This project first explored various methods of designing a communication and control system for an AGV. It then implemented a SOA based communication system in ROS on the selected AGV. The ROS package created in the project implemented functions of the Aria and ArNetworking libraries from Adept. The next step of the project implemented the functions of teleoperation, mapping and transfer of maps and navigation from Aria into ROS. The packages built implemented these functions in different ways to test the best method to transfer Aria functions into ROS. A generic set of rules were then formulated that aided the conversion of these functions for users unfamiliar with either of the two packages (ROS and Aria).

Resource allocation in control and communication systems constitutes the distribution of (finite) system resources in a way that achieves maximum system functionality and or cost effectiveness. Specific resource allocation problems in subband coding, Discrete Multi-tone modulation based systems and autonomous multi-agent control are addressed in this thesis. In subband coding, the number of bits used (out of a target bit budget) to code a sub- band signal are allocated in a way that minimizes the coding distortion. In Discrete Multi-tone modulation based systems, high bit rate streams are split into several parallel lower rate streams. These individual data streams are transmitted over different subchannels. Given a target bit rate, the goal of resource allocation is to distribute the bits among the different subchannels such that the total transmitted power is minimized. The last problem is achieving stable control of a fleet of autonomous agents by utilizing the available communication resources (such as transmitted Power and bandwidth) as effectively as possible. We present an efficient bit loading algorithm that applies to both subband coding and single-user multicarrier communication system. The goal is to effect an optimal distribution of B bits among N subchannels (subbands) to achieve a minimum transmitted power (distortion error variance) for multicarrier (subband coding) systems. All the algorithms in literature, except a few (which provides a suboptimal solution), have run times that increase with B. By contrast, we provide an algorithm that solves the aforementioned problems exactly and with a complexity (given by O(N log(N)),) which is dependent only on N. Bit loading in multi-user multicarrier systems not only involves the distribution of bit rates across the subchannels but also the assignment of these subchannels to different users. The motivation for studying suboptimal bit allocation is underscored by implicit and explicit claims made in some of the papers which present suboptimal bit loading algorithms, without a formal proof, that the underlying problem is NP-hard. Consequently, for no other reason than the sake of completeness, we present a proof for NP-hardness of the multiuser multicarrier bit loading problem, thereby formally justifying the search for suboptimal solutions. There has been a growing interest in the area of cooperative control of networks of mobile autonomous agents. Applications for such a set up include organization of large sensor networks, air traffic control, achieving and maintaining formations of unmanned vehicles operating under- water, air traffic control etc. As in Abel et al, our goal is to devise control laws that, require minimal information exchange between the agents and minimal knowledge on the part of each agent of the overall formation objective, are fault tolerant, scalable, and easily reconfigurable in the face of the loss or arrival of an agent, and the loss of a communication link. A major drawback of the control law proposed in Abel et al is that it assumes all agents can exchange information at will. This is fine if agents acquire each others state information through straightforward sensing. If however, state information is exchanged through broadcast commu- nication, this assumption is highly unrealistic. By modifying the control law presented in Abel et al, we devise a scheme that allows for a sharing of the resource, which is the communication channel, but also achieves the desired formation stably. Accordingly we modify the control law presented in [23] to be compatible with networks constrained by MAC protocols.

Šiame darbe tiriama sritis yra kompiuteriniuose žaidimuose naudojamas dirbtinis intelektas. Konkrečiai gilinamasi į metodus, kurie valdo daugybę veikėjų žaidime, siekiančių tam tikro tikslo. Dėl konkretiems žaidimams unikalių mechanikų, šie metodai paprastai būna labai glaudžiai susiję su žaidimo aplinka ir taisyklėmis. Tyrimo tikslas yra sukurti ir ištirti metodą, skirtą daugybės veikėjų pajėgų valdymui ir jų veiksmų modeliavimui virtualioje aplinkoje. Analizuojami metodai skirti pavienių veikėjų veiksmų įvertinimui ir modeliavimui, metodai skirti optimalių sprendimų žinių bazei sudaryti ir metodai toms žinioms pritaikyti paskirstant veikėjus aplinkoje. Pagal analizės rezultatus sukuriamas projektas daugelio veikėjų valdymui realiu laiku virtualioje aplinkoje. Lokalių veikėjų veiksmų modeliavimui naudojamas procedūrinis taktinių veiksmų parinkimo metodas. Veikėjų judėjimui aplinkoje modeliuoti naudojamas neuroninis tinklas. Jis apmokomas pagal genetiniu algoritmu sudarytus optimalius sprendimus. Suprojektuota sistema realizuojama ir testuojama. Atliekamas eksperimentas su sistemos veikimo metu gautais rezultatais. Eksperimente nustatoma, kad šis sprendimo būdas gali tikslingai reaguoti į situacijas, susidarančias realaus laiko virtualioje aplinkoje, ir modeliuoti veikėjų veiksmus joje. / The research area of this paper is artificial intelligence used in computer games. Specifically it is focused on methods for controlling a group of agents with a specific goal. Because of the uniqueness of individual game mechanics, those kinds of methods are usually closely related to that games environment and rules. The goal of this study is to design and test a method that could control a group of multiple agents in a virtual environment. Methods for evaluating and selecting individual agent actions in a local environment, for gathering a database of optimal solutions and for applying that knowledge in distributing agents across the environment are analyzed. A design for controlling the actions of multiple agents in a real time virtual environment is designed, based on the results. Dynamic procedural combat tactics is used to model individual agent actions in a local environment. A neural network is used to model the movement of multiple agents in an environment. It is trained using optimal solutions, generated by a genetic algorithm. Designed system is implemented and tested. Using data that the system generates, an experiment is conducted. It shows that this solution is capable of correctly reacting to situations, occurring in a real time virtual environment, and of modeling multiple agent actions in it.

Soft robots are inherently safer than traditional robots due to their compliance and high power density ratio resulting in lower accidental impact forces. Thus they are a natural option for human-robot interaction. This thesis specifically looked at human-robot co-manipulation which is defined as a human and a robot working together to move an object too large or awkward to be safely maneuvered by a single agent. To better understand how humans communicate while co-manipulating an object, this work looked at haptic interaction of human-human dyadic co-manipulation trials and studied some of the trends found in that interaction. These trends point to ways robots can effectively work with human partners in the future. Before successful human-robot co-manipulation with large-scale soft robots can be achieved, low-level joint angle control is needed. Low-level model predictive control of soft robot joints requires a sufficiently accurate model of the system. This thesis introduces a recursive Newton-Euler method for deriving the dynamics that is sufficiently accurate and accounts for flexible joints in an intuitive way. This model has been shown to be accurate to a median absolute error of 3.15 degrees for a three-link three-joint six degree of freedom soft robot arm. Once a sufficiently accurate model was developed, a gain-based evolutionary model predictive control (MPC) technique was formulated based on a previous evolutionary MPC technique. This new method is referred to as model evolutionary gain-based predictive control or MEGa-PC. This control law is compared to nonlinear evolutionary model predictive control (NEMPC). The new technique allows intentionally decreasing the control frequency to 10 Hz while maintaining control of the system. This is proven to help MPC solve more difficult problems by having the ability to extend the control horizon. This new controller is also demonstrated to work well on a three-joint three-link soft robot arm. Although complete physical human-robot co-manipulation is outside the scope of this thesis, this thesis covers three main building blocks for physical human and soft robot co-manipulation: human-human haptic communication, soft robot modeling, and model evolutionary gain-based predictive control.

We present a motion control framework which allows a group of robots to work together to decide upon their motions by minimizing a collective cost without any central computing component or any one agent performing a large portion of the computation. When developing distributed control algorithms, care must be taken to respect the limited computational capacity of each agent as well as respect the information and communication constraints of the network. To address these issues, we develop a distributed, behavior-based model predictive control (MPC) framework which alleviates the computational difficulties present in many distributed MPC frameworks, while respecting the communication and information constraints of the network. In developing the multi-agent control framework, we make three contributions. First, we develop a distributed optimization technique which respects the dynamic communication restraints of the network, converges to a collective minimum of the cost, and has transients suitable for robot motion control. Second, we develop a behavior-based MPC framework to control the motion of a single-agent and apply the framework to robot navigation. The third contribution is to combine the concepts of distributed optimization and behavior-based MPC to develop the mentioned multi-agent behavior-based MPC algorithm suitable for multi-robot motion control.

<div>In this thesis, we focus on the problem of estimating an unknown quantity of interest, when the information required to do so is dispersed over a network of agents. In particular, each agent in the network receives sequential observations generated by the unknown quantity, and the collective goal of the network is to eventually learn this quantity by means of appropriately crafted information diffusion rules. The abstraction described above can be used to model a variety of problems ranging from environmental monitoring of a dynamical process using autonomous robot teams, to statistical inference using a network of processors, to social learning in groups of individuals. The limited information content of each agent, coupled with dynamically changing networks, the possibility of adversarial attacks, and constraints imposed by the communication channels, introduce various unique challenges in addressing such problems. We contribute towards systematically resolving some of these challenges.</div><div><br></div><div>In the first part of this thesis, we focus on tracking the state of a dynamical process, and develop a distributed observer for the most general class of LTI systems, linear measurement models, and time-invariant graphs. To do so, we introduce the notion of a multi-sensor observable decomposition - a generalization of the Kalman observable canonical decomposition for a single sensor. We then consider a scenario where certain agents in the network are compromised based on the classical Byzantine adversary model. For this worst-case adversarial setting, we identify certain fundamental necessary conditions that are a blend of system- and network-theoretic requirements. We then develop an attack-resilient, provably-correct, fully distributed state estimation algorithm. Finally, by drawing connections to the concept of age-of-information for characterizing information freshness, we show how our framework can be extended to handle a broad class of time-varying graphs. Notably, in each of the cases above, our proposed algorithms guarantee exponential convergence at any desired convergence rate.</div><div><br></div><div>In the second part of the thesis, we turn our attention to the problem of distributed hypothesis testing/inference, where each agent receives a stream of stochastic signals generated by an unknown static state that belongs to a finite set of hypotheses. To enable each agent to uniquely identify the true state, we develop a novel distributed learning rule that employs a min-protocol for data-aggregation, as opposed to the large body of existing techniques that rely on "belief-averaging". We establish consistency of our rule under minimal requirements on the observation model and the network structure, and prove that it guarantees exponentially fast convergence to the truth with probability 1. Most importantly, we establish that the learning rate of our algorithm is network-independent, and a strict improvement over all existing approaches. We also develop a simple variant of our learning algorithm that can account for misbehaving agents. As the final contribution of this work, we develop communication-efficient rules for distributed hypothesis testing. Specifically, we draw on ideas from event-triggered control to reduce the number of communication rounds, and employ an adaptive quantization scheme that guarantees exponentially fast learning almost surely, even when just 1 bit is used to encode each hypothesis. </div>

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Thesis (MScEng (Mechanical and Mechatronic Engineering))--University of Stellenbosch, 2010. / ENGLISH ABSTRACT: This work considers the control of reconfigurable assembly systems using a welding assembly system as a case study. The assembly system consists of a pallet magazine, a feeding system, an inspection and removal system, a welding system and a conveyor. The aim of the work is to compare PC and PLC as controllers, as well as to compare two different approaches to reconfigurable control. The control system of the pallet magazine was developed using a PC and a PLC. The PC control was programmed using Visual C#, while the PLC was programmed in Ladder Logic using Siemens S-300 STEP7. The two controllers were compared based on the attributes that measure the quality of a controller's software, which include its capability, availability, usability and adaptability. The approaches to reconfigurable control considered were the agent-based methodology and the IEC 61499 distributed control methodology, both of which were applied to the feeding system. The agent-based control system was implemented using the JADE agent platform, while the IEC 61499 distributed control system was implemented using the FBDK software kit. These two methods were compared based on the characteristics of a reconfigurable system, which include the system's modularity, integrability, convertibility, diagnosability, customization and scalability. The result obtained in comparing the PC to the PLC shows that the PLC performs better in terms of capability, availability and usability, while the PC performs better in terms of adaptability. Also, the result of the comparison between the agent-based control system and the IEC 61499 distributed control system shows that the agent-based control system performs better in terms of integrability, diagnosability and scalability, while the IEC 61499 distributed control system performs better in terms of modularity and customization. They are, however, on a par in terms of convertibility. / AFRIKAANSE OPSOMMING: Hierdie werk beskou die beheer van herkonfigureerbare monteringstelsels met 'n sweismonteringstelsel as gevallestudie. Die monteringstelsel bestaan uit 'n paletmagasyn, 'n voerstelsel, 'n inspeksie-en- verwyderingstelsel, 'n sweisstelsel en 'n voerband. Die mikpunt van die werk is om persoonlike rekenaars (PCs) en programmeerbare-logikabeheerders (PLCs) as beheerders te vergelyk, asook om twee verskillende benaderings tot herkonfigureerbare beheer te vergelyk. Die beheerstelsel van die paletmagasyn is ontwikkel met 'n PC en 'n PLC. Die PC-beheer is in Visual C# geprogrammeer, terwyl die PLC in leerlogika met Siemens S-300 STEP7 geprogrammeer is. Die twee beheerders is vergelyk in terme van die eienskappe wat die kwaliteit van 'n beheerder se sagteware meet en sluit in vermoë, beskikbaarheid, bruikbaarheid en aanpasbaarheid. Die benaderings tot herkonfigureerbare beheer wat oorweeg is, is die agent-gebaseerde metodologie en die IEC 61499 verspreide-beheermetodologie. Beide is op die voerstelsel toegepas. Die agent-gebaseerde beheerstelsel is geïmplementeer met behulp van die JADE agent-platform, terwyl die IEC 61499 verspreide stelsel geïmplementeer is met behulp van die FBDK sagteware-stel. Hierdie twee metodes se vergelyking is gebaseer op die eienskappe van 'n herkonfigureerbare stelsel, waarby die stelsel se modulariteit, integreerbaarheid, diagnoseerbaarheid, pasmaakbaarheid en skaleerbaarheid ingesluit is. Die resultate wat in die vergelyking tussen die PC en PLC verkry is, toon dat die PLC beter vaar in terme van vermoë, beskikbaarheid en bruikbaarheid, terwyl die PC beter vaar in terme van aanpasbaarheid. Die resultaat van die vergelyking tussen die agent-gebaseerde beheerstelsel en die IEC 61499 verspreide beheerstelsel wys dat die agent-gebaseerde beheerstelsel beter vaar in terme van integreerbaarheid, diagnoseerbaarheid en skaleerbaarheid, terwyl die IEC 61499 verspreide beheerstelsel beter vaar in terme van modulariteit en pasmaakbaarheid. Hulle is egter vergelykbaar in terme van omskepbaarheid.

Swarm robotics for music is a relatively new way to explore algorithmic composition as well as new modes of human robot interaction. This work outlines a strategy for making music with a robotic swarm constrained by acoustic sound, rhythmic music using sequencers, motion causing changes in the music, and finally human and swarm interaction. Two novel simulation programs are created in this thesis: the first is a multi-agent simulation designed to explore suitable parameters for motion to music mappings as well as parameters for real time interaction. The second is a boid-based robotic swarm simulation that adheres to the constraints established, using derived parameters from the multi-agent simulation: orientation, number of neighbors, and speed. In addition, five interaction modes are created that vary along an axis of direct and indirect forms of human control over the swarm motion. The mappings and interaction modes of the swarm robot simulation are evaluated in a user study involving music technology students. The purpose of the study is to determine the legibility of the motion to musical mappings and evaluate user preferences for the mappings and modes of interaction in problem solving and in open-ended contexts. The findings suggest that typical users of a swarm robot system do not necessarily prefer more inherently legible mappings in open-ended contexts. Users prefer direct and intermediate modes of interaction in problem solving scenarios, but favor intermediate modes of interaction in open-ended ones. The results from this study will be used in the design and development of a new swarm robotic system for music that can be used in both contexts.