For robots that work in a limited space, for example, conveyors, there may be sufficient rigid fix-ation on the floor. For robots that work on a flat surface, for example, in offices, wheels could be installed. However, a robot that has to perform the task on an unprepared surface requires a more complex construction. Unlike the wheels, the legs do not need constant contact with the support, which is well suited for uneven and bumpy terrain, such as woodlands, semi-deserts, foothill areas, etc.
One more application for task autonomous robots is to choose the trajectory of motion with the least energy costs. The existing methods of finding the exit from the labyrinth such as the rule of one hand, the wave algorithm have their pros and cons. To the general disadvantages, one can attribute the time spent on finding the exit. Using the neural network (NN) will increase the speed of the path generation due to the parallel processing of information, which in turn will reduce power consumption.
With the increase in the dimension, the difference in speed will be more obvious. Such results are achieved through parallel processing of information, which is not achievable in the classical versions.
Mobile robot, neural network, hexapod, search for the optimal route.
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