Robot Path Planning using Rapidly-Exploring Random Trees: A Membrane Computing Approach

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, in the membrane computing framework, models based on Enzymatic Numerical P systems (ENPS) have been applied to robot controllers. These controllers handle the power of motors according to motion commands usually generated by planning algorithms, but today there is a lack of planning algorithms based on membrane computing for robotics. With this motivation, we provide a new variant of ENPS called Random Enzymatic Numerical P systems with Proteins and Shared Memory (RENPSM) addressed to implement RRT algorithms and we illustrate it by presenting a model for path planning of mobile robots based on the bidirectional RRT algorithm. A software for RENPSM has been developed within the Robot Operating System (ROS) and simulation experiments have been conducted by means of the Pioneer 3-DX robot simulation platform.