Hybrid robot controller synthesis with GP and UETPN
Attila Ors Kilyen, Tiberiu Stefan Letia · 2018
Controller synthesis for robotic agents has been one of the leading topics of Genetic Programming (GP) for decades. In this paper, a novel approach is presented to synthesise hybrid controllers. It uses Koza style genetic programming (GP) to generate Unified Enhanced Timed Petri Net (UETPN) models. UETPN models combine capabilities of timed Petri-nets, fuzzy logic systems and simple arithmetic operators. They can handle both event-like and continuous inputs (and outputs). They can change their inner state and execution flow based on the existence of a particular input event, or a value provided by a continuous input channel. In order to generate UETPN models (with GP), an intermediate language was designed, called UETPN Lisp. Dynamic and static editing and custom tailored crossover operators improve the proposed evolutive system. A three-wheel robot is modelled with a dynamic system. In order to exemplify the potential of the presented framework, a solution to solve the problem of corridor navigation and line following is proposed.