Inverse Kinematics of Serial Manipulators in Cluttered Environments Using a New Paradigm of Particle Swarm Optimization

In this paper, a new Behavioral-based Particle Swarm Optimization algorithm is proposed in order to solve the inverse kinematics problem for a manipulator operating in an environment cluttered with obstacles. The introduced variant of the Particle Swarm Optimization relies on the idea of dividing the population of the particles in subgroups, each of which with a specific task, achieving in this manner a faster convergence to the final result. The proposed algorithm is exploited in order to solve the inverse kinematics problem for a generic serial manipulator both in its dexterous and reachable workspace.