Generation of rapidly-exploring random trees by using a new class of membrane systems

Methods based on Rapidly-exploring Random Trees (RRTs) have been in use in robotics to solve motion planning problems for nearly two decades. On the other hand, models based on Enzymatic Numerical P systems (ENPS) have been applied to robot controllers for more than six years. These controllers in real robots handle the power of motors ac- cording to motion commands usually generated by planning algorithms, but today there is a lack of planning algorithms based on membrane sys- tems for robotics. With this motivation, we provide in this paper a new variant of ENPS called Random Enzymatic Numerical P systems with Proteins and Shared Memory (RENPSM) oriented to RRTs for planning in robotics and we illustrate it by presenting a model for generation of RRTs with holonomic limitations. We are working on the ENPS frame- work with the idea of moving towards a complete mobile robot system based on membrane systems, i.e. including controllers and planning; and we have incorporated new ingredients into the ENPS framework to meet the requirements of the RRT generation algorithm.