Affiliations: [a] Research Group on Natural Computing, Department of Computer Science and Artificial Intelligence, School of Computer Engineering, Universidad de Sevilla, Seville, Spain | [b] College of Information Science and Technology, Chengdu University of Technology, Chengdu, Sichuan, China | [c] COL Labloratory, School of Computer Science, University of Nottingham, Nottingham, UK
Correspondence:
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Corresponding author: Ferrante Neri, School of Computer Science, University of Nottingham, Jubilee Campus, Wollaton Road, Nottingham NG8 1BB, UK. E-mail: [email protected].
Abstract: 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* 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* 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*, 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.
Keywords: Optimal motion planning, Rapidly-exploring Random Tree, Membrane Computing, OpenMP, CUDA
