A membrane parallel rapidly-exploring random tree algorithm for robotic motion planning

摘要

In recent years, incremental sampling-based motion planning algorithms have been widely used to solve robot motion planning problems in high-dimensional configuration spaces. In particular, the Rapidly-exploring Random Tree (RRT) algorithm and its asymptotically-optimal counterpart called RRT <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="{}^{*}" display="inline" overflow="scroll"> <mml:msup> <mml:mi/> <mml:mo>*</mml:mo> </mml:msup> </mml:math> are popular algorithms used in real-life applications due to its desirable properties. Such algorithms are inherently iterative, but certain modules such as the collision-checking procedure can be parallelized providing significant speedup with respect to sequential implementations. In this paper, the RRT and RRT <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="{}^{*}" display="inline" overflow="scroll"> <mml:msup> <mml:mi/> <mml:mo>*</mml:mo> </mml:msup> </mml:math> algorithms have been adapted to a bioinspired computational framework called Membrane Computing whose models of computation, a.k.a. P systems, run in a non-deterministic and massively parallel way. A large number of robotic applications are currently using a variant of P systems called Enzymatic Numerical P systems (ENPS) for reactive controlling, but there is a lack of solutions for motion planning in the framework. The novel models in this work have been designed using the ENPS framework. In order to test and validate the ENPS models for RRT and RRT <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="{}^{*}" display="inline" overflow="scroll"> <mml:msup> <mml:mi/> <mml:mo>*</mml:mo> </mml:msup> </mml:math> , we present two ad-hoc implementations able to emulate the computation of the models using OpenMP and CUDA. Finally, we show the speedup of our solutions with respect to sequential baseline implementations. The results show a speedup up to 6x using OpenMP with 8 cores against the sequential implementation and up to 24x using CUDA against the best multi-threading configuration.