An Behavior-based Robotics
Robot Motion Planning
Advanced Digital Logic Design Using VHDL, State Machines, and Synthesis for FPGA's
Advanced Digital Logic Design Using VHDL, State Machines, and Synthesis for FPGA's
Bio-Inspired Artificial Intelligence: Theories, Methods, and Technologies
Bio-Inspired Artificial Intelligence: Theories, Methods, and Technologies
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This paper discusses how to generate mobile robots' behaviors using genetic algorithms, GA. The behaviors are built using state machines implemented in a programmable logic device, an FPGA, and they are encoded in such a way that a state machine architecture executes them, controlling the overall operation of a small mobile robot. The behaviors generated by the GA are evaluated according to a fitness function that grades their performance. Basically, the fitness function evaluates the robot's performance when it goes from an origin to a destination. In our approach each individuals' chromosome represents, given a set of inputs coming from the sensors and the current state, the next state and outputs that controls the robot's movements. For each generation the GA needs to evaluate population's individuals, doing this with the real robot it would required to much time, that would be impossible to do. Thus, the GA needs a simulator, as close as it can be to the real robot and its environment. The simulator gets the individuals' chromosomes and executes the algorithm state machine represented by them, it simulates the movements of the robot depending of the output generated in the present state and the simulated robot's sensors. Our objective was to prove that GA is a good option as a method for finding behaviors for mobile robots' navigation and also that these behaviors can be implemented in FPGAs.