Fuzzy-GA-based trajectory planner for robot manipulators sharing a common workspace

Abstract

This paper presents a novel fuzzy genetic algorithm (GA) approach to tackling the problem of trajectory planning of two collaborative robot manipulators sharing a common workspace, where the manipulators have to consider each other as a moving obstacle whose trajectory or behaviour is unknown and unpredictable, as each manipulator has individual goals and where both have the same priority. The goals are not restricted to a given set of joint values, but are specified in the workspace as coordinates at which it is desired to place the end-effector of the manipulator. By not constraining the goal to the joint space, the number of possible solutions that satisfies the goal increases according to the number of degrees of freedom of the manipulators. A simple GA planner is used to produce an initial estimation of the movements of the robots' articulations and collision free motion is obtained by the corrective action of the collision-avoidance fuzzy units.

Wall-Following Behavior-Based Mobile Robot Using Particle Swarm Fuzzy Controller

“…Currently, several methods that hybrid fuzzy system with evolutionary algorithms has been offered in behavior-based mobile robot, such as Genetic Algorithm (GA) [8,9], Genetic Programming [10] to overcome the behavior-based issues. However, the current evolutionary algorithms used have several drawbacks [11], such as not easy to be implemented and computationally expensive [12], require process should be completed and parameters should be adjusted, have slow convergence ability to find near optimum solution, and dependent heuristically to genetic operators [13].…”

Fast trajectory planning via the B-spline augmented virtual motion camouflage approach

“…Many methodologies have been developed for a myriad of applications, and new developments continue to emerge in this active field of research. In addition to many heuristic methodologies [1]- [3] that solve for global optimal solutions, and hybrid methods [11] that combine heuristic approaches with other methods, many methods focus on finding a local optimum or improving upon it and can be generally grouped as mathematical programming Two often used mathematical programming methods are (1) the calculus of variations (CoV) with Pontryagin's Minimum Principle (PMP) approach [4]- [6], and (2) direct collocation (DC) with nonlinear programming (NLP) approach [7], [8], [18]- [22]. Both methods have their own advantages and disadvantages.…”

SALSA-based motion optimization for robotic manipulators with strong nonlinear dynamic coupling

“…In [3], cooperate genetic algorithm with pattern search as a generalize pattern search in computing the optimal trajectory for end-effector. In [4], fuzzy genetic algorithm is introduced to tackle trajectory planning problem for two collaborative robotic manipulators. PSO-based time-optimal trajectory planning is proposed in [5] to search the global optimal solution for space manipulators.…”