Hindawi Publishing Corporation Journal of Robotics Volume 2011, Article ID 549489, 9 pages doi:10.1155/2011/549489 Research Article Optimal Search Strategy of Robotic Assembly Based on Neural Vibration Learning Lejla Banjanovic-Mehmedovic, 1 Senad Karic, 2 and Fahrudin Mehmedovic 3 1 Faculty of Electrical Engineering, University of Tuzla, 75000 Tuzla, Bosnia and Herzegovina 2 H&H Inc., 75000 Tuzla, Bosnia and Herzegovina 3 ABB, 71000 Sarajevo, Bosnia and Herzegovina Correspondence should be addressed to Lejla Banjanovic-Mehmedovic, lejla.mehmedovic@untz.ba Received 17 July 2011; Accepted 8 November 2011 Academic Editor: Ivo Bukovsky Copyright © 2011 Lejla Banjanovic-Mehmedovic et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. This paper presents implementation of optimal search strategy (OSS) in verification of assembly process based on neural vibration learning. The application problem is the complex robot assembly of miniature parts in the example of mating the gears of one multistage planetary speed reducer. Assembly of tube over the planetary gears was noticed as the most difficult problem of overall assembly. The favourable influence of vibration and rotation movement on compensation of tolerance was also observed. With the proposed neural-network-based learning algorithm, it is possible to find extended scope of vibration state parameter. Using optimal search strategy based on minimal distance path between vibration parameter stage sets (amplitude and frequencies of robots gripe vibration) and recovery parameter algorithm, we can improve the robot assembly behaviour, that is, allow the fastest possible way of mating. We have verified by using simulation programs that search strategy is suitable for the situation of unexpected events due to uncertainties. 1. Introduction The planning is a key ability of intelligent systems, increas- ing their autonomy, reliabilities, efficiently and flexibility through the construction of sequences of actions to achieve their goals [1]. In artificial intelligence, planning originally meant a search for a sequence of logical operators or actions that transform an initial world state into a desired goal state. Robot motion planning usually ignores dynamics and considers other aspects, such as uncertainties, differential constraints, modeling uncertainties, and optimality. The robotic assembly, wheelchair navigation, sewer inspection robot, autonomous driving system in urban and off-road environments, and machine’s task planning for the robotic system all are examples of autonomous systems, which solve path planning/replanning problems [2, 3]. Dynamic replanning is necessary because at any time during execution of its tasks the robot might unexpect- edly run into problems [2]. The typical approach used for replanning is repair plans, which are prepared in advance and invoked to deal with specific exceptions during execution. This class of approaches may work well in relatively static and predictable environment. In more dynamic and uncertain environment where it is hard to anticipate possible excep- tions, the replanning generates a (partially) new plan in case when one or more actions have problems during execution [4]. Very interesting area of research is using planning strategies in robot assembly. The example components can be assembled faster, gentle, and more reliably using the intelligent techniques. In order to create robot behaviours that are similarly intelligent, we seek inspiration from human strategies date [5]. The working theory is that the human accomplishes an assembly in phases, with a defined behaviour and a subgoal in each phase. The human changes behaviours according to events that occur during the assembly, and the behaviour is consistent between the events. The human’s strategy is similar to a discrete event system in that the human progresses through a series of behavioural states separated by recognizable physical events.