Agents paradigm to solve a complex optimization problem

Agents are computational systems interacting with dynamic, and non-entirely predictable environments. They decide for themselves, on the basis of their individual beliefs, goals, etc., how to respond to the environment. An agent is usually motivated to achieve a feasible objective by means of the collaboration of other agents. For example, lifting a heavy table may be impossible without help. However, it can be done by simply asking others for help. The process of gaining collaboration can take many forms. However, some tasks need more detailed communication to generate an explicit mutually acceptable agreement through negotiation. This paper presents a new method to solve the inverse kinematic problem of a redundant robot using the agents paradigm. This method guarantees rapid convergence of the robot to desired position, with substantially good accuracy. The originality of the proposed approach is its ability to overcome some of the standard problems such as the computation of the inverse or pseudoinverse Jacobian matrix, problem of singularity, redundancy, and considerably increased computational complexity, etc.