RESUMO
The merging of environmental maps constructed by individual UAVs alone and the sharing of information are key to improving the efficiency of distributed multi-UAVexploration. This paper investigates the raster map-merging problem in the absence of a common reference coordinate system and the relative position information of UAVs, and proposes a raster map-merging method with a directed crossover multidimensional perturbation variational genetic algorithm (DCPGA). The algorithm uses an optimization function reflecting the degree of dissimilarity between the overlapping regions of two raster maps as the fitness function, with each possible rotation translation transformation corresponding to a chromosome, and the binary encoding of the coordinates as the gene string. The experimental results show that the algorithm could converge quickly and had a strong global search capability to search for the optimal overlap area of the two raster maps, thus achieving map merging.
Assuntos
AlgoritmosRESUMO
In this work, we studied the area coverage control problem (ACCP) based on the time cost metric of a robot network with an input disturbance in a dynamic environment, which was modeled by a time-varying risk density function. A coverage control method based on the time cost metric was proposed. The area coverage task that considers the time cost consists of two phases: the robot network is driven to cover the task area with a time-optimal effect in the first phase; the second phase is when the accident occurs and the robot is driven to the accident site at maximum speed. Considering that there were movable objects in the task area, a time-varying risk density function was used to describe the risk degree at different locations in the task area. In the presence of the input disturbance, a robust controller was designed to drive each robot, with different maximum control input values, to the position that locally minimized the time cost metric function in a fixed time, and the conditions for maximum control input were obtained. Finally, simulation results and comparison result are presented in this paper.
RESUMO
The negative illumination thermoradiative solar cell (NITSC) consisting of a concentrator, an absorber, and a thermoradiative cell (TRC) is established, where the radiation and reflection losses from the absorber to the environment and the radiation loss from the TRC to the environment are taken into consideration. The power output and overall efficiency of the NITSC are analytically derived. The operating temperature of the TRC is determined through the thermal equilibrium equations, and the efficiency of the NITSC is calculated through the optimization of the output voltage of the TRC and the concentrating factor for a given value of the bandgap. Moreover, the maximum efficiencies of the NITSC at different conditions and the optimal values of the bandgap are determined, and consequently, the corresponding optimum operating conditions are obtained. The results obtained here will be helpful for the optimum design and operation of TRCs.
RESUMO
A theoretical model of a nanoscale spin-Seebeck power device (SSPD) is proposed based on the longitudinal spin-Seebeck effect in bilayers made of a ferromagnetic insulator and a normal metal. Expressions for the power output and thermal efficiency of the SSPD are derived analytically. The performance characteristics of the nanoscale SSPD are analyzed using numerical simulation. The maximum power output density and efficiency are calculated numerically. The effect of the spin Hall angle on the performance characteristics of the SSPD is analyzed. The choice of materials and the structure of the device are discussed. The optimum criteria of some key parameters of the SSPD, such as the power output density, efficiency, thickness of the normal metal, and the load resistance, are given. The results obtained here could provide a theoretical basis for the optimal design and operation of nanoscale SSPDs.
RESUMO
The benchmark functions and some of the algorithms proposed for the special session on real parameter optimization of the 2005 IEEE Congress on Evolutionary Computation (CEC'05) have played and still play an important role in the assessment of the state of the art in continuous optimization. In this article, we show that if bound constraints are not enforced for the final reported solutions, state-of-the-art algorithms produce infeasible best candidate solutions for the majority of functions of the IEEE CEC'05 benchmark function suite. This occurs even though the optima of the CEC'05 functions are within the specified bounds. This phenomenon has important implications on algorithm comparisons, and therefore on algorithm designs. This article's goal is to draw the attention of the community to the fact that some authors might have drawn wrong conclusions from experiments using the CEC'05 problems.