Abstract
It is argued in this paper that motion planning and control involve two different types of processing. Global planning - such as that required to make one's way through a city - involves symbolic sequential processing, whilst local obstacle-avoidance capabilities - such as those used to reach one's mug when there are other objects around - involve massively-parallel distributed processing. The main algorithms for motion planning and control developed within the AI and neurocomputing paradigms are reviewed, their strengths and weaknesses being highlighted. The conclusion is that a combination of AI techniques with neurocomputing ones that takes advantage of the strong points of both computing paradigms is required to develop robust computational systems for motion planning and control. The key point to achieve such a combination is to devise good interfaces between the representations used in the two paradigms.
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