Transformations of Arm-Z modular manipulator with Particle Swarm Optimization. (December 2018) - Zawidzki, Machi; Szklarski, Jacek
Highlights: The concept of Arm-Z (AZ) as a modular hyper-redundant manipulator is introduced. AZ represents a large class of reconfigurable modular construction systems. Requirements for manipulators are the most challenging among reconfigurable systems. Particle swarm optimization is implemented for efficient state transition of AZ. Cases studied: constrained AZ bending & manipulating under environmental constraints. Abstract: A novel concept of hyper-redundant, snake-like manipulator is presented. It is based on the reconfigurable modular construction system–Arm-Z (AZ). AZ is comprised of linearly joined congruent modules with possibility of relative twist. AZ is an Extremely Modular System, i.e. it is composed of a single basic unit and allows for creating free-form shapes. Required level of usefulness and efficiency are among the most challenging design aspects of such reconfigurable systems. Here AZ is considered in the context of kinematics of robotic arms. In general, due to its highly non-linear nature, it is very difficult to find transitions between given states (configurations), especially under realistic environmental and structural constraints. As a way to control the manipulator, an implementation of Particle Swarm Optimization (PSO) for finding transitions between AZ states in realistic scenarios is proposed. Four practical examples are presented which are variations of two distinct problems: bending of a hexagonal AZ in a narrow slot (strong environmental constraints), and reaching a given point in 3D space by the tip of dodecagonal AZ (acting as a robotic arm). The problem of AZ transformation has been defined as a multi-objective optimization. The methodology is general with no restrictions to the objective function. Since the problem is strongly non-linear, in order to cover large space of potential solutions, the algorithm runs for a relatively large number of random initial swarms. This task was distributed on a computer cluster. Although the nature of AZ reconfiguration is discrete, the optimization algorithm is continuous.