Thesis (MEng)--Stellenbosch University, 2015. / ENGLISH ABSTRACT: This thesis presents a robust, sampling-based path planning algorithm for commercial airliners that simultaneously performs collision avoidance both with intruder aircraft and terrain. The existing resolution systems implemented on commercial airliners are fast and reliable; however, they do possess certain limitations. This thesis aims to propose an algorithm that is capable of rectifying some of these limitations. The development and research required to derive this conflict resolution system is supplied in the document, including a detailed literature study explaining the selection of the final algorithm. The proposed algorithm applies an incremental sampling-based technique to determine a safe path quickly and reliably. The algorithm makes use of a local planning method to ensure that the paths proposed by the system are indeed flyable. Additional search optimisation techniques are implemented to reduce the computational complexity of the algorithm. As the number of samples increases, the algorithm strives towards an optimal solution; thereby deriving a safe, near-optimal path that avoids the predicted conflict region. The development and justification of the different methods used to adapt the basic algorithm for the application as a confiict resolution system are described in depth. The final system is simulated using a simplified aircraft model. The simulation results show that the proposed algorithm is able to successfully resolve various conflict scenarios, including the generic two aircraft scenario, terrain only scenario, a two aircraft with terrain scenario and a multiple aircraft and terrain scenario. The developed algorithm is tested in cluttered dynamic environments to ensure that it is capable of dealing with airport scenarios. A statistical analysis of the simulation results shows that the algorithm finds an initial resolution path quickly and reliably, while utilising all additional computation time to strive towards a near-optimal solution. / AFRIKAANSE OPSOMMING: Hierdie tesis bied 'n robuuste, monster-gebaseerde roetebeplanningsalgoritme vir kommersiële vliegtuie aan, wat botsingvermyding met indringervliegtuie en met die terrein gelyktydig uitvoer. Die bestaande konflikvermyding- stelsels wat op kommersiële vliegtuie geïmplementeer word, is vinnig en betroubaar; dit het egter ook sekere tekortkominge. Hierdie tesis is daarop gemik om 'n algoritme voor te stel wat in staat is om sommige van hierdie tekortkominge reg te stel. Die ontwikkeling en navorsing wat nodig was om hierdie konflik-vermyding-algoritme af te lei, word in die dokument voorgelê, insluitende 'n gedetailleerde literatuurstudie wat die keuse van die finale algoritme verduidelik. Die voorgestelde algoritme pas 'n inkrementele, monster-gebaseerde tegniek toe om vinnig en betroubaar 'n veilige roete te bepaal. Die algoritme maak gebruik van 'n lokale beplanningsmetode om te verseker dat die roetes wat die stelsel voorstel inderdaad uitvoerbaar is. Aanvullende soektog-optimeringstegnieke word geïmplementeer om die berekeningskompleksiteit van die algoritme te verlaag. Soos die aantal monsters toeneem, streef die algoritme na 'n optimale oplossing; sodoende herlei dit na 'n veilige, byna-optimale roete wat die voorspelde konflikgebied vermy. Die ontwikkeling en regverdiging van die verskillende metodes wat gebruik is om die basiese algoritme aan te pas vir die toepassing daarvan as 'n konflik-vermyding-stelsels word in diepte beskryf. Die finale stelsel word gesimuleer deur 'n vereenvoudigde vliegtuigmodel te gebruik. Die simulasie resultate dui daarop dat die voorgestelde algoritme verskeie konflikscenario's suksesvol kan oplos, insluitend die generiese tweevliegtuigscenario, die slegs-terreinscenario, die tweevliegtuig-met-terreinscenario en die veelvuldige vliegtuig-enterreinscenario. Die ontwikkelde algoritme is in 'n beisge (cluttered), dinamiese omgewing getoets om te verseker dat dit 'n besige lughawescenario kan hanteer. 'n Statistiese ontleding van die simulasie resultate bewys dat die algoritme vinnig en betroubaar 'n aanvanklike oplossingspad kan vind, addisioneel word die oorblywende berekeningstyd ook gebruik om na 'n byna optimaleoplossing te streef.

The thesis aims to put in practice control unit of industrial robot ROB 2-6. The control unit is put in practice with the use of processor from the ARM STM32F100 family. Altogether with the control module it is supposed to be also realized HMI which will enable program loading and servicing of the control unit. The visualization model and algorithm of track planning is also realized within this work.

<p>This thesis presents a computational model of the surgical stitching tasks and a path planning algorithm for robotic assisted stitching. The overall goal of the research is to enable surgical robots to perform automatic suturing. Suturing comprises several distinct steps, one of them is the stitching. During stitching, reaching the desired exit point is difficult because it must be accomplished without direct visual feedback. Moreover, the stitching is a time consuming procedure repeated multiple times during suturing. Therefore, it would be desirable to enhance the surgical robots with the ability of performing automatic suturing. The focus of this work is on the automation of the stitching task. The thesis presents a model based path planning algorithm for the autonomous stitching. The method uses a nonlinear model for the curved needle - soft tissue interaction. The tissue is modeled as a deformable object using continuum mechanics tools. This thesis uses a mesh free deformable tissue model namely, Reproducing Kernel Particle Method (RKPM). RKPM was chosen as it has been proven to accurately handle large deformation and requires no re-meshing algorithms. This method has the potential to be more realistic in modeling various material characteristics by using appropriate strain energy functions. The stitching task is simulated using a constrained deformable model; the deformable tissue is constrained by the interaction with the curved needle. The stitching model was used for needle trajectory path planning during stitching. This new path planning algorithm for the robotic stitching was developed, implemented, and evaluated. Several simulations and experiments were conducted. The first group of simulations comprised random insertions from different insertion points without planning to assess the modeling method and the trajectory of the needle inside the tissue. Then the parameters of the simulations were set according to the measured experimental parameters. The proposed path planning method was tested using a surgical ETHICON needle of type SH 1=2 Circle with the radius of 8:88mm attached to a robotic manipulator. The needle was held by a grasper which is attached to the robotic arm. The experimental results illustrate that the path planned curved needle insertions are fifty percent more accurate than the unplanned ones. The results also show that this open loop approach is sensitive to model parameters.</p> / Master of Applied Science (MASc)

In this thesis, an exploration mode for the PUREi9 robotic vacuum cleaner is implemented. This exploration would provide information for optimizing the cleaning path beforehand, and would allow the robot to relocalize itself or the charger more easily in case it gets lost. Two elements are needed in order to implement an exploration mode; first, an exploration algo-rithm which will decide the next position of the robot in order to obtain useful information about the environment (unknown areas, empty spaces, obstacles...), and second, an exploration map which stores that information and is updated each time a new relevant position is reached. These elements are related and generally both are required for performing successfully the exploration of a specific environment. A frontier-based strategy is adopted for the exploration algorithm, together with occupancy grid maps. This strategy has long been regarded as a key method for autonomous robots working in unknown or changing environments. The idea of frontier-based algorithms is to divide the environ-ment into cells of regular size and drive the robot to the frontiers between cells with no obstacles and cells for which no information has been gathered. It plans one step ahead by choosing a lo-cation which provides new environment information, instead of planning in advance all locations where the robot needs to acquire new sensor information. Based on frontier strategy, two different exploration algorithms are implemented in the project. The first one is called "random frontier strategy", which chooses arbitrarily the frontier to go among the frontiers set. The second is called "closest frontier strategy", which chooses the closest frontier as the NBV (Next Best View) the robot should drive to. A path planning algorithm, based on Dijkstra’s algorithm and a node graph, has also been implemented in order to guide the robot towards the frontiers. The two methods have been compared by means of simulations in different environments. In addition, both exploration strategies have been tested on a real device. It is found that the closest frontier strategy is more efficient in terms of path length between scanning points, while both methods give a similar exploration ratio, or percentage of fully explored cells within the final map. Some additional work is required in order to improve the performance of the exploration method in the future, such as detecting unreachable frontiers, implementing a more robust path planning algorithm, or filtering the laser measurements more extensively. / I den här rapporten har vi implementerat en utforskningsmod för robotdammsugaren Pure i9. Sådan utforskning skulle ge underlag för att optimera städmönstret i förhand och låta roboten relokalisera sig själv eller laddaren om den tappar bort sig. För att implementera utforskning behövs två saker. För det första krävs en algoritm för utforsk-ning, som bestämmer nästa position för roboten, med målet att samla användbar information om omgivningen (okända eller fria områden, hinder etc.) För det andra krävs en karta som lagrar informationen och uppdateras varje gång roboten når en relevant ny position. Dessa två hänger ihop och i allmänhet krävs båda för att framgångsrikt utforska ett specifikt område. Vi har valt en front-baserad strategi för utforskningsalgoritmen, tillsammans med en rutnäts-karta med sannolikheten för hinder. Denna strategi har länge betraktats som en nyckelmetod för autonoma robotar som arbetar i okända eller föränderliga miljöer. Idén med front-baserade strate-gier är att köra roboten till fronterna mellan celler utan hinder och celler där information saknas. Den planerar ett steg framåt genom att välja en plats som ger ny information om miljön, istället för att i förväg planera alla platser där roboten behöver samla in ny sensorinformation. Baserat på front-strategi, har vi implementerat två utforskningsalgoritmer i projektet. Den första är en slumpmässig strategi, som godtyckligt väljer en front att åka till, ur hela mängden av fronter. Den andra är en närmaste fronten-strategi som väljer den närmaste fronten som den nästa bästa utsiktspunkt som roboten ska åka till. Vi har också implementerat en algoritm för banplanering, baserad på Dijkstras algoritm och en nod-graf, för att styra roboten mot fronterna. Vi har jämfört de två metoderna genom simulering i olika miljöer. Dessutom har båda utforsk-ningsstrategierna testats på en riktig enhet. Närmaste fronten-strategin är effektivare med avse-ende på banlängd mellan skanningspunkter, medan båda metoderna ger liknande utforsknings-grad, eller samma procentandel av fullt utforskade celler inom den slutliga kartan.