Marko Švaco

Robotic application in neurosurgery using intelligent visual and haptic interaction

Today, the complexity and high technical requirements of neurosurgical operations are so demanding that modern robotic achievements and advances of accompanied technologies appear as the immanent means, which can significantly improve neurosurgical practice. A novel robotic system (RONNA –RObotic NeuroNAvigation) for application in neurosurgery is presented. The RONNA consists of two conventional articulated robot arms with a total of 13 degrees of freedom. A rigid and accurate robot is used for precise targeting of planned operating points and a compliant robot is used as operative assistant. A distinctive marker was developed for the purpose of precise localization and registration of the patient’s head. A novel visual calibration method is presented. The developed dual arm neurosurgical system enables flexible and reliable application with embedded behaviour based control providing intuitive interaction with surgical team and new possibilities compared to the existing surgical robot solutions.

Industrial robotic system with adaptive control

In this paper an adaptive multiagent robotic assembly system is presented. State of the art industrial equipment is utilized to perform various assembly tasks in a highly unstructured environment without the need for central control. The emphasis is given to the developed methods that address particular issues in such robotic assembly systems. Close collaboration and intertwined work with human operators is one application under development, possible due to complex sensorial inputs on the robots. Active voice commands and prompts additionally contribute to human-robot interaction. Encounter with unknown objects is another issue that has been addressed and can be solved autonomously for simple case scenarios. Actual assembly applications as well as applications under development are presented. The operation in unstructured environments has been facilitated with vision systems, F/T sensors and other sensorial devices.

A multiagent framework for industrial robotic applications

Abstract The paper presents a novel approach toward modeling and governing complex system behavior in flexible and adaptive robotic assembly systems. A fully distributed multiagent approach is implemented for autonomous control. The system is defined at multiple levels of granularity where agents provide services in respect to the current global goal. A decentralized multiagent approach is adopted for reasons of flexibility and fault tolerance embedded in the design phase. To prove the concept a robotic application for intelligent assembly is presented and discussed. It consists of multiple industrial robots equipped with force/torque sensors, 2D and 3D vision systems, automatic tool changers and other sensors and actuators. Through fusion of sensory input and mutual communication agents construct and negotiate an assembly plan and reconfigure respectively.

Medical applicability of a low-cost industrial robot arm guided with an optical tracking system

Robot systems used in surgical procedures can autonomously position tools at points correlated with preoperative imaging techniques such as magnetic resonance (MR) and computed tomography (CT). The aim of this paper is to measure and assess medical applicability of a low-cost, lightweight industrial robot arm (Universal robot UR5) guided with the medically certified optical tracking system (Polaris Vicra) to positions registered from a CT scan. Technical setup, measurement equipment, device communication and robot control based on OTS feedback are described. Robot intrinsic accuracy, CT scan accuracy and two methods of robot tool positioning with aid of the optical tracking system (OTS) are measured. Measurements show RMS error of the robot (0.669 mm) is decreased 55.4% when guided with OTS using a single marker probe (0.29 mm) and 40.5% when using OTS with relative referencing (0.39 mm). RMS error of the CT scan readings is 0.46 mm.

Brain biopsy performed with the RONNA G3 system: a case study on using a novel robotic navigation device for stereotactic neurosurgery

Robotic neuronavigation is becoming an important tool for neurosurgeons. We present a case study of a frameless stereotactic biopsy guided by the RONNA G3 robotic neuronavigation system.

Validation of Three KUKA Agilus Robots for Application in Neurosurgery

In this paper, we verify three different 6 degrees of freedom Kuka Agilus robots for application in neurosurgery. Application specific reachability maps are generated for robots with 707 mm (R700), 901 mm (R900), and 1101 mm (R1100) horizontal reach. The reachability of each robot reflects a working volume of a standard stereotactic frame which utilizes the center of arc principle. A working volume with 100% reachability yield has been identified for the R900 and R1100 robots when the robot is positioned sideways to the patient. The R700 robot doesn’t have a 100% reachability yield work volume. Robot configurations within the reachability map are further optimized given two dexterity performance indices: the condition number and a new fuzzy joint limit avoidance function. In the experiments, we have further evaluated the impact on robot work volume given robot orientation with respect to the patient. After reorienting the robot a significant increase in work volume with 100% reachability yield was obtained for all three robots.

Automated Marker Localization in the Planning Phase of Robotic Neurosurgery

Accurate patient registration is a critical issue in medical image-guided interventions. The neurosurgical robotic system RObotic Neuro-NAvigation (RONNA) uses four retro-reflective spheres, on a marker attached to the patient’s cranial bone, for patient registration in physical and image space. In this paper, the algorithm for automatic localization of spherical fiducials in CT scans is presented and clinically evaluated. The developed localization algorithm uses a unique approach, which combines machine vision algorithms, biomedical image filtration methods, and mathematical estimation methods. The performance of the localization algorithm was evaluated in comparison with four skilled human operators. The measurements were based on twelve patient and eight lab phantom CT scans. The localization error of the algorithm in comparison with the human readings was smaller by 49.29% according to the ground truth estimation and by 45.91% according to the intra-modal estimation. Localization processing time was reduced by 84.96%. Reliability in terms of successful localization of the fiducial marker was 100% for 20 different test samples containing a total of 116 spherical fiducials. Based on the tests carried out in clinical conditions, the localization algorithm has demonstrated reliability with a high degree of accuracy and short processing time. The developed algorithm provides fully automated and accurate machine vision-based patient localization for the neurosurgical clinical application of the robotic system RONNA.

ARTgrid: a two-level learning architecture based on adaptive resonance theory

This paper proposes a novel neural network architecture based on adaptive resonance theory (ART) called ARTgrid that can perform both online and offline clustering of 2D object structures. The main novelty of the proposed architecture is a two-level categorization and search mechanism that can enhance computation speed while maintaining high performance in cases of higher vigilance values. ARTgrid is developed for specific robotic applications for work in unstructured environments with diverse work objects. For that reason simulations are conducted on random generated data which represents actual manipulation objects, that is, their respective 2D structures. ARTgrid verification is done through comparison in clustering speed with the fuzzy ART algorithm and Adaptive Fuzzy Shadow (AFS) network. Simulation results show that by applying higher vigilance values (ρ > 0.85) clustering performance of ARTgrid is considerably better, while lower vigilance values produce comparable results with the original fuzzy ART algorithm.

Autonomous Planning Framework for Distributed Multiagent Robotic Systems

In this paper a creative action planning algorithm (CAPA) is presented for solving multiagent planning problems and task allocation. The distributed multiagent system taken in consideration is a system of m autonomous agents. Agents workspace contains simplified blocks which form different space structures. By employing the planning algorithm and through interaction agents allocate tasks which they execute in order to assemble the required space structure. The planning algorithm is based on an inductive engine. From a given set of objects which can differ from the initial set agents need to reach a solution in the anticipated search space. A multiagent framework for autonomous planning is developed and implemented on an actual robotic system consisting of three 6 DOF industrial robots.

Task planning based on the interpretation of spatial structures

U ovom istraživanju razvijen je novi algoritam planiranja za transformaciju pocetnog neuređenog stanja objekata u uređeno konacno stanje. Zadatak algoritma planiranja je pronaci moguci niz djelovanja kojima se pocetno stanje okoline, kroz konacan broj diskretnih transformacija, može dovesti u zadano konacno stanje. Stanje okoline tumaci se kroz položaj i orijentaciju objekata. Zadatak planiranja rjesava se u dva koraka. Razvijena je konstruktivna heuristika pomocu koje se dobiva pocetni skup rjesenja. Konstruktivna heuristika koristi mutacije za generiranje pocetne populacije. Genetski algoritam je razvijen za optimizaciju pocetnog skupa rjesenja. Genetski algoritam karakteriziran je usporednom evolucijskom strategijom za pronalaženje rjesenja, s ciljem prostorne pretvorbe neuređenog stanja objekata u uređeno, ogranicen na dvodimenzionalnu interpretaciju radnog prostora. Verifikacija algoritma planiranja napravljena je u virtualnom okruženju.