Les robots sont tout près d'arriver chez nous. Mais avant cela, ils doivent acquérir la capacité d'interagir physiquement avec les humains, de manière sûre et efficace. De telles capacités sont indispensables pour qu'il puissent vivre parmi nous, et nous assister dans diverses tâches quotidiennes, comme porter une meuble. Dans cette thèse, nous avons pour but de doter le robot humanoïde bipède HRP-2 de la capacité à effectuer des actions haptiques en commun avec l'homme. Dans un premier temps, nous étudions comment des dyades humains collaborent pour transporter un objet encombrant. De cette étude, nous extrayons un modèle global de primitives de mouvement que nous utilisons pour implémenter un comportement proactif sur le robot HRP-2, afin qu'il puisse effectuer la même tâche avec un humain. Puis nous évaluons les performances de ce schéma de contrôle proactif au cours de tests utilisateur. Finalement, nous exposons diverses pistes d'évolution de notre travail: la stabilisation d'un humanoïde à travers l'interaction physique, la généralisation du modèle de primitives de mouvements à d'autres tâches collaboratives et l'inclusion de la vision dans des tâches collaboratives haptiques.

This thesis presents an adaptive and reconfigurable mobile robot: the Dual Mobile Robot (DMR). It is driven by two adaptive track-wheel driving modules that combine wheels and tracks to allow real-time interchangeability according to terrain condition. The DMR can automatically convert from a wheel-based robot into a track-based robot by rotating the track-wheel driving modules by 90 degrees, either only tracks or wheels contact with the ground without any interference. It can be driven as a wheel-based robot when operating over a paved road to achieve higher speed and low energy consumption, and as a track-based robot over uneven terrain. In addition, unlike most state-of-the-art mobile robot designs that have an integrated architecture, this design provides a modular architecture which allows modifications and upgrades to be performed via simple replacements or local changes of modules. To establish the modular architecture, this research utilized a unique design paradigm, “Design for product adaptability”. A function-based design process for product adaptability has been conducted in the conceptual design stage. By following the design process, two types of design alternatives of the DMR have been created. After the best product configuration was chosen through evaluation and prioritization, the selected configuration has been implemented by detail design. The DMR prototype was developed and tested to demonstrate its adaptability and advanced mobility functions in real-world environments. The experimental results successfully validated the hypothesis of the proposed robot with its track-wheel interchangeable ability, significantly exceeding the capability of other existing systems.

This thesis presents an adaptive and reconfigurable mobile robot: the Dual Mobile Robot (DMR). It is driven by two adaptive track-wheel driving modules that combine wheels and tracks to allow real-time interchangeability according to terrain condition. The DMR can automatically convert from a wheel-based robot into a track-based robot by rotating the track-wheel driving modules by 90 degrees, either only tracks or wheels contact with the ground without any interference. It can be driven as a wheel-based robot when operating over a paved road to achieve higher speed and low energy consumption, and as a track-based robot over uneven terrain. In addition, unlike most state-of-the-art mobile robot designs that have an integrated architecture, this design provides a modular architecture which allows modifications and upgrades to be performed via simple replacements or local changes of modules. To establish the modular architecture, this research utilized a unique design paradigm, “Design for product adaptability”. A function-based design process for product adaptability has been conducted in the conceptual design stage. By following the design process, two types of design alternatives of the DMR have been created. After the best product configuration was chosen through evaluation and prioritization, the selected configuration has been implemented by detail design. The DMR prototype was developed and tested to demonstrate its adaptability and advanced mobility functions in real-world environments. The experimental results successfully validated the hypothesis of the proposed robot with its track-wheel interchangeable ability, significantly exceeding the capability of other existing systems.

The task assignment problem is one of the fundamental combinatorial optimization problems. It has been extensively studied in operation research, management science, computer science and robotics. Task assignment problems arise in various applications of multi-robot systems (MRS), such as environmental monitoring, disaster response, extraterrestrial exploration, sensing data collection and collaborative autonomous manufacturing. In these MRS applications, there are realistic constraints on robots and tasks that must be taken into account both from the modeling perspective and the algorithmic perspective. From the modeling aspect, such constraints include (a) Task group constraints: where tasks form disjoint groups and each robot can be assigned to at most one task in each group. One example of the group constraints comes from tightly-coupled tasks, where multiple micro tasks form one tightly-coupled macro task and need multiple robots to perform each simultaneously. (b) Task deadline constraints: where tasks must be assigned to meet their deadlines. (c) Dynamically-arising tasks: where tasks arrive dynamically and the payoffs of future tasks are unknown. Such tasks arise in scenarios like searchrescue, where new victims are found dynamically. (d) Robot budget constraints: where the number of tasks each robot can perform is bounded according to the resource it possesses (e.g., energy). From the solution aspect, there is often a need for decentralized solution that are implemented on individual robots, especially when no powerful centralized controller exists or when the system needs to avoid single-point failure or be adaptive to environmental changes. Most existing algorithms either do not consider the above constraints in problem modeling, are centralized or do not provide formal performance guarantees. In this thesis, I propose methods to address these issues for two classes of problems, namely, the constrained linear assignment problem and constrained generalized assignment problem. Constrained linear assignment problem belongs to P, while constrained generalized assignment problem is NP-hard. I develop decomposition-based distributed auction algorithms with performance guarantees for both problem classes. The multi-robot assignment problem is decomposed into an optimization problem for each robot and each robot iteratively solving its own optimization problem leads to a provably good solution to the overall problem. For constrained linear assignment problem, my approaches provides an almost optimal solution. For constrained generalized assignment problem, I present a distributed algorithm that provides a solution within a constant factor of the optimal solution. I also study the online version of the task allocation problem with task group constraints. For the online problem, I prove that a repeated greedy version of my algorithm gives solution with constant factor competitive ratio. I include simulation results to evaluate the average-case performance of the proposed algorithms. I also include results on multi-robot cooperative package transport to illustrate the approach.

The subject of this thesis is the design and practical application of a model-based controller with variable structure control (VSC). Robot manipulators are highly non-linear systems, however they form a specific class in the non-linear group. Exact mathematical descriptions of the robot dynamics can be achieved and further, robot manipulators have specific useful properties that can be used for the design of advanced controllers. The inclusion of the inverse dynamic description of the robot manipulator as a feedforward term of the controller (model-based controller) is used to transform two non-linear systems i.e. the controller and the robot, into one linear system. The limitation of this technique arises from the accuracy of the inverse dynamic model. The linearisation only takes place if the model is known exactly. To deal with the uncertainties that arise in the model, a control methodology based on variable structure control is proposed. The design of the controller is based on a Lyapunov approach and engineering considerations of the robot. A candidate Lyapunov function of a pseudo-energy form is selected to start the controller design. The general form of the controller is selected to satisfy the negative definiteness of the Lyapunov function. The initial uncertainties between the actual robot dynamics and the model used in the controller are dealt with using a classical VSC regulator. The deficiencies of this approach are evident however because of the chattering phenomenum. The model uncertainties are examined from an engineering point of view and adjustable bounds are then devised for the VSC regulator, and simulations confirm a reduction in the chattering. Implementation on the SPRINTA robot reveals further limitations in the proposed methodology and the bound adjustment is enhanced to take into account the position of the robot and the tracking errors. Two controllers based on the same principle are then obtained and their performances are compared to a PID controller, for three types of trajectory. Tests reveal the superiority of the devised control methodology over the classic PID controller. The devised controller demonstrates that the inclusion of the robot dynamics and properties in the controller design with adequate engineering considerations lead to improved robot responses.

Most current space robotics vehicles use solar energy as their prime energy source. In spherical robotic vehicles the use of solar cells is very restricted. Focusing on the particular problem, an improved method to generate electrical power will be developed; the innovation is the use of an internal pendulum-generator mechanism to generate electrical power while the ball is rolling. This concept will enable spherical robots on future long-duration planetary exploration missions. Through a developed proof-of-concept prototype, inspired by the Russian thistle plant, or tumbleweed, this thesis will demonstrate power generation capabilities of such a mechanism. Furthermore, it will also present and validate a parametric analytical model that can be used in future developments as a design tool to quantify power and define design parameters. The same model was used to define the design parameters and power generation capabilities of such a system in Martian environment.

Most current space robotics vehicles use solar energy as their prime energy source. In spherical robotic vehicles the use of solar cells is very restricted. Focusing on the particular problem, an improved method to generate electrical power will be developed; the innovation is the use of an internal pendulum-generator mechanism to generate electrical power while the ball is rolling. This concept will enable spherical robots on future long-duration planetary exploration missions. Through a developed proof-of-concept prototype, inspired by the Russian thistle plant, or tumbleweed, this thesis will demonstrate power generation capabilities of such a mechanism. Furthermore, it will also present and validate a parametric analytical model that can be used in future developments as a design tool to quantify power and define design parameters. The same model was used to define the design parameters and power generation capabilities of such a system in Martian environment.

Model-based control strategies for robot manipulators can present numerous performance advantages when an accurate model of the system dynamics is available. In practice, obtaining such a model is a challenging task which involves modeling such physical processes as friction, which may not be well understood and difficult to model. Furthermore, uncertainties in the physical parameters of a system may be introduced from significant discrepancies between the manufacturer data and the actual system. Traditionally, adaptive and robust control strategies have been developed to deal with parametric uncertainty in the dynamic model, but often require knowledge of the structure of the dynamics. Recent approaches to model-based manipulator control involve data-driven learning of the inverse dynamics relationship, eliminating the need for any a-priori knowledge of the system model. Locally Weighted Projection Regression (LWPR) has been proposed for learning the inverse dynamics function of a manipulator. Due to its use of simple local, linear models, LWPR is suitable for online and incremental learning. Although global regression techniques such as Gaussian Process Regression (GPR) have been shown to outperform LWPR in terms of accuracy, due to its heavy computational requirements, GPR has been applied mainly to offline learning of inverse dynamics. More recent efforts in making GPR computationally tractable for real-time control have resulted in several approximations which operate on a select subset, or sparse representation of the entire training data set. Despite the significant advancements that have been made in the area of learning control, there has not been much work in recent years to evaluate these newer regression techniques against traditional model-based control strategies such as adaptive control. Hence, the first portion of this thesis provides a comparison between a fixed model-based control strategy, an adaptive controller and the LWPR-based learning controller. Simulations are carried out in order to evaluate the position and orientation tracking performance of each controller under varied end effector loading, velocities and inaccuracies in the known dynamic parameters. Both the adaptive controller and LWPR controller are shown to have comparable performance in the presence of parametric uncertainty. However, it is shown that the learning controller is unable to generalize well outside of the regions in which it has been trained. Hence, achieving good performance requires significant amounts of training in the anticipated region of operation. In addition to poor generalization performance, most learning controllers commence learning entirely from `scratch,' making no use of any a-priori knowledge which may be available from the well-known rigid body dynamics (RBD) formulation. The second portion of this thesis develops two techniques for online, incremental learning algorithms which incorporate prior knowledge to improve generalization performance. First, prior knowledge is incorporated into the LWPR framework by initializing the local linear models with a first order approximation of the prior information. Second, prior knowledge is incorporated into the mean function of Sparse Online Gaussian Processes (SOGP) and Sparse Pseudo-input Gaussian Processes (SPGP), and a modified version of the algorithm is proposed to allow for online, incremental updates. It is shown that the proposed approaches allow the system to operate well even without any initial training data, and further performance improvement can be achieved with additional online training. Furthermore, it is also shown that even partial knowledge of the system dynamics, for example, only the gravity loading vector, can be used effectively to initialize the learning.

This thesis deals with the design, fabrication and analysis of a novel actuator for a fish-like swimming mini robot. The developed actuator is tested on a mini boat. The actuator relies on a novel piezoelectric ultrasonic motor, developed according to the design requirements of actuator for fish-like swimming mini robots. Developed motor is within the dimensions of 25x6x6 mm in a simple mechanical structure with simple driving circuitry compared to its predecessor. Bidirectional rotation of the motor is transformed to a flapping tail motion for underwater locomotion in a simple mechatronic structure. The simplicity in the motor and actuator enables further development on the miniaturization, improvement on the performances as well as easy and low cost manufacturing. The developed actuator is a candidate to be used in mini swimming robot with fish- like locomotion.

Mobile robotics has become a rapidly developing field of interdisciplinary research within robotics. This promising field has attracted the attention of academicy, industry, several government agencies. Currently from security to personal service mobile robots are being used in a variety of tasks. The use of such robots is expected to only increase in the near future. In this study, it is aimed to design and manufacture a versatile robot base. This base is aimed to be the main driving unit for various applications performed both indoors and outdoors ranging from personal service and assistance to military applications. The study does not attempt to individually address any specific application, indeed it is aimed to shape up a robotic module that can be used in a wide range of application on different terrain with proper modification. The robot base is specifically designed for mixed terrain applications, yet this study attempts to provide some guidelines to help robot designers. The manufactured robot base is tested with tracks, wheels, and with both tracks and wheels, results are provided as guidelines to robot designers. Last but no the least, this study aims to obtain the know-how of building functional and flexible robots in Turkey by facilitating local resources as much as possible.