As part of the work of Denisa-Andreaa Constantinescu, she has published a paper at the Jornadas SARTECO that analyzes the possibilities of paralellizing the well-known Value Iteration algorithm for making decisions aboard a mobile robot. She has used the V-REP simulator of CRUMB for conducting many tests in that paper.

This is the title and abstract of the pre-camera-ready version:

Denisa Andreea Constantinescu, Angeles Navarro, Juan-Antonio Fernandez-Madrigal, and Rafael Asenjo (2017), Optimization of a decision-making algorithm for heterogeneous platforms, Jornadas SARTECO, Málaga (Spain).

This paper presents our experience optimizing a decision-making algorithm for navigating an autonomous robotic agent under uncertainty. For the experiment we used CRUMB – a relatively low cost mobile robot equipped with a heterogeneous computing platform. We have chosen the Value Iteration (VI) algorithm for computing the optimal navigation policy in the context of a Markov Decision Process. Using as a starting point the classical VI algorithm, we have developed a sequential implementation and proposed three variants that optimize for time and power efficiency. The first optimization is based on multi-core parallelism on the CPU, the second on asymmetric big.LITTLE multi-core parallelism, and the third is a heterogeneous or hybrid solution that combines the previous two. Our results show that multi-core parallelism yields the best real time performance (sequential programming is the only one not suitable for real-time), while the hybrid approach has the smallest energy consumption.