Robots are sophisticated machines which are specially designed to have the capabilities to handle operations, on behalf of human, in many different scenarios. In the past decades, the design of robot systems has been evolving and there are increasing numbers of possible applications of robot. Some systems can even be able to overcome the individual limitations and handle complex problems by combining the strengths of multiple robots. To reduce the risk of human life, robots are now being put into missions under extremely dangerous or hazardous environment where human intervention is not tolerable , such as search-and-rescue missions inside damaged buildings after natural disasters and cleaning up of radioactive materials in nuclear accidence. Even though robots are dispensable, if they are damaged, disabled or trapped, the mission would not be accomplished. Therefore, the longevity of a robot system is always a challenge for robotic operations in such difficult environments. To tackle this challenge, many studies focus on improving the design of individual robot, minimizing the chance of robot failure; or the way that how functioning robots may share the job of the failed robots. The way that how other robots can help failed robots recover, however, has yet to be widely discussed. This thesis studies the feasibility of having multi-robot system with different automatic cooperative recovery abilities on top of its primary functions. A novel cooperative recovery framework is proposed for generic control among system primary functions and recovery behaviours. A number of experiments have been done to study the influence of cooperative recovery on a multi-robot system and how it can affect the system in terms of system performance, sustainability and overhead. An Immunity-based cooperative recovery model has also been created to overcome the drawback introduced by cooperative recovery, finding a balance between the two system objective among system productivity and longevity. Two modified versions of cooperative recovery model are also included in this study to further maximize the system potential. / published_or_final_version / Industrial and Manufacturing Systems Engineering / Doctoral / Doctor of Philosophy

The use of autonomous robots in industrial applications is growing in popularity and possesses the following advantages: cost effectiveness, job efficiency and safety aspects. Despite the advantages, the major drawback to using autonomous robots is the cost involved to acquire such robots. It is the aim of GMSA to develop a low cost AGV capable of performing material handling in an industrial environment. Collective autonomous robots are often used to perform tasks, that is, more than one working together to achieve a common goal. The intelligent controller, responsible for establishing coordination between the individual robots, plays a key role in managing the tasks of each robot to achieve the common goal. This dissertation addresses the development of an AGV capable of such functionality. Key research areas include: the development of an autonomous coupling system, integration of key safety devices and the development of an intelligent control strategy that can be used to govern the operation of multiple AGVs in an area.

Despite decades of research robotic manipulation systems outside of highly-structured industrial applications are still far from ubiquitous. Perhaps particularly curious is the fact that there appears to be a large divide between the theoretical grasp modeling literature and the practical manipulation community. Specifically, it appears that the most successful approaches to tasks such as pick-and-place or grasping in clutter are those that have opted for simple grippers or even suction systems instead of dexterous multi-fingered platforms. We argue that the reason for the success of these simple manipulation systemsis what we call passive stability: passive phenomena due to nonbackdrivable joints or underactuation allow for robust grasping without complex sensor feedback or controller design. While these effects are being leveraged to great effect, it appears the practical manipulation community lacks the tools to analyze them. In fact, we argue that the traditional grasp modeling theory assumes a complexity that most robotic hands do not possess and is therefore of limited applicability to the robotic hands commonly used today. We discuss these limitations of the existing grasp modeling literature and setout to develop our own tools for the analysis of passive effects in robotic grasping. We show that problems of this kind are difficult to solve due to the non-convexity of the Maximum Dissipation Principle (MDP), which is part of the Coulomb friction law. We show that for planar grasps the MDP can be decomposed into a number of piecewise convex problems, which can be solved for efficiently. Despite decades of research robotic manipulation systems outside of highlystructured industrial applications are still far from ubiquitous. Perhaps particularly curious is the fact that there appears to be a large divide between the theoretical grasp modeling literature and the practical manipulation community. Specifically, it appears that the most successful approaches to tasks such as pick-and-place or grasping in clutter are those that have opted for simple grippers or even suction systems instead of dexterous multi-fingered platforms. We argue that the reason for the success of these simple manipulation systemsis what we call passive stability: passive phenomena due to nonbackdrivable joints or underactuation allow for robust grasping without complex sensor feedback or controller design. While these effects are being leveraged to great effect, it appears the practical manipulation community lacks the tools to analyze them. In fact, we argue that the traditional grasp modeling theory assumes a complexity that most robotic hands do not possess and is therefore of limited applicability to the robotic hands commonly used today. We discuss these limitations of the existing grasp modeling literature and setout to develop our own tools for the analysis of passive effects in robotic grasping. We show that problems of this kind are difficult to solve due to the non-convexity of the Maximum Dissipation Principle (MDP), which is part of the Coulomb friction law. We show that for planar grasps the MDP can be decomposed into a number of piecewise convex problems, which can be solved for efficiently. We show that the number of these piecewise convex problems is quadratic in the number of contacts and develop a polynomial time algorithm for their enumeration. Thus, we present the first polynomial runtime algorithm for the determination of passive stability of planar grasps. For the spacial case we present the first grasp model that captures passive effects due to nonbackdrivable actuators and underactuation. Formulating the grasp model as a Mixed Integer Program we illustrate that a consequence of omitting the maximum dissipation principle from this formulation is the introduction of solutions that violate energy conservation laws and are thus unphysical. We propose a physically motivated iterative scheme to mitigate this effect and thus provide the first algorithm that allows for the determination of passive stability for spacial grasps with both fully actuated and underactuated robotic hands. We verify the accuracy of our predictions with experimental data and illustrate practical applications of our algorithm. We build upon this work and describe a convex relaxation of the Coulombfriction law and a successive hierarchical tightening approach that allows us to find solutions to the exact problem including the maximum dissipation principle. It is the first grasp stability method that allows for the efficient solution of the passive stability problem to arbitrary accuracy. The generality of our grasp model allows us to solve a wide variety of problems such as the computation of optimal actuator commands. This makes our framework a valuable tool for practical manipulation applications. Our work is relevant beyond robotic manipulation as it applies to the stability of any assembly of rigid bodies with frictional contacts, unilateral constraints and externally applied wrenches. Finally, we argue that with the advent of data-driven methods as well as theemergence of a new generation of highly sensorized hands there are opportunities for the application of the traditional grasp modeling theory to fields such as robotic in-hand manipulation through model-free reinforcement learning. We present a method that applies traditional grasp models to maintain quasi-static stability throughout a nominally model-free reinforcement learning task. We suggest that such methods can potentially reduce the sample complexity of reinforcement learning for in-hand manipulation.We show that the number of these piecewise convex problems is quadratic in the number of contacts and develop a polynomial time algorithm for their enumeration. Thus, we present the first polynomial runtime algorithm for the determination of passive stability of planar grasps.