Configurable Mobile Robot Behaviors Implemented on FPGA Based Architectures

For educational purposes there is a need to teach electrical and computer engineering students the basics of the design of state machines using programmable logic devices, and for students interested in mobile robots to teach them the basics of mobile robots' behaviors. At the same time one of the topics of interest in the mobile robot's community is how to generate their new behaviors, using state machines, automatically. This paper discusses how to create mobile robots' behaviors using genetic algorithms (GAs), implementing them in programmable logic devices (FPGAs) and how these behaviors can be programmed by students in a mobile robotics course. The behaviors are encoded as algorithm state machines and using feed-forward artificial neural networks (ANN). In the state-machine approach, each individual's chromosome represents, given a set of inputs coming from the sensors and the current state, both the next state and the outputs that control the robot's movements. In the ANN approach, whose weights are found also with GAs, a pipeline architecture was built to perform it; each pipeline executes a layer of the ANN, thus once the pipeline is full the execution speed of the ANN is one sensors' clock cycle. We evaluate the behaviors generated by the GA according to a fitness function that grades their performance for avoiding obstacles. The inputs to such a robot are infrared sensors to detect obstacles; the outputs are the velocities of its wheels. Our objectives are first, to prove that GAs are a good option as a method for finding behaviors for mobile robots' navigation, and second, that these behaviors can be implemented in an efficient way in FPGAs. We tested both behaviors in a small mobile robot, that is build in an electrical engineering course that teaches how to build mobile robots.