
ControlContributions
Neural NetworkModel
Control gains
Results
References
Vision-based head pose estimation
Acquire face point cloud from stereocamera
Find edges of 2D planar regions in scene
structure (Torr and Zisserman, 2000)
bound resulting plane indiceswith their 2D convex hull
Extract face and face-height neighborsinto a predefined 2D prismatic model(Rusu, R. et. al, 2008)
Cluster extracted points based withEuclidean Clustering (Rusu Thesis)
[Top-left]: Dense point cloud of the scene.[Top-right]: Downsampled cluttered cloud of the scene.[Bottom-left]: Using RANSAC, we searched for 2D plane candidates inthe scene and compute the convex hull of found planar regions. Wethen extrude point indices in the hull to a prismatic polygonal modelto give the face region.[Bottom-right]: An additional step clusters the resultant cloud basedon a user-defined Euclidean distance.
Olalekan OgunmoluRobotic Radiotherapy: An optimal, adaptive control approach.
RoboticRadiotherapy:An optimal,
adaptivecontrol
approach.
OlalekanOgunmolu
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Cyberknife
Drawbacks
BOO
Overview
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Head Pose Estimation
Goal: Compute optimal translation and rotation of thehead
from a model point set X = {−→x i} to a measured point setP = {−→p i}, where Nx = Np = 3,point −→x i ∈ X has the same index as −→p i ∈ Pset three points on the table scene as model points
Obtain centroid of segmented cloud from the previousscene
set this as measured point
Compute the covariance matrix, Σpx , of P and Xextract cyclic components of the skew-symmetric matrix as∆
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Head Pose Estimation (Besl & McKay, ’92)
Form symmetric 4× 4 matrix Q(Σpx)
Q(Σpx) =
[tr(Σpx) ∆T
∆ Σpx + ΣTpx − tr(Σpx)I3
]Optimal rotation quaternion, qR , is maximum eigenvalueof Q(Σpx)
Optimal translation vector is −→q T = −→µ x − R(−→q R)−→µ p
where µx and µp are the mean of point sets X and Prespectively
Face pose is [qT , qR ] = {x , y , z , θ, φ, ψ} w.r.t the worldframe
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Control Proposals
Solve a state feedback and feedforward regulation problem
An adaptation model based on head pose estimate givenpast states and past control inputs:
ZN = {u(k), u(k − 1), . . . u(k − nu), y(k), . . . , y(k − ny )}Let a persistently exciting input signal uex ∈ L2 ∩ L∞excite the system’s nonlinear modes
Control Design Goals
Stabilize system states, y =[x , y , z , θ, φ, ψ
]T
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Control Design Goals
Provide closed loop tracking given a desired trajectory, r
Robustify system to (non-)parametric uncertainties
changing head shapes, size and other anatomic/tumorvariations
Indirect MRAC system. (Source mdpi.com)
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Model Reference Adaptive Control
Model head and bladder dynamics asy = Ay + BΛ (u− f (y)) + w(k)
A, Λ unknown, B, sgnΛ knownf (y) , nonlinear function to be adapted forx , tuple containing past controls and current outputs
Approximate f (y) by a neural network with continuousmemory states
f (u(k − d), y(k),w(k)) is realized with a long-short termmemory cell (Horchreiter and Schmidhuber, ’91, ’97)purpose: remember good adaptation gains
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Assumptions
A dynamic RNN with N neurons, ϕ(x), existsmaps from a compact input space u ⊂ U to y ⊂ Y on theLebesgue integrable functions within [0,T ] or [0,∞)
f (y, u) is exactly ΘTΦ(y)f has coefficients Θ ∈ RN×m and a Lipschitz-continuous vectorof basis functions Φ(y) ∈ RN
Inside a ball YR with known, finite radius R,an ideal neural network (NN) approximation f (y) : Rn → Rm, isrealized to a sufficient degree of accuracy, εf > 0;
Outside YR ,the NN approximation error can be upper-bounded by a knownunbounded, scalar function εmax(y);‖ε(y)‖ ≤ εmax(y), ∀ y ∈ YR ;
There exists an exponentially stable reference modelym = Amym + Bmr
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Adaptive Neuro-Control Scheme
Set control law in terms of parameter estimates from theneural network weights and Lipschitz basis functions
Φ(y) = {y(k−d), · · · , y(k−d−4),u(k−d) · · ·u(k−d−5)}i .e. network looks back in time by 5 time steps at everyinstant, and then makes a prediction
Derive adaptive adjustment mechanism from Lyapunovanalysis for Adaptive Control (Parks, P., 1966)
u = KTy y︸︷︷︸
state feedback
+ KTr r︸︷︷︸
optimal regulator
+ f (y, u)︸ ︷︷ ︸approximator
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Controller formulation
Ky and Kr are adaptive gains to be designed
Term Contributions
KT
y y term keeps the states of the approximation sety ∈ BR stable,
KT
r r term causes the states to follow a givenreference trajectory
Function approximator f (y,u) ensures states thatstart outside the approximation set y ∈ BR
converge to BR in finite time
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Adaptive Control Formulation
Assume model matching conditions
such that Ky = Ky , and Kr = Kr (ideally)
Realize the approximator as f (y) = ΘTΦ(y) + εf (y)
ΘT denotes the vectorized weights of the neural networkΦ(y) denotes the vector of lagged inputs and output,εf (y) is the approximation error.Φ(y) = {y(k−d) · · · y(k−d−4),u(k−d) · · ·u(k−d−5)}
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Neural Network Model
Neural Net Architecture
input: lagged vector of pastobservations and currentcontrol actions
repeat × 3
pass input through anlstm cellfollowed by 30%dropout
output will be controlpredictions directly fed as valvevoltages
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Lyapunov Redesign
Theorem:Given correct choice of adaptive gains Ky and Kr , theerror state vector, e(k) with closed loop time derivative e,is uniformly ultimately bounded, and the state y willconverge to a neighborhood of r.
Proof:
Choose a Lyapunov function candidate V in terms of the
generalized error state space e, gains, KT
y , KT
r , andparameter error εf (y(k)) space
V(e, Ky , KT
r ) = eTPe + tr(KT
y Γ−1y K
T
y |Λ|) + tr(KT
r Γ−1r Kr |Λ|)
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Stability proof
V(e, Ky , Kr ) = eTPe + eTPe + 2tr(KTy Γ−1y
˙Ky |Λ|)
+ 2tr(KTr Γ−1r
˙Kr |Λ|)
=[Ame + BΛ[∆K
Tr r + ∆K
Tx x]]T
Pe + . . .
eTP[Ame + BΛ[∆K
Tr r + ∆K
Tx x]]
+ . . .
2 tr(∆KTx Γ−1
x˙Kx |Λ|) + 2 tr(∆KT
r Γ−1r
˙Kr |Λ|)
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Stability Analysis
= eT (P Am + ATmP)e + 2eTPBΛ
(K
Ty y + K
Tr r)
+2tr(
KTy Γ−1y
˙Ky |Λ|)
+ 2tr(
KTr Γ−1r
˙Kr |Λ|)
= −eTQe− 2eTPBΛεf (y) + 2eTPBΛKTy y
+ 2 tr(
KTy Γ−1y
˙Ky
)+ 2eTPBΛK
Tr r + 2 tr
(∆KT
r Γ−1r
˙Kr
)Notice xT y = tr
(y xT
)from trace identity
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Stability Analysis Cont’d
Therefore,
V(·) = −eTQe− 2eTPBΛεf
+ 2 tr(
KTy (Γ−1
y˙Ky + yeTPBsgn(Λ)
)|Λ|
+ 2 tr(
KTr (Γ−1
r˙Kr + reTPBsgn(Λ)
)|Λ|
where for a real-valued x , we have x = sgn(x)|x |.first two terms will be negative definite for all e 6= 0
since Am is Hurwitz
other terms will be identically null if we choose theadaptation laws
˙Ky = −ΓyyeTP Bsgn(Λ), ˙Kr = −Γr reTP B sgn(Λ)
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Proof Main Matter
,
V(·) = −eTQe− 2eTPBΛεf
≤ −λlow‖e‖2 + 2‖e‖‖PB‖λhigh(Λ)εmax
λlow , λhigh ≡ minimum and maximum characteristic rootsof Q and Λ respectively.
V(·) is thus negative definite outside the compact set
χ =(
e : ‖e‖ ≤ 2‖PB‖λhigh(Λ)εmax (y)λlow (Q)
)thus, we conclude that the error e is uniformly ultimatelybounded.
i.e. y(t)→ 0 as t →∞
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Experimental Results
Head 3-DOF pose made up of tuples {z(k), θ(k), φ(k)}i .e. roll, pitch and yawsample from the parameters of the network
Set f (·) to the fully connected layer of samples from the network
Control law is implemented on the ROS middleware
Gains Ky and Kr found by solving the ODEs iteratively
using a single step of the integral of the solutions to˙Ky (t), ˙Kr (t)
implemented using Runge-Kutta Dormand-Prince 5ODE-solver available in the Boost C++ libraries
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Results
Sample from trained network parameters
Set f (·) to fully connected layer of network
ym is computed based on
ym(t) = eAmtym(0) +∫ t
0eAm(t−τ)Bm r(τ)dτ
Set ym(0) = y(0) at t = 0
For a nonnegative Q and a positive definite P,
the pair (Q,Am) will be observableso that the dynamical system is globally asymptoticallystable.
Pick a positive definite, Q = diag(100, 100, 100) for thedissipation energy
Set Λ = I3×3
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Results
Solving the general form of thelyapunov equation, we have
P =
−1705002668 0 00 −170500
2668 00 0 −170500
2668
Solenoid valves operate in pairs
two valves create a difference inair mass within each IAB at anygiven timeset
B =
1 1 0 0 0 00 0 1 1 0 00 0 0 0 1 1
B maps to the 3-axescontrollers[
uz uθ uψ]T
non-zero terms are themax. duty-cycle tovalves based on thesoftware configurationof the NI RIO PWMgenerator
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Results
10 20 30 40 50 60 70 80time (secs)
5
10
15
20
heig
ht (m
m) z-motion correction
z-setpoint
z
10 20 30 40 50 60 70 80time (secs)
0.5
1
1.5
2
pitch angle
(deg)
pitch motion correction
pitch set-point
pitch motion20 40 60 80 100 120 140 160 180
time (secs)
70
72
74
76
78
80
ro
ll (
de
g)
roll motion correction
roll setpoint
roll motion
[Left]: Goal command: (z , θ, φ) = (2.5mm, 0.25◦, 35◦) to(14mm, 1.6◦, 45◦)T . [Right]: Head roll tracking.
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Steve Webb. Intensity-Modulated Radiation Therapy. Instituteof Physics Publishing Ltd, Bristol and Philadelphia, 2001.
Rodney D Wiersma, Zhifei Wen, Meredith Sadinski, KarlFarrey, and Kamil M Yenice. Development of a framelessstereotactic radiosurgery system based on real-time 6dposition monitoring and adaptive head motioncompensation. Physics in Medicine & Biology, 55(2):389,2009.
L. Xing. Dosimetric effects of patient displacement andcollimator and gantry angle misalignment on intensitymodulated radiation therapy. Radiother Oncol, 56(1):97–108, 2000.
Laura I Cervino, Todd Pawlicki, Joshua D Lawson, and Steve BJiang. Frame-less and mask-less cranial stereotactic
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radiosurgery: a feasibility study. Physics in medicine andbiology, 55(7):1863, 2010.
Xinmin Liu, Andrew H Belcher, Zachary Grelewicz, andRodney D Wiersma. Robotic Stage for Head MotionCorrection in Stereotactic Radiosurgery. In AmericanControl Conference (ACC), 2015, pages 5776–5781. IEEE,2015.
Mark Ostyn, Thomas Dwyer, Matthew Miller, Paden King,Rachel Sacks, Ross Cruikshank, Melvin Rosario, DanielMartinez, Siyong Kim, and Woon-Hong Yeo. Anelectromechanical, patient positioning system for head andneck radiotherapy. Physics in Medicine & Biology, 62(18):7520, 2017.
Eulalie Coevoet, Adrien Escande, and Christian Duriez.Optimization-based inverse model of soft robots with
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contact handling. IEEE Robotics and Automation Letters, 2(3):1413–1419, 2017.
James M Bern, Grace Kumagai, and Stelian Coros.Fabrication, modeling, and control of plush robots. InIntelligent Robots and Systems (IROS), 2017 IEEE/RSJInternational Conference on, pages 3739–3746. IEEE, 2017.
Isuru S Godage, Gustavo A Medrano-Cerda, David T Branson,Emanuele Guglielmino, and Darwin G Caldwell. Dynamicsfor variable length multisection continuum arms. TheInternational Journal of Robotics Research, 35(6):695–722,2016.
Federico Renda, Michele Giorelli, Marcello Calisti, MatteoCianchetti, and Cecilia Laschi. Dynamic model of amultibending soft robot arm driven by cables. IEEETransactions on Robotics, 30(5):1109–1122, 2014.
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Rongjie Kang, David T Branson, Emanuele Guglielmino, andDarwin G Caldwell. Dynamic modeling and control of anoctopus inspired multiple continuum arm robot. Computers& Mathematics with Applications, 64(5):1004–1016, 2012.
Federico Renda, Vito Cacucciolo, Jorge Dias, and LakmalSeneviratne. Discrete cosserat approach for soft robotdynamics: A new piece-wise constant strain model withtorsion and shears. In Intelligent Robots and Systems(IROS), 2016 IEEE/RSJ International Conference on, pages5495–5502. IEEE, 2016.
Federico Renda and Lakmal Seneviratne. A Geometric andUnified Approach for Modeling Soft-Rigid Multi-bodySystems with Lumped and Distributed Degrees of Freedom.2018 IEEE International Conference on Robotics andAutomation (ICRA), pages 1567 – 1574, 2018.
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Results
Solution
Solution:Soft-Robot PositionCorrecting Systems
Experiment Setup
Kalman Filtering
Sensor Fusion
SystemIdentification
3DOFControl
Vision-based HeadPose Estimation
AdaptiveNeuroControl
ControlContributions
Neural NetworkModel
Control gains
Results
References
Juan Carlos Simo and Loc Vu-Quoc. On the dynamics in spaceof rods undergoing large motions—a geometrically exactapproach. Computer methods in applied mechanics andengineering, 66(2):125–161, 1988.
Olalekan OgunmoluRobotic Radiotherapy: An optimal, adaptive control approach.