As outlined in the previous section, the experiments completed so far make use of a very crude and limited rig. However, the success attained demonstrates the feasibility and the potential of the approach. The nature of the x-y table used means that the kinematics of the system are trivial, and that dynamics do not come into consideration. The use of stepper motors alleviates the need for closed loop joint control. The addition of a second camera would circumvent any need for calibration, since the ambiguity present in a single image could be removed. It may also prove beneficial to incorporate other sensors to provide additional information, such as force feedback [17]. Integration of data from multiple sensors can improve robustness; any redundancy in the information provided can be used to highlight noise and spurious sensor readings [18].
The learning system presented here is still under development and could be improved in many ways. Different algorithms for generalisation to new situations and management of the potentially large number of experiences are currently under consideration. More extensive and demanding tests are required to prove that the approach is suitable for use in an industrial setting.
The use of visual feedback as a means to compensate for the poor mechanics of a cheap robot has been demonstrated. Due to the nature of this approach, the system is flexible with respect to the position of the workpiece. Initial experiments indicate that a learning controller is capable of adapting to the behaviour of a specific robot. Effects due to the low cost nature of the robot, such as those due to backlash, can be overcome.
Work is underway to extend the ideas presented to PCB component placement. The majority of industrial PCB assembly machines are built with speed of operation a critical consideration, and therefore do not provide a realistic comparison in terms of cost. However, one commercially available component placement machine of comparable speed [19] sells for over 120,000. This demonstrates the potential cost savings of the approach outlined in this paper. The basic principles applied should readily extend to a variety of other robotic applications, thereby making them considerably more cost effective.
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