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.

This article describes the development of a component-based technology robot system that can be rapidly configured to perform a specific manufacturing task. The system is conceived with standard and inter-operable components including actuator modules, rigid link connectors and tools that can be assembled into robots with arbitrary geometry and degrees of freedom. The reconfigurable "plug-and-play" robot kinematic and dynamic modeling algorithms are developed. These algorithms are the basis for the control and simulation of reconfigurable robots. The concept of robot configuration optimization is introduced for the effective use of the rapidly reconfigurable robots. Control and communications of the workcell components are facilitated by a workcell-wide TCP/IP network and device level CAN-bus networks. An object-oriented simulation and visualization software for the reconfigurable robot is developed based on Windows NT. Prototypes of the robot systems configured to perform 3D contour following task and the positioning task are constructed and demonstrated. Applications of such systems for biomedical tissue scaffold fabrication are considered. / Singapore-MIT Alliance (SMA)