Browsing by Author "Rocha, Rui P."
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- Analysis and parameter adjustment of the RDPSO towards an understanding of robotic network dynamic partitioning based on Darwin's theoryPublication . Couceiro, Micael; M. L. Martins, Fernando; Rocha, Rui P.; Ferreira, N. M. FonsecaAlthough the well-known Particle Swarm Optimization (PSO) algorithm has been first introduced more than a decade ago, there is a lack of methods to tune the algorithm parameters in order to improve its performance. An extension of the PSO to multi-robot foraging has been recently proposed and denoted as Robotic Darwinian PSO (RDPSO), wherein sociobiological mechanisms are used to enhance the ability to escape from local optima. This novel swarm algorithm benefits from using multiple smaller networks (one for each swarm), thus decreasing the number of nodes (i.e., robots) and the amount of information exchanged among robots belonging to the same sub-network. This article presents a formal analysis of RDPSO in order to better understand the relationship between the algorithm’s parameters and its convergence. Therefore, a stability analysis and parameter adjustment based on acceleration and deceleration states of the robots is performed. These parameters are evaluated in a population of physical mobile robots for different values of communication range. Experimental results show that, for the proposed mission and parameter tuning, the algorithm con-verges to the global optimum in approximately 90% of the experiments regardless on the number of robots and the communication range.
- Mechanism and convergence analysis of a multi-robo tswarm approach based on natural selectionPublication . Couceiro, Micael; M. L. Martins, Fernando; Rocha, Rui P.; Ferreira, Nuno M. F.The Darwinian Particle Swarm Optimization (DPSO) is an evolutionary algorithm that extends the Particle Swarm Optimization (PSO) using natural selection, or survival-of-the-fittest, to enhance the ability to escape from local optima. An extension of the DPSO to multi-robot applications has been recently proposed and denoted as Robotic Darwinian PSO (RDPSO), benefiting from the dynamical partitioning of the whole population of robots. Therefore, the RDPSO decreases the amount of required information exchange among robots, and is scalable to large populations of robots. This paper presents a stability analysis of the RDPSO to better understand the relationship between the algorithm parameters and the robot’s convergence. Moreover, the analysis of the RDPSO is further extended for real robot constraints (e.g., robot dynamics, obstacles and communication constraints) and experimental assessment with physical robots. The optimal parameters are evaluated in groups of physical robots and a larger population of simulated mobile robots for different target distributions within larger scenarios. Experimental results show that robots are able to converge regardless of the RDPSO parameters within the defined attraction domain. However, a more conservative parametrization presents a significant influence on the convergence time. To further evaluate the herein proposed approach, the RDPSO is further compared with four state-of-the-art swarm robotic alternatives under simulation. It is observed that the RDPSO algorithm provably converges to the optimal solution faster and more accurately than the other approaches.
- Towards a further understanding of the roboticdarwinian PSOPublication . Couceiro, Micael; M. L. Martins, Fernando; Manuel Clemente, Filipe; Rocha, Rui P.; Ferreira, Nuno M. F.This paper presents a statistical significance analysis of a modified version of the Particle Swarm Optimization (PSO) on groups of simulated robots performing a distributed exploration task, denoted as RDPSO (Robotic DPSO). This work aims to evaluate this novel exploration strategy studying the performance of the algorithm under communication constraints while increasing the population of robots. Experimental results show that there is no linear relationship between the number of robots and the maximum communication range. In general, the decreased performance by the developed algorithm under communication constraints can be overcome by slightly increasing the number of robots as the maximum communication range is decreased.