Artif Intell Rev (2007) 27:113–130 DOI 10.1007/s10462-008-9087-0 Recurrent neural robot controllers: feedback mechanisms for identifying environmental motion dynamics Stephen Paul McKibbin · Bala Amavasai · Arul N. Selvan · Fabio Caparrelli · W. A. F. W. Othman Published online: 28 October 2008 © Springer Science+Business Media B.V. 2008 Abstract In this paper a series of recurrent controllers for mobile robots have been devel- oped. The system combines the iterative learning capability of neural controllers and the optimisation ability of particle swarms. In particular, three controllers have been developed: an Exo-sensing, an Ego-sensing and a Composite controller which is the hybrid of the latter two. The task for each controller is to learn to follow a moving target and identify its trajec- tory using only local information. We show how the learned behaviours of each architecture rely on different sensory representations, although good results are obtained in all cases. Keywords Dynamic Neural Networks · Particle Swarm Optimisation · Sensory-Motor Coordination 1 Introduction In nature, animals use the sensory information that is available to them in order to benefit them in completing a task. In some cases this information is readily available to them at instantaneously and can be acted upon immediately and in other cases that same information S. P. McKibbin (B ) · B. Amavasai · A. N. Selvan · F. Caparrelli · W.A. F. W. Othman Microsystems & Machine Vision Laboratory, Materials & Engineering Research Institute, Sheffield Hallam University, Sheffield S1 1WB, UK e-mail: S.Mckibbin@shu.ac.uk; Stephen.p.mckibbin@student.shu.ac.uk B. Amavasai Mechanistic Research, Procter & Gamble, 460 Basingstoke Road, Reading RG2 0QE, UK e-mail: amavasai.b@pg.com A. N. Selvan e-mail: A.N.Selvan@shu.ac.uk F. Caparrelli e-mail: F.Caparrelli@shu.ac.uk W. A. F. W. Othman e-mail: Wan.A.Othman@student.shu.ac.uk 123