This thesis describes a research study in the design, implementation, evaluation and commercialisation of pneumatic motion control systems for modular robots. The research programme was conducted as part of a collaborative study, sponsored by the Science and Engineering Research Council, between Loughborough University and Martonair (UK) Limited. Microprocessor based motion control strategies have been used to produce low cost pneumatic servo-drives which can be used for 'point-to-point' positioning of payloads. Software based realtime control strategies have evolved which accomplish servo-controlled positioning while compensating for drive system non-linearities and time delays. The application of novel compensation techniques has resulted in a significant improvement in both the static and dynamic performance of the drive. A theoretical foundation is presented based on a linearised model of a pneumatic actuator, servo-valve, and load system. The thesis describes the design and evolution of microprocessor based hardware and software for motion control of pneumatic drives. A British Standards based test-facility has allowed control strategies to be evaluated with reference to standard performance criteria. It is demonstrated in this research study that the dynamic and static performance characteristics of a pneumatic motion control system can be dramatically improved by applying appropriate software based realtime control strategies. This makes the application of computer controlled pneumatic servos in manufacturing very attractive with cost performance ratios which match or better alternative drive technologies. The research study has led to commercial products (marketed by Martonair Ltd), in which realtime control algorithms implementing these control strategy designs are executed within a microprocessor based motion controller.

This dissertation describes the design, analysis, planning, and control of a self-reconfigurable modular robotic system. The proposed robotic system mainly contains three major types of robotic modules: load carrier, manipulation module, and locomotion module. Each module is capable of navigation and interaction with the environment individually. In addition, the robotic system is proposed to reassemble autonomously into various configurations to perform complex tasks such as humanoid configuration to enable enhanced functionality to reconfigure into a configuration that would enable the system to cross over a ditch. A non-back drivable active docking mechanism with two Degrees of Freedom (DOFs) was designed to fit into the tracked units of the robot modules for achieveing the reconfiguration. The quantity and location of the docking mechanisms are customizable and selectable to satisfy various mission requirements and adapt to different environments. During the reconfiguration process, the target coupling mechanism of each module reconfigurable with each other autonomously. A Lyapunov function-based precision controller was developed to align the target docking mechanisms in a close range and high precision for assembling the robot modules autonomously into other configurations. Additionally, an trajectory optimization algorithm was developed to help the robot determine when to switch the locomotion modes and find the fastest path to the destination with the desired pose. / Doctor of Philosophy / Though the capabilities of individual robot platforms have advanced greatly from their original rigid construction to more modern reconfigurable platforms, it is still difficult to build a singular platform capable of adapting to all situations and environments that users may want or need it to function in. To help improve the versatility of robot systems, modular robots have become an active area of research. These modular robotic systems are made up of multiple robotic platforms capable of docking together, breaking apart, or otherwise reconfiguring to form a multitude of shapes to overcome and adapt to many diverse challenges and environments. This dissertation describes the design of a new modular robotic system with autonomous docking and reconfiguration. Existing technologies and motivations for the creation of a new modular robotic system are covered. Then the physical design, with a primary focus on the docking mechanism, is reviewed. A validation of the proposed robotic system in a virtual environment with real physical properties is demonstrated. Following this, the development of a Lyapunov function-based controller to autonomously align the docking mechanisms is presented. The overall docking process was also preliminarily verified using a prototype of a locomotion module and an active docking mechanism. In addition, the trajectory optimization and tracking methods are presented in the end.

We investigate the use of self-assembly in a robotic system as a means of responding to dierent environmental contingencies. Self-assembly is the mechanism through which agents in a multi-robot system autonomously form connections with one another to create larger composite robotic entities. Initially, we consider a simple response mechanism that uses stochastic self-assembly without any explicit control over the resulting morphology | the robots self-assemble into a larger, randomly shaped composite entity if the task they encounter is beyond the physical capabilities of a single robot. We present distributed behavioural control that enables a group of robots to make this collective decision about when and if to self-assemble in the context of a hill crossing task. In a series of real-world experiments, we analyse the eect of dierent distributed timing and decision strategies on system performance. Outside of a task execution context, we present fully decentralised behavioural control capable of creating periodically repeating global morphologies. We then show how arbitrary morphologies can be generated by abstracting our behavioural control into a morphology control language and adding symbolic communication between connected agents. Finally, we integrate our earlier distributed response mechanism into the morphology control language. We run simulated and real-world experiments to demonstrate a self-assembling robotic system that can respond to varying environmental contingencies by forming dierent appropriate morphologies.

This thesis deals with modular robotics and self-reconfigurable robotic systems. At the beginning are systems defined and classified, the main emphasis is on Molecubes. After that, similar system is designed with a focus on the actual construction of the modules. In conclusion, several sample assemblies was made.

This master thesis is focused on Modular Self-Reconfigurable Robotic Systems. Their description is made at first and then possibilities of their use are listed. The next chapter concerns Molecubes modular system. The design of similar system where the construction of the individual modules is described follows. The transformations of coordinated systems in the individual modules are described and the calculation of forward kinematics and simulation of inverse kinematics is made at the end of the thesis.

Modular and Reconfigurable Robots (MRRs) are those designed to address the increasing demand for flexible and versatile manipulators in manufacturing facilities. The term, modularity, indicates that they are constructed by using a limited number of interchangeable standardized modules which can be assembled in different kinematic configurations. Thereby, a wide variety of specialized robots can be built from a set of standard components. The term, reconfigurability, implies that the robots can be disassembled and rearranged to accommodate different products or tasks rather than being replaced. A set of MRR modules may consist of joints, links, and end-effectors. Different kinematic configurations are achieved by using different joint, link, and end-effector modules and by changing their relative orientation. The number of distinct kinematic configurations, attainable by a set of modules, varies with respect to the size of the module set from several tens to several thousands. Although determining the most suitable configuration for a specific task from a predefined set of modules is a highly nonlinear optimization problem in a hybrid continuous and discrete search space, a solution to this problem is crucial to effectively utilize MRRs in manufacturing facilities. The objective of this thesis is to develop novel optimization methods that can effectively search the Kinematic Configuration (KC) space to identify the most suitable manipulator for any given task. In specific terms, the goal is to develop and synthesize fast and efficient algorithms for a Task-Based Configuration Optimization (TBCO) from a given set of constraints and optimization criteria. To achieve such efficiency, a TBCO solver, based on Memetic Algorithms (MA), is proposed. MAs are hybrids of Genetic Algorithms (GAs) and local search algorithms. MAs benefit from the exploration abilities of GAs and the exploitation abilities of local search methods simultaneously. Consequently, MAs can significantly enhance the search efficiency of a wide range of optimization problems, including the TBCO. To achieve more optimal solutions, the proposed TBCO utilizes all the solutions of the Inverse Kinematics(IK) problem. Another objective is to develop a method for incorporating the multiple solutions of the IK problem in a trajectory optimization framework. The output of the proposed trajectory optimization method consists of a sequence of desired tasks and a single IK solution to reach each task point. Moreover, the total cost of the optimized trajectory is utilized in the TBCO as a performance measure, providing a means to identify kinematic configurations with more efficient optimized trajectories. The final objective is to develop novel IK solvers which are both general and complete. Generality means that the solvers are applicable to all the kinematic configurations which can be assembled from the available module inventory. Completeness entails the algorithm can obtain all the possible IK solutions.

Modular and Reconfigurable Robots (MRRs) are those designed to address the increasing demand for flexible and versatile manipulators in manufacturing facilities. The term, modularity, indicates that they are constructed by using a limited number of interchangeable standardized modules which can be assembled in different kinematic configurations. Thereby, a wide variety of specialized robots can be built from a set of standard components. The term, reconfigurability, implies that the robots can be disassembled and rearranged to accommodate different products or tasks rather than being replaced. A set of MRR modules may consist of joints, links, and end-effectors. Different kinematic configurations are achieved by using different joint, link, and end-effector modules and by changing their relative orientation. The number of distinct kinematic configurations, attainable by a set of modules, varies with respect to the size of the module set from several tens to several thousands. Although determining the most suitable configuration for a specific task from a predefined set of modules is a highly nonlinear optimization problem in a hybrid continuous and discrete search space, a solution to this problem is crucial to effectively utilize MRRs in manufacturing facilities. The objective of this thesis is to develop novel optimization methods that can effectively search the Kinematic Configuration (KC) space to identify the most suitable manipulator for any given task. In specific terms, the goal is to develop and synthesize fast and efficient algorithms for a Task-Based Configuration Optimization (TBCO) from a given set of constraints and optimization criteria. To achieve such efficiency, a TBCO solver, based on Memetic Algorithms (MA), is proposed. MAs are hybrids of Genetic Algorithms (GAs) and local search algorithms. MAs benefit from the exploration abilities of GAs and the exploitation abilities of local search methods simultaneously. Consequently, MAs can significantly enhance the search efficiency of a wide range of optimization problems, including the TBCO. To achieve more optimal solutions, the proposed TBCO utilizes all the solutions of the Inverse Kinematics(IK) problem. Another objective is to develop a method for incorporating the multiple solutions of the IK problem in a trajectory optimization framework. The output of the proposed trajectory optimization method consists of a sequence of desired tasks and a single IK solution to reach each task point. Moreover, the total cost of the optimized trajectory is utilized in the TBCO as a performance measure, providing a means to identify kinematic configurations with more efficient optimized trajectories. The final objective is to develop novel IK solvers which are both general and complete. Generality means that the solvers are applicable to all the kinematic configurations which can be assembled from the available module inventory. Completeness entails the algorithm can obtain all the possible IK solutions.

We investigate the use of self-assembly in a robotic system as a means of responding<p>to different environmental contingencies. Self-assembly is the mechanism through which<p>agents in a multi-robot system autonomously form connections with one another to create<p>larger composite robotic entities. Initially, we consider a simple response mechanism<p>that uses stochastic self-assembly without any explicit control over the resulting morphology<p> / Doctorat en Sciences de l'ingénieur / info:eu-repo/semantics/nonPublished