基于认知差异的多机器人协同信息趋向烟羽源搜索方法

多机器人协同稀疏烟羽源搜索研究中,追求群体信息一致而忽视个体独立搜索能力的发挥,导致群体无法有效适应复杂搜索状况.为此,提出一种基于认知差异的协同信息趋向源搜索方法.首先,利用相对熵度量群内个体对源位置估计的认知差异;然后,据此赋予不同个体烟羽采样以相应权重,在贝叶斯推理过程自适应权衡自身线索与群体线索;最后,采用分布式信息熵决策实施协同信息趋向搜索.多种场景下的仿真结果验证了所提出算法的优越性.

Multi-robot collaborative infotaxis searching for plume source based on cognitive differences

In multi-robot plume source searching with sporadic cues, the classic approaches strive for achieving social information consistency of all robots while the exploration ability of individual robot is ignored, which weakens the adaptivity of the group in complex environment. To overcome this drawback, a cooperative infotaxis searching approach is proposed. The relative entropy is introduced to measure the cognitive differences of likelihood function of source location between robots. Then, different weights are assigned to the sensor measurements of individual robot based on the cognitive differences. In the Bayesian learning process, the trade-off between individual cues and social cues is adaptively regulated for acquiring private source location probability distribution. Finally, the collaborative infotaxis search strategy is implemented by performing an entropy decision of each robot. The advantages of the proposed method are illustrated by simulation experiments under different scenarios.