Inverse kinematics application on medical robot using adapted PSO method

Sancaktar, Idris; Tuna, Burak A.; Ulutaş, Mustafa

doi:10.1016/j.jestch.2018.06.011

Cited by 23 publications

(12 citation statements)

References 14 publications

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“…The paper [ 15 ] illustrates a method of solving the inverse kinematics problem about the position using the PSO algorithm (particle swarm optimization) for the creation of a surgical robot with three linear and three rotary axes that is intended to provide treatment of fractures. The iterative solution of inverse kinematics of a da Vinci robot in a controller-responder system using the Newton method is shown in work [ 16 ], while the inverse kinematics solution of a surgical robot with six degrees of freedom implemented upon a model in CATIA program and D-H (Denavit and Hartenberg) notation is shown in work [ 17 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Specifying Inputs for the Computational Structure of a Surgical System via Optical Method and DLT Algorithm Based on In Vitro Experiments on Cardiovascular Tissue in Minimally Invasive and Robotic Surgery

Ilewicz

Ładyżyńska-Kozdraś

2022

Sensors

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With the application of four optical CMOS sensors, it was possible to capture the trajectory of an endoscopic tool during an in vitro surgical experiment on a cardiovascular preparation. This was due to the possibility of obtaining a path when a reflective marker was attached. In the work, APAS (Ariel Performance Analysis System) software and DLT (direct linear transformation) algorithm were used. This made it possible to acquire kinematic inputs to the computational model of dynamics, which enabled, regardless of the type of surgical robot structure, derivation of the analogous motion of an endoscopic effector due to the mathematical transformation of the trajectory to joints coordinates. Experiments were carried out with the participation of a practiced cardiac surgeon employing classic endoscopic instruments and robot surgical systems. The results indicated by the experiment showed that the inverse task of kinematics of position for the surgical robot with RCM (remote center of motion) structure was solved. The achieved results from the experiment were used as inputs for deriving a numerical dynamics model of surgical robot during transient states that was obtained by applying the finite element method and by driving dynamics moments acquired through the block diagrams method using a steering system with DC (direct current) motor and PID (proportional–integral–derivative) controller. The results section illustrates the course of kinematic values of endoscopic tools which were employed to apply numerical models as inputs, the course of the driving torque of the model of the surgical robot that enabled the selection of the drive system and the strength values, stresses and displacements according to von Mises hypothesis in its structure during the analysis of transient states that made it possible to establish the strength safety of the surgical robot. For the conducted experiments, the accuracy was ± 2 [mm]. In the paper, the employment of optical CMOS sensors in surgical robotics and endoscopy is discussed. The paper concludes that the usage of optical sensors for determining inputs for numerical models of dynamics of surgical robots provides the basis for setting the course of physical quantities that appear in their real object structure, in manners close to reality.

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Section: Literature Reviewmentioning

confidence: 99%

Specifying Inputs for the Computational Structure of a Surgical System via Optical Method and DLT Algorithm Based on In Vitro Experiments on Cardiovascular Tissue in Minimally Invasive and Robotic Surgery

Ilewicz

Ładyżyńska-Kozdraś

2022

Sensors

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“…The right party is located in the right position of the center of gravity point and the left party in against position. By this classification in each state, the nearest persons of right and left position to the center of gravity point are selected as the agent of that party at that state according to Equations (10) and (11). Step 6.…”

Section: President Election Algorithmmentioning

confidence: 99%

“…In a study, if no achievement is resulted at the end of a certain number of steps, PSO algorithm is stopped and the final point is considered as the new beginning point. The process is repeated through repositioning until the criterion is satisfied to decrease the locally capacity particles [10]. Particle Swarm Optimization -Grey Wolf Optimizer (PSO-GWO) method has been also used to acquire the optimal size of the different system components in order to minimize the total cost of fresh water production [11].…”

Section: Introductionmentioning

confidence: 99%

Novel Particle Swarm Optimization Algorithm Based on President Election: Applied to a Renewable Hybrid Power System Controller

Yahyazadeh

Johari

HosseinNia

2021

IJE

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“…During the training process, the training accuracy of the BP neural network is measured by the training error RMSE [10]:…”

Section: Bp Neural Networkmentioning

confidence: 99%

Fault Diagnosis of High-Power Tractor Engine Based on Competitive Multiswarm Cooperative Particle Swarm Optimizer Algorithm

Xiao

Wang

et al. 2020

Shock and Vibration

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With the rapid development of high-power tractor, the fault diagnosis of high-power tractor has become more and more important for ensuring the operating safety and efficiency. PSO is an iterative optimization evolutionary algorithm, which can iterate through different particles to find the optimal solution. However, there is only one population in the standard PSO algorithm, and the information exchange between the populations is relatively single, which can easily lead to the stagnation of the development of the population. In this paper, due to high-power tractor diesel engine fault complexity, fault correlation, and multifault concurrency, a multigroup coevolution particle swarm optimization BP neural network for diesel engine fault diagnosis method was proposed. First, the USB-CAN device was used to collect data of 8 items of the diesel engine under five different working conditions, and the data was parsed through the SAE J1939 protocol; then, the BP neural network was reconstructed, and a competitive multiswarm cooperative particle swarm optimizer algorithm (COM-MCPSO) was used to optimize its structure and weights. Finally, the data of optimized neural network under five different fault conditions show that, compared with BP neural network and PSO optimized BP neural network, the fault diagnosis of COM-MCPSO optimized BP neural network not only improves the network training speed, but also enhances generalization ability and improves recognition accuracy.

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Inverse kinematics application on medical robot using adapted PSO method

Cited by 23 publications

References 14 publications

Specifying Inputs for the Computational Structure of a Surgical System via Optical Method and DLT Algorithm Based on In Vitro Experiments on Cardiovascular Tissue in Minimally Invasive and Robotic Surgery

Specifying Inputs for the Computational Structure of a Surgical System via Optical Method and DLT Algorithm Based on In Vitro Experiments on Cardiovascular Tissue in Minimally Invasive and Robotic Surgery

Novel Particle Swarm Optimization Algorithm Based on President Election: Applied to a Renewable Hybrid Power System Controller

Fault Diagnosis of High-Power Tractor Engine Based on Competitive Multiswarm Cooperative Particle Swarm Optimizer Algorithm

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