Transformations of Arm-Z modular manipulator with Particle Swarm Optimization

Abstract

Arm-Z (AZ) is a concept of innovative hyper-redundant manipulator, which represents larger class of reconfigurable modular construction systems. AZ belongs to the family of Extremely Modular Systems, i.e. it is composed of a single basic unit and allows for creating free-form shapes.A required level of usefulness and efficiency are among the most challenging design aspects of such reconfigurable systems to achieve. Here AZ is considered in the context of kinematics of robotic arms. Due to highly non-linear nature of the system, it may be very difficult to find transitions between two given states, especially under practical environmental and structural constraints.This paper presents an implementation of Particle Swarm Optimization (PSO) for finding transitions between AZ states in realistic scenarios. 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 geometrical point by the tip of a 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 cluster. Although the nature of AZ reconfiguration is discrete, the optimization algorithm is continuous.

Simulation of simple movements of Arm-Z oblique swivel joint chain manipulator

“…In all previous experiments, there was practically no wobbling. This is, however, a major issue of discrete version of Arm-Z manipulator presented in [38]. Discrete means that the relative twists of the modules were not continuous, as in the case described in this paper, but discrete.…”

Trajectory Planning and Collision Control of a Mobile Robot: A Penalty‐Based PSO Approach

“…It is well suited for continuous variable and global search problems. It is efectively applied to a varied series of problems such as network [27], structural optimization [28], and fuzzy control system [29]. In APSO, the system makes it ready to change with the population of a random value and searches for the best by updating the local generation.…”