Skip to Content

Visual Control of UAV`s by using soft computing

...God say “let there be light” and one trillion evolutive brain-eyes systems began their journey toward intelligence...


Flying is a wonderful resource and a difficult one too. Trying to control a scale helicopter flying in a windy day is as difficult as trying to keep a broomstick on balance in one of your fingers in the same windy day. And yet there exist wonderful, neurally controlled small beings which master the delights of flying since the early creation. We talk of course about insects, first to conquer our gas filled atmosphere. In one sense insect are bio-machines controlled by finite, neural machines that have self learned to process complex vision-to-control problems. We are thus motivated to continue this strategy and define self-learned neural controller capable of flying real world machines using real world images.


Before an autonomous machine learning process could begin, there must exist a driven force that thrusts the learning of the machine from the inside. We have experimentally found that a possible candidate for this internal willpower is a neural agent that begin as a simple array of independent, mutually inhibit neurons, which compete to have a unique random winner, in successive energy consuming cycles. Once such engagement exists, self learning begins to move by its own. What we found is that once an autonomous, solid way of taken random, non conflictive decisions is established inside its inner self, the machine is ready to learn by itself more complicated survival tricks, especially when active cooperation with other processing agents is required.