CAJ | 학술논문

研究具有风速变化的动态环境下气味源定位问题，提出一种基于支持向量回归和微粒群优化的多机器人气味源定位方法。以当前时刻机器人的位置为输入，以机器人所测的气味浓度值为输出，利用支持向量回归，建立机器人所在位置气味浓度的预测模型；采用改进微粒群优化方法定位气味源时，以气味浓度最大的机器人所在的观测窗内，基于预测模型得到的气味浓度最大值的所在位置作为微粒的全局极值，以当前机器人的位置作为微粒的个体极值，完成微粒的更新；根据机器人所测的气味浓度值，定位气味源。将所提方法应用于2个气味源定位场景，实验结果表明所提方法能够在短时间内成功定位气味源。
연구구유풍속변화적동태배경하기미원정위문제，제출일충기우지지향량회귀화미립군우화적다궤기인기미원정위방법。이당전시각궤기인적위치위수입，이궤기인소측적기미농도치위수출，이용지지향량회귀，건립궤기인소재위치기미농도적예측모형；채용개진미립군우화방법정위기미원시，이기미농도최대적궤기인소재적관측창내，기우예측모형득도적기미농도최대치적소재위치작위미립적전국겁치，이당전궤기인적위치작위미립적개체겁치，완성미립적경신；근거궤기인소측적기미농도치，정위기미원。장소제방법응용우2개기미원정위장경，실험결과표명소제방법능구재단시간내성공정위기미원。
Aiming at the problem of odor source localization in dynamic environments with changing wind,a method of localizing odor source using multiple robots based on particle swarm optimization and support vector regression is proposed.In this method, a model predicting concentration of an odor at a location based on support vector regression is developed,which takes a robot’s current position as its input,and the corresponding concentration value measured by the robot as its output.Then,an improved particle swarm optimization is used to localize odor source,and the position corresponding to the maximal concentration value ob-tained by the prediction model is taken as the particle’s global optimum in the observation window of the robot with the maximal concentration value.In addition,the current position of a robot is taken as the particle’s local optimum.The velocity and position of a particle is updated based on the above global and local optima.Finally,the position of an odor source is localized based on the concentration value measured by a robot.The proposed method is applied to localize odor sources in two scenarios,and the exper-imental results confirm that the proposed method can successfully localize odor source in a short time.