Applications of Biological Cell Models in Robotics

In this paper I present some of the most representative biological models applied to robotics. In particular, this work represents a survey of some models inspired, or making use of concepts, by gene regulatory networks (GRNs): these networks describe the complex interactions that affect gene expression and, consequently, cell behaviour

Kinematically optimal hyper-redundant manipulator configurations

“Hyper-redundant” robots have a very large or infinite degree of kinematic redundancy. This paper develops new methods for determining “optimal” hyper-redundant manipulator configurations based on a continuum formulation of kinematics. This formulation uses a backbone curve model to capture the robot's essential macroscopic geometric features. The calculus of variations is used to develop differential equations, whose solution is the optimal backbone curve shape. We show that this approach is computationally efficient on a single processor, and generates solutions in O(1) time for an N degree-of-freedom manipulator when implemented in parallel on O(N) processors. For this reason, it is better suited to hyper-redundant robots than other redundancy resolution methods. Furthermore, this approach is useful for many hyper-redundant mechanical morphologies which are not handled by known methods

Addressing Tasks Through Robot Adaptation

Developing flexible, broadly capable systems is essential for robots to move out of factories and into our daily lives, functioning as responsive agents that can handle whatever the world throws at them. This dissertation focuses on two kinds of robot adaptation. Modular self-reconfigurable robots (MSRR) adapt to the requirements of their task and environments by transforming themselves. By rearranging the connective structure of their component robot modules, these systems can assume different morphologies: for example, a cluster of modules might configure themselves into a car to maneuver on flat ground, a snake to climb stairs, or an arm to pick and place objects. Conversely, environment augmentation is a strategy in which the robot transforms its environment to meet its own needs, adding physical structures that allow it to overcome obstacles. In both areas, the presented work includes elements of hardware design, algorithms, and integrated systems, with the common goal of establishing these methods of adaptation as viable strategies to address tasks. The research takes a systems-level view of robotics, placing particular emphasis on experimental validation in hardware

Autonomous Task-Based Evolutionary Design of Modular Robots

In an attempt to solve the problem of finding a set of multiple unique modular robotic designs that can be constructed using a given repertoire of modules to perform a specific task, a novel synthesis framework is introduced based on design optimization concepts and evolutionary algorithms to search for the optimal design. Designing modular robotic systems faces two main challenges: the lack of basic rules of thumb and design bias introduced by human designers. The space of possible designs cannot be easily grasped by human designers especially for new tasks or tasks that are not fully understood by designers. Therefore, evolutionary computation is employed to design modular robots autonomously. Evolutionary algorithms can efficiently handle problems with discrete search spaces and solutions of variable sizes as these algorithms offer feasible robustness to local minima in the search space; and they can be parallelized easily to reducing system runtime. Moreover, they do not have to make assumptions about the solution form. This dissertation proposes a novel autonomous system for task-based modular robotic design based on evolutionary algorithms to search for the optimal design. The introduced system offers a flexible synthesis algorithm that can accommodate to different task-based design needs and can be applied to different modular shapes to produce homogenous modular robots. The proposed system uses a new representation for modular robotic assembly configuration based on graph theory and Assembly Incidence Matrix (AIM), in order to enable efficient and extendible task-based design of modular robots that can take input modules of different geometries and Degrees Of Freedom (DOFs). Robotic simulation is a powerful tool for saving time and money when designing robots as it provides an accurate method of assessing robotic adequacy to accomplish a specific task. Furthermore, it is difficult to predict robotic performance without simulation. Thus, simulation is used in this research to evaluate the robotic designs by measuring the fitness of the evolved robots, while incorporating the environmental features and robotic hardware constraints. Results are illustrated for a number of benchmark problems. The results presented a significant advance in robotic design automation state of the art

On adaptive robot systems for manufacturing applications

System adaptability is very important to current manufacturing practices due to frequent changes in the customer needs. Two basic concepts that can be employed to achieve system adaptability are flexible systems and modular systems. Flexible systems are fixed integral systems with some adjustable components. Adjustable components have limited ranges of parameter changes that can be made, thus restricting the adaptability of systems. Modular systems are composed of a set of pre-existing modules. Usually, the parameters of modules in modular systems are fixed, and thus increased system adaptability is realized only by increasing the number of modules. Increasing the number of modules could result in higher costs, poor positioning accuracy, and low system stiffness in the context of manufacturing applications. In this thesis, a new idea was formulated: a combination of the flexible system and modular system concepts. Systems developed based on this new idea are called adaptive systems. This thesis is focused on adaptive robot systems. An adaptive robot system is such that adaptive components or adjustable parameters are introduced upon the modular architecture of a robot system. This implies that there are two levels to achieve system adaptability: the level where a set of modules is appropriately assembled and the level where adjustable components or parameters are specified. Four main contributions were developed in this thesis study. First, a General Architecture of Modular Robots (GAMR) was developed. The starting point was to define the architecture of adaptive robot systems to have as many configuration variations as possible. A novel application of the Axiomatic Design Theory (ADT) was applied to GAMR development. It was found that GAMR was the one with the most coverage, and with a judicious definition of adjustable parameters. Second, a system called Automatic Kinematic and Dynamic Analysis (AKDA) was developed. This system was a foundation for synthesis of adaptive robot configurations. In comparison with the existing approach, the proposed approach has achieved systemization, generality, flexibility, and completeness. Third, this thesis research has developed a finding that in modular system design, simultaneous consideration of both kinematic and dynamic behaviors is a necessary step, owing to a strong coupling between design variables and system behaviors. Based on this finding, a method for simultaneous consideration of type synthesis, number synthesis, and dimension synthesis was developed. Fourth, an adaptive modular Parallel Kinematic Machine (PKM) was developed to demonstrate the benefits of adaptive robot systems in parallel kinematic machines, which have found many applications in machine tool industries. In this architecture, actuators and limbs were modularized, while the platforms were adjustable in such a way that both the joint positions and orientations on the platforms can be changed