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Robotic catching of thrown objects is one of the common robotic tasks, which is explored in several works. This task includes subtask of tracking and forecasting the trajectory of the thrown object. Here we propose an algorithm for estimating future trajectory based on video signal from two cameras. Most of existing implementations use deterministic trajectory prediction and several are based on machine learning. We propose a combined forecasting algorithm where the deterministic motion model for each trajectory is generated via the genetic programming algorithm. Genetic programming is implemented on C++ with use of CUDA library and executed in parallel way on the graphical processing unit. Parallel execution allow genetic programming in real time. Numerical experiments with real trajectories of the thrown tennis ball show that the algorithm can forecast the trajectory accurately. © 2016

The problem of optimally assigning agents (resources) to a given set of tasks is known as the assignment problem (AP). The classical AP and many of its variations have been extensively discussed in the literature. In this paper, we examine a specific class of the problem, in which each task is assigned to a group of collaborating agents. APs in this class cannot be solved using the Hungarian or other known polynomial time algorithms. We employ the genetic algorithm (GA) to solve the problem. However, we show that if the size of the problem is large, then standard crossover operators cannot efficiently find near-optimal solutions within a reasonable time. In general, the efficiency of the GA depends on the choice of genetic operators (selection, crossover, and mutation) and the associated parameters. In order to design an efficient GA for determining the near-optimal assignment of tasks to collaborative agents, we focus on the construction of crossover operators. We analyze why a naive implementation with standard crossover operators is not capable of sufficiently solving the problem. Furthermore, we suggest modifications to these operators by adding a shuffled list and introduce two new operators (team-based and team-based shuffled list). We demonstrate that the modified and new operators with shuffled lists perform significantly better than all operators without shuffled lists and solve the presented AP more efficiently. The performance of the GA can be further enhanced by using chaotic sequences. Moreover, the GA is also compared with the particle swarm optimization (PSO) and differential evolution (DE) algorithms, demonstrating the superiority of the GA over these search algorithms., Export Date: 22 October 2018; Article; CODEN: NRCGE; Correspondence Address: Younas, I.; Department of Computer Science, National University of Computer and Emerging SciencesPakistan; email: irfan.younas@nu.edu.pk; Funding details: KTH, Kungliga Tekniska Högskolan; Funding text: We would like to thank Prof. Rassul Ayani for supervising this research and Prof. Christian Schulte at the KTH Royal Institute of Technology for his valuable comments and suggestions. QC 20181126

Calcium dynamics plays key role in many intracellular processes. Studying of calcium sparks, that is events of calcium release via groups of ryanodine receptors, or RyR channels, is of a great importance for live sciences. Recent experimental studies show, that RyRs have a non-uniform arrangement in calcium release units (CRU), however, to our knowledge, there is yet no published Ca 2+ spark model which takes into account this fact. In the paper we made use of a genetic simulated annealing (GSA) algorithm for the RyR cluster generation and Gillespie algorithm for modeling calcium spark. Computer simulation of the model CRU taking into account a specific homotetrameric structure of the RyR channel shows that experimental nearest neighbor distances (NND) distributions of RyRs in CRU do not provide exact information about calcium spark probability. Having the same NND distribution, the CRUs can have different inverse distance matrices and hence different calcium spark properties. Our model approach opens novel perspectives for studying the calcium release process. © 2017 The Authors. Published by Elsevier B.V.