基于P-PSO 算法的室内有障碍通风环境下的多机器人气味源搜索

受湍流影响, 室内通风环境下的烟羽分布表现出波动变化且不连续的特性; 在一些角落处, 较大的漩涡会产生长时间的局部浓度极值区; 另外室内的障碍物也会改变烟羽的分布状况. 因此室内有障碍通风环境下的机器人气味源搜索问题变得很复杂. 本文提出了基于概率适应度函数的粒子群优化(Probability-fitness-function based particle swarm optimization, P-PSO)算法并用于多机器人气味源搜索. P-PSO算法的特点是采用概率而非确定数来表达适应度函数值. 针对气味源搜索问题, P-PSO算法的适应度函数值由贝叶斯和变论域模糊推理估计的气味源概率表达. 为验证提出的搜索策略, 构建了对应实际边界条件的室内通风环境的烟羽模型. 仿真研究证明了本文提出的P-PSO搜索算法用于解决气味源搜索问题的可行性.

P-PSO Algorithm Based Multi-robot Odor Source Search in Ventilated Indoor Environment with Obstacles

Influenced by turbulence, the practical odor plume in ventilated indoor environment is fluctuant and intermittent. Bigger eddies can easily lead to longtime local concentration maxima in some corners. In addition, the obstacles can also change the plume distribution. These make the mobile robot based odor source search quite complicated. A probability-fitness-function based particle swarm optimization (P-PSO) algorithm is proposed and used for multi-robot based odor source search in ventilated indoor environment with obstacles. The P-PSO algorithm uses probability instead of a definite number to express the value of fitness function. For the odor source search problem, the fitness function is expressed by the odor source probability estimated by Bayesian inference combined with variable-universe fuzzy inference. To validate the proposed search strategy, different odor plumes corresponding to the real boundary conditions of an indoor environment are set up. Simulation results demonstrate the feasibility of the P-PSO algorithm for solving the odor source localization problem.