The emergence of cloud computing has transformed the potential of robotics by enabling multi-robotic teams to fulfil complex tasks in the cloud. This paradigm is known as “cloud robotics” and relieves robots from hardware and software limitations, as large amounts of available resources and parallel computing capabilities are available in the cloud. The introduction of cloud-enabled robots alleviates the need for computationally intensive robots to be built, as many, if not all, of the CPU-intensive tasks can be offloaded into the cloud, resulting in multi-robots that require much less power, energy consumption and on-board processing units. While the benefits of cloud robotics are clearly evident and have resulted in an increase in interest among the scientific community, one of the biggest challenges of cloud robotics is the inherent communication challenges brought about by disconnections between the multi-robotic system and the cloud. The communication delays brought about by the cloud disconnection results in robots not being able to receive and transmit data to the physical cloud. The unavailability of these robotic services in certain instances could prove fatal in a heterogeneous environment that requires multi-robotic teams to assist with the saving of human lives. This niche area is relatively unexplored in the literature. This work serves to assist with the challenge of disconnection in cloud robotics by proposing a survivable cloud multi-robotics (SCMR) framework for heterogeneous environments. The SCMR framework leverages the combination of a virtual ad hoc network formed by the robot-to-robot communication and a physical cloud infrastructure formed by the robot-to-cloud communications. The Quality of Service (QoS) on the SCMR framework is tested and validated by determining the optimal energy utilization and Time of Response (ToR) on drivability analysis with and without cloud connection. The experimental results demonstrate that the proposed framework is feasible for current multi-robotic applications and shows the survivability aspect of the framework in instances of cloud disconnection. / School of Computing / M.Sc. (Computer Science)

Thesis (MScEng)--Stellenbosch University, 2013. / ENGLISH ABSTRACT: This thesis describes the development of a distributed control system for SSL soccer robots. The project continues on work done to develop a robotics research platform at Stellenbosch University. The wireless communication system is implemented using Player middleware. This enables high level programming of the robot drivers and communication clients, resulting in an easily modifiable system. The system is developed to be used as either a centralised or decentralised control system. The software of the robot’s motor controller unit is updated to ensure optimal movement. Slippage of the robot’s wheels restricts the robot’s movement capabilities. Trajectory tracking software is developed to ensure that the robot follows the desired trajectory while operating within its physical limits. The distributed control architecture reduces the robots dependency on the wireless network and the off-field computer. The robots are given some autonomy by integrating the navigation and control on the robot self. Kalman filters are designed to estimate the robots translational and rotational velocities. The Kalman filters fuse vision data from an overhead vision system with inertial measurements of an on-board IMU. This ensures reliable and accurate position, orientation and velocity information on the robot. Test results show an improvement in the controller performance as a result of the proposed system. / AFRIKAANSE OPSOMMING: Hierdie tesis beskryf die ontwikkeling van ’n verspreidebeheerstelsel vir SSL sokker robotte. Die projek gaan voort op vorige werk wat gedoen is om ’n robotika navorsingsplatform aan die Universiteit van Stellenbosch te ontwikkel. Die kommunikasiestelsel is geïmplementeer met behulp van Player middelware. Dit stel die robotbeheerders en kommunikasiekliënte in staat om in hoë vlak tale geprogrameer te word. Dit lei tot ’n maklik veranderbare stelsel. Die stelsel is so ontwikkel dat dit gebruik kan word as óf ’n gesentraliseerde of verspreidebeheerstelsel. Die sagteware van die motorbeheer eenheid is opgedateer om optimale robot beweging te verseker. As die robot se wiele gly beperk dit die robot se bewegingsvermoëns. Trajekvolgings sagteware is ontwikkel om te verseker dat die robot die gewenste pad volg, terwyl dit binne sy fisiese operasionele grense bly. Die verspreibeheerargitektuur verminder die robot se afhanklikheid op die kommunikasienetwerk en die sentrale rekenaar. Die robot is ’n mate van outonomie gegee deur die integrasie van die navigasie en beheer op die robot self te doen. Kalman filters is ontwerp om die robot se translasie en rotasie snelhede te beraam. Die Kalman filters kombineer visuele data van ’n oorhoofse visiestelsel met inertia metings van ’n IMU op die robot. Dit verseker betroubare en akkurate posisie, oriëntasie en snelheids inligting. Toetsresultate toon ’n verbetering in die beheervermoë as ’n gevolg van die voorgestelde stelsel.

Odour localisation is the problem of finding the source of an odour or other volatile chemical. It promises many valuable practical and humanitarian applications. Most localisation methods require a robot to reactively track an odour plume along its entire length. This approach is time consuming and may be not be possible in a cluttered indoor environment, where airflow tends to form sectors of circulating airflow. Such environments may be encountered in crawl-ways under floors, roof cavities, mines, caves, tree-canopies, air-ducts, sewers or tunnel systems. Operation in these places is important for such applications as search and rescue and locating the sources of toxic chemicals in an industrial setting. This thesis addresses odour localisation in this class of environments. The solution consists of a sense-map-plan-act style control scheme (and low level behaviour based controller) with two main stages. Firstly, the airflow in the environment is modelled using naive physics rules which are encapsulated into an algorithm named a Naive Reasoning Machine. It was used in preference to conventional methods as it is fast, does not require boundary conditions, and most importantly, provides approximate solutions to the degree of accuracy required for the task, with analogical data structures that are readily useful to a reasoning algorithm. Secondly, a reasoning algorithm navigates the robot to specific target locations that are determined with a physical map, the airflow map, and knowledge of odour dispersal. Sensor measurements at the target positions provide information regarding the likelihood that odour was emitted from potential odour source locations. The target positions and their traversal are determined so that all the potential odour source sites are accounted for. The core method provides values corresponding to the confidence that the odour source is located in a given region. A second search stage exploiting vision is then used to locate the specific location of the odour source within the predicted region. This comprises the second part of a bi-modal, two-stage search, with each stage exploiting complementary sensing modalities. Single hypothesis airflow modelling faces limitations due to the fact that large differences between airflow topologies are predicted for only small variations in a physical map. This is due to uncertainties in the map and approximations in the modelling process. Furthermore, there are uncertainties regarding the flow direction through inlet/outlet ducts. A method is presented for dealing with these uncertainties, by generating multiple airflow hypotheses. As the robot performs odour localisation, airflow in the environment is measured and used to adjust the confidences of the hypotheses using Bayesian inference. The best hypothesis is then selected, which allows the completion of the localisation task. This method improves the robustness of odour localisation in the presence of uncertainties, making it possible where the single hypothesis method would fail. It also demonstrates the potential for integrating naive physics into a statistical framework. Extensive experimental results are presented to support the methods described above.

Recent work has shown humanoid robots with spinal columns, instead of rigid torsos, benefit from both better balance and an increased ability to absorb external impact. Similarly, snake robots have shown promise as a viable option for exploration in confined spaces with limited human access, such as during power plant maintenance. Both spines and snakes, as well as hyper-redundant manipulators, can simplify to a model of a system with multiple links. The multi-link inverted pendulum is a well known benchmark problem in control systems due to its ability to accommodate varying model complexity. Such a system is useful for testing new learning algorithms or laying the foundation for autonomous control of more complex devices such as robotic spines and multi-segmented arms which currently use traditional control methods or are operated by humans. It is often easy to view these systems as single-agent learners due to the high level of interaction among the segments. However, as the number of links in the system increases, the system becomes harder to control. This work replaces the centralized learner with a team of coevolved agents. The use of a multiagent approach allows for control of larger systems. The addition of transfer learning not only increases the learning rate, but also enables the training of larger teams which were previously infeasible due to extended training times. The results presented support these claims by examining neuro-evolutionary control of 3-, 6-, and 12-link systems with nominal conditions as well as with sensor noise, actuator noise, and the addition of more complex physics. / Graduation date: 2012

Successful development of mechatronic systems requires a combination of targeted hardware and software design. The compact rescue robot (CRR), a quadruped pneumatically-actuated walking robot that seeks to use the benefits garnered from pneumatic power, is a prime example of such a system. This thesis discusses the development and testing of a simulation that will aid in further design and development of the CRR by enabling users to examine the impacts of pneumatic actuation on a walking robot. However, development of an entirely new dynamic simulation specific to the system is not practical. Instead, the simulation combines a MATLAB/Simulink actuator simulation with a readily available C++ dynamics library. This multi-platform approach results in additional incurred challenges due to the transfer of data between the platforms. As a result, the system developed here is designed in the fashion that provides the best balance of realistic behavior, model integrity, and practicality. An analytically derived actuator model is developed using classical fluid circuit modeling together with nonlinear area and pressure curves to model the valve and a Stribeck-Tanh model to characterize the effects of friction on the cylinder. The valve model is designed in Simulink and validated on a single degree-of-freedom test rig. This actuator model is then interfaced with SrLib, a dynamics library that computes dynamics of the robot and interactions with the environment, and validated through comparisons with a CRR prototype. Conclusions are focused on the final composition of the simulation, its performance and limitations, and the benefits it offers to the system as a whole.

Control of systems requiring direct physical human-robot interaction (pHRI) requires special consideration of the motion, dynamics, and control of both the human and the robot. Humans actively change their dynamic characteristics during motion, and robots should be designed with this in mind. Both the case of humans trying to control haptic robots using physical contact and the case of using wearable robots that must work with human muscles are pHRI systems. Force feedback haptic devices require physical contact between the operator and the machine, which creates a coupled system. This human contact creates a situation in which the stiffness of the system changes based on how the operator modulates the stiffness of their arm. The natural human tendency is to increase arm stiffness to attempt to stabilize motion. However, this increases the overall stiffness of the system, making it more difficult to control and reducing stability. Instability poses a threat of injury or load damage for large assistive haptic devices with heavy loads. Controllers do not typically account for this, as operator stiffness is often not directly measurable. The common solution of using a controller with significantly increased controller damping has the disadvantage of slowing the device and decreasing operator efficiency. By expanding the information available to the controller, it can be designed to adjust a robot's motion based on the how the operator is interacting with it and allow for faster movement in low stiffness situations. This research explored the utility of a system that can estimate operator arm stiffness and compensate accordingly. By measuring muscle activity, a model of the human arm was utilized to estimate the stiffness level of the operator, and then adjust the gains of an impedance-based controller to stabilize the device. This achieved the goal of reducing oscillations and increasing device performance, as demonstrated through a series of user trials with the device. Through the design of this system, the effectiveness of a variety of operator models were analyzed and several different controllers were explored. The final device has the potential to increase the performance of operators and reduce fatigue due to usage, which in industrial settings could translate into better efficiency and higher productivity. Similarly, wearable robots must consider human muscle activity. Wearable robots, often called exoskeleton robots, are used for a variety of tasks, including force amplification, rehabilitation, and medical diagnosis. Force amplification exoskeletons operate much like haptic assist devices, and could leverage the same adaptive control system. The latter two types, however, are designed with the purpose of modulating human muscles, in which case the wearer's muscles must adapt to the way the robot moves, the reverse of the robot adapting to how the human moves. In this case, the robot controller must apply a force to the arm to cause the arm muscles to adapt and generate a specific muscle activity pattern. This related problem is explored and a muscle control algorithm is designed that allows a wearable robot to induce a specified muscle pattern in the wearer's arm. The two problems, in which the robot must adapt to the human's motion and in which the robot must induce the human to adapt its motion, are related critical problems that must be solved to enable simple and natural physical human robot interaction.

Incremental smoothing and mapping (iSAM) is presented, a novel approach to the simultaneous localization and mapping (SLAM) problem. SLAM is the problem of estimating an observer's position from local measurements only, while creating a consistent map of the environment. The problem is difficult because even very small errors in the local measurements accumulate over time and lead to large global errors. iSAM provides an exact and efficient solution to the SLAM estimation problem while also addressing data association. For the estimation problem, iSAM provides an exact solution by performing smoothing, which keeps all previous poses as part of the estimation problem, and therefore avoids linearization errors. iSAM uses methods from sparse linear algebra to provide an efficient incremental solution. In particular, iSAM deploys a direct equation solver based on QR matrix factorization of the naturally sparse smoothing information matrix. Instead of refactoring the matrix whenever new measurements arrive, only the entries of the factor matrix that actually change are calculated. iSAM is efficient even for robot trajectories with many loops as it performs periodic variable reordering to avoid unnecessary fill-in in the factor matrix. For the data association problem, I present state of the art data association techniques in the context of iSAM and present an efficient algorithm to obtain the necessary estimation uncertainties in real-time based on the factored information matrix. I systematically evaluate the components of iSAM as well as the overall algorithm using various simulated and real-world data sets.

We present automatic tools for configuring and deploying multi-robot networks of decentralized, mobile robots. These methods are tailored to the decentralized nature of the multi-robot network and the limited information available to each robot. We present methods for determining if user-defined network tasks are feasible or infeasible for the network, considering the limited range of its sensors. To this end, we define rigid and persistent feasibility and present necessary and sufficient conditions (along with corresponding algorithms) for determining the feasibility of arbitrary, user-defined deployments. Control laws for moving multi-robot networks in acyclic, persistent formations are defined. We also present novel Embedded Graph Grammar Systems (EGGs) for coordinating and deploying the network. These methods exploit graph representations of the network, as well as graph-based rules that dictate how robots coordinate their control. Automatic systems are defined that allow the robots to assemble arbitrary, user-defined formations without any reliance on localization. Further, this system is augmented to deploy these formations at the user-defined, global location in the environment, despite limited localization of the network. The culmination of this research is an intuitive software program with a Graphical User Interface (GUI) and a satellite image map which allows users to enter the desired locations of sensors. The automatic tools presented here automatically configure an actual multi-robot network to deploy and execute user-defined network tasks.

Thesis (MScEng)--Stellenbosch University, 2012. / ENGLISH ABSTRACT: Simultaneous localization and mapping (SLAM) is vital for autonomous robot navigation. The robot must build a map of its environment while tracking its own motion through that map. Although many solutions to this intricate problem have been proposed, one of the most prominent issues that still needs to be resolved is to accurately measure and track landmarks over time. In this thesis we investigate the use of stereo vision for this purpose. In order to find landmarks in images we explore the use of two feature detectors: the scale-invariant feature transform (SIFT) and speeded-up robust features (SURF). Both these algorithms find salient points in images and calculate a descriptor for each point that is invariant to scale, rotation and illumination. By using the descriptors we match these image features between stereo images and use the geometry of the system to calculate a set of 3D landmark measurements. A Taylor approximation of this transformation is used to derive a Gaussian noise model for the measurements. The measured landmarks are matched to landmarks in a map to find correspondences. We find that this process often incorrectly matches ambiguous landmarks. To find these mismatches we develop a novel outlier detection scheme based on the random sample consensus (RANSAC) framework. We use a similarity transformation for the RANSAC model and derive a probabilistic consensus measure that takes the uncertainties of landmark locations into account. Through simulation and practical tests we find that this method is a significant improvement on the standard approach of using the fundamental matrix. With accurately identified landmarks we are able to perform SLAM. We investigate the use of three popular SLAM algorithms: EKF SLAM, FastSLAM and FastSLAM 2. EKF SLAM uses a Gaussian distribution to describe the systems states and linearizes the motion and measurement equations with Taylor approximations. The two FastSLAM algorithms are based on the Rao-Blackwellized particle filter that uses particles to describe the robot states, and EKFs to estimate the landmark states. FastSLAM 2 uses a refinement process to decrease the size of the proposal distribution and in doing so decreases the number of particles needed for accurate SLAM. We test the three SLAM algorithms extensively in a simulation environment and find that all three are capable of very accurate results under the right circumstances. EKF SLAM displays extreme sensitivity to landmark mismatches. FastSLAM, on the other hand, is considerably more robust against landmark mismatches but is unable to describe the six-dimensional state vector required for 3D SLAM. FastSLAM 2 offers a good compromise between efficiency and accuracy, and performs well overall. In order to evaluate the complete system we test it with real world data. We find that our outlier detection algorithm is very effective and greatly increases the accuracy of the SLAM systems. We compare results obtained by all three SLAM systems, with both feature detection algorithms, against DGPS ground truth data and achieve accuracies comparable to other state-of-the-art systems. From our results we conclude that stereo vision is viable as a sensor for SLAM. / AFRIKAANSE OPSOMMING: Gelyktydige lokalisering en kartering (simultaneous localization and mapping, SLAM) is ’n noodsaaklike proses in outomatiese robot-navigasie. Die robot moet ’n kaart bou van sy omgewing en tegelykertyd sy eie beweging deur die kaart bepaal. Alhoewel daar baie oplossings vir hierdie ingewikkelde probleem bestaan, moet een belangrike saak nog opgelos word, naamlik om landmerke met verloop van tyd akkuraat op te spoor en te meet. In hierdie tesis ondersoek ons die moontlikheid om stereo-visie vir hierdie doel te gebruik. Ons ondersoek die gebruik van twee beeldkenmerk-onttrekkers: scale-invariant feature transform (SIFT) en speeded-up robust features (SURF). Altwee algoritmes vind toepaslike punte in beelde en bereken ’n beskrywer vir elke punt wat onveranderlik is ten opsigte van skaal, rotasie en beligting. Deur die beskrywer te gebruik, kan ons ooreenstemmende beeldkenmerke soek en die geometrie van die stelsel gebruik om ’n stel driedimensionele landmerkmetings te bereken. Ons gebruik ’n Taylor- benadering van hierdie transformasie om ’n Gaussiese ruis-model vir die metings te herlei. Die gemete landmerke se beskrywers word dan vergelyk met dié van landmerke in ’n kaart om ooreenkomste te vind. Hierdie proses maak egter dikwels foute. Om die foutiewe ooreenkomste op te spoor het ons ’n nuwe uitskieterherkenningsalgoritme ontwikkel wat gebaseer is op die RANSAC-raamwerk. Ons gebruik ’n gelykvormigheidstransformasie vir die RANSAC-model en lei ’n konsensusmate af wat die onsekerhede van die ligging van landmerke in ag neem. Met simulasie en praktiese toetse stel ons vas dat die metode ’n beduidende verbetering op die standaardprosedure, waar die fundamentele matriks gebruik word, is. Met ons akkuraat geïdentifiseerde landmerke kan ons dan SLAM uitvoer. Ons ondersoek die gebruik van drie SLAM-algoritmes: EKF SLAM, FastSLAM en FastSLAM 2. EKF SLAM gebruik ’n Gaussiese verspreiding om die stelseltoestande te beskryf en Taylor-benaderings om die bewegings- en meetvergelykings te lineariseer. Die twee FastSLAM-algoritmes is gebaseer op die Rao-Blackwell partikelfilter wat partikels gebruik om robottoestande te beskryf en EKF’s om die landmerktoestande af te skat. FastSLAM 2 gebruik ’n verfyningsproses om die grootte van die voorstelverspreiding te verminder en dus die aantal partikels wat vir akkurate SLAM benodig word, te verminder. Ons toets die drie SLAM-algoritmes deeglik in ’n simulasie-omgewing en vind dat al drie onder die regte omstandighede akkurate resultate kan behaal. EKF SLAM is egter baie sensitief vir foutiewe landmerkooreenkomste. FastSLAM is meer bestand daarteen, maar kan nie die sesdimensionele verspreiding wat vir 3D SLAM vereis word, beskryf nie. FastSLAM 2 bied ’n goeie kompromie tussen effektiwiteit en akkuraatheid, en presteer oor die algemeen goed. Ons toets die hele stelsel met werklike data om dit te evalueer, en vind dat ons uitskieterherkenningsalgoritme baie effektief is en die akkuraatheid van die SLAM-stelsels beduidend verbeter. Ons vergelyk resultate van die drie SLAM-stelsels met onafhanklike DGPS-data, wat as korrek beskou kan word, en behaal akkuraatheid wat vergelykbaar is met ander toonaangewende stelsels. Ons resultate lei tot die gevolgtrekking dat stereo-visie ’n lewensvatbare sensor vir SLAM is.

Thesis (MScEng) Stellenbosch University, 2014 / ENGLISH ABSTRACT: Teleoperation as a field has seen much change since its inception in the early 1940s with Dr. Raymond Goertz producing the first teleoperation system for manipulating radioactive materials. With advances in core and supporting technologies, the systems have grown in complexity and capability, allowing users to perform tasks anywhere in the world irrespective of physical distance. The feasibility of such systems has increased as the drive for use of telepresence robots, exploration robots as in space exploration, search and rescue robots and military systems such as UAVs and UGVs gain popularity. This prompted the development of a proof of concept modular, user centred telerobotic system. The current project is the second iteration in the development process. Teleoperation and more specifically telerobotic systems pose a challenge for many system developers. This may be a result of complexity or the wide assortment of knowledge areas that developers must master in order to deliver the final system. Developers have to balance system usability, user requirements, technical design and performance requirements. Several developmental process models are considered in context of Engineering Management (EM). A larger Systems Engineering developmental process is used, with focus on the primary and supportive EM components. The author used a hybrid developmental model that is user focussed in its approach, the User-Centred Systems Design (UCSD) methodology was adopted as the primary model for application within the two distinct developmental categories. The first category hardware and system integration utilised the UCSD model as is. The second - Software development - relied on the use of agile models, rapid application development (RAD) and extreme programming (XP) were discussed with XP being chosen as it could easily incorporate UCSD principles in its development process. Hardware systems development consisted of mechanical design of end-effectors, configuration management and design, as well as haptic and visual feedback systems design for the overall physical system. Also included is the physical interface design of the input (master) cell. Further software development was broken into, three sections, the first and most important was the graphical user interface, haptic control system with kinematic model and video feedback control. The force following and matching characteristics of the system were tested and were found to show an improvement over the previous implementation. The force magnitude error at steady state was reduced by 10%. While there was a dramatic improvement in system response, the rise time was reduced by a factor 10. The system did however show a decrease in angular accuracy, which was attributed to control system limitations. Further human-factor analysis experiments were conducted to test the system in two typical use-case scenarios. The first was a planar experiment and the second a 3D placement task. The factors of interest identified were field-of-view, feedback vision mode, and input modality. Heuristic performance indicators such as time-to-completion and number of collisions for a given task were measured. System performance was only showed significant improvement when used with haptic control. This shows that the research into haptic control systems will prove to be valuable in producing usable systems. The vision factor analysis failed to yield significant results, although they were useful in the qualitative systems analysis. The feedback from post-experimentation questionnaires showed that users prefer the Point of View as a field of view and 2D viewing over 3D viewing, while the haptic input modality was preferred. The results from the technical verification process can be used in conjunction with insights gained from user preference and human-factor analysis to provide guidance for future telerobotic systems development at Stellenbosch University. / AFRIKAANSE OPSOMMING: Telewerksverigting as ’n gebied het al vele veranderinge ondergaan vandat die eerste stelsels deur Dr. Raymond Goertz geimplementeer was in die vroeë 1940s vir die hantering van radioaktiewe materiale. Met vordering in kern en ondersteunende tegnologieë, het die telewerksverigtingstelsels toegeneem in kompleksiteit asook gevorder in vermoeënsvaardigheid, wat gebruikers in staat stel om take te verrig vanuit enige plek op aarde, ongeag die fisiese afstand wat die gebruiker en die werksarea skei. Die lewensvatbaarheid van hierdie stelsels het ook toegeneem weens die belangstelling in teleteenwoordigheidrobotte, ruimtevaardige-robotte, reddings-robotte en militêre-robotte soos onbemandelug- voertuie (OLV) en onbemande-grond-voertuie(OGV). As gevolg van die belangstelling in telerobotiese stelsels is die ontwikkeling van ’n modulêre, gebruikers-gesentreerde telewerksverigting stelsel onderneem. Die huidige projek is ’n tweede iterasie hiervan. Telewerksverigting, en meer spesifiek, telerobotika stelsels ontwikelling, vereis dat stelselontwikkelaars ’n verskeidenheid kennisareas bemeester. Die ontwikkelaar moet ’n belans vind tussen gebruiker vereistes, bruikbaarheid asook tegniese ontwerp en prestasie vereistes. Menigde ontwikkelingsproses modelle is oorweeg en behandel in die konteks van Ingenieursbestuur (IB). ’n Stelselsontwikkeling proses is gevolg met ’n fokus op primêre en ondersteunende IB komponente. ’n Gemengde ontwikkeling is toegepass tot die projek wat die gebruiker as ’n hoof komponent van die stelsel in ag neem. Die oorhoofse ontwikkelingsmodel is die User-centred Systems Design (UCSD) proses, wat vir beide hardeware en sagteware ontwikkeling gebruik is. Vir die hardeware ontwikkeling is die UCSD toegepas soos dit uiteengesit is in die literatuur. Die sagteware ontwikkeling is voltooi met behulp van ratse metodes, “Rapid Application Development” RAD en “Extreme Programming” (XP) was oorweeg en XP was gekies as ontwikkelingsmodel. XP was die natuurlike keuse weens die gemak waarmee UCSD metodes en prinsiepe kon geinkorporeer word in die ontwikkelings proses. Hardeware onwikkeling het bestaan uit meganiese ontwerp, manipulasiegereedskap ontwerp, konfigurasie bestuur en ontwikkeling asook haptiese en visueleterugvoer stelselsontwerp van die fisiese stelsel insluitend die fisiese koppelvlakontwerp van die meester sel. Verder is sagtewareontwerp opgedeel in ’n haptiesebeheerstel met ’n kinematiese model ontwikkeling, videoterugvoerbeheer en gebruikersintervlak ontwerp. Die vermoëe van die stelsel om krag insette na te boots was verbeter met ’n gestadigde verbetering van 10%. Die reaksietyd van die stelsel is verbeter met ’n faktor van 10. Die stelsel het ’n verswakking getoon in die algehele hoekakkuraatheid, die oorsprong van die verswakking kan aan die beheerstelsel teogeken word. Verdere menslikefaktoranalise eksperimente is voltooi om die stelsel in twee tipiese gebruikgeval scenario’s te toets. Die eerste, ’n platvlak-eksperiment en die tweede ’n 3D plasingingstaak eksperiment. Die faktore van belang is ïdentifiseer as, visie-veld, terugvoervisie modus en insette modaliteit. Heuristiese prestasie-aanwysers soos tyd-tot-voltooiing en die aantal botsings vir ’n gegewe taak is gemeet. Stelselprestasie het slegs aansienlike verbetering getoon wanneer die stelsel met die haptiesebeheer modus bedryf word. Die visiefaktor ontleding het geen noemenswaardige resultate opgelewer nie. Terugvoervorms was na elke eksperiment voltooi. Vraelyste het getoon dat gebruikers die oogpunt van ’n lae hoek en 2D video oor 3D video verkies, terwyl die haptic beheer modaliteit verkies word.