基于刚性约束的双移动机器人协同定位

准确、快速的状态估计是保证多机器人顺利完成协作搬运任务的关键.然而，大部分现有多机器人协同定位方法都存在一定的局限性，往往无法同时兼顾定位精度与计算复杂度.因此，本文从协作搬运任务的特点出发，将距离与方位的刚性约束条件引入协同定位中，同时根据机器人之间的紧密耦合关系建立起通用有效的运动模型和量测模型.最终在此刚性约束系统建模的基础上，提出一种基于高斯-厄米特求积分卡尔曼滤波（Quadrature Kalman Filter，QKF）的双移动机器人协同定位方法.仿真实验结果表明：与基于无约束模型的QKF协同定位方法相比，本文所提方法不但具有更高的定位精度，而且计算复杂度大大降低，有助于实现多机器人实时协同定位.

Cooperative Localization in a Team of Two Mobile Robots Based on Rigid Constraints

Accurate and fast estimation for states is the key to the multi-robot cooperative transportation.However,the majority of the existing multi-robot cooperative localization approaches have a common limitation in which they cannot satisfy the requirements to the positioning accuracy and computational complexity.According to the task characteristics of cooperative transportation,the rigid constrains of the range and azimuth information are first introduced into the cooperative localization.Moreover,the close coupling relationship between robots is fully utilized to establish the general and effective kinematics and measurement models with the rigid constrains.This facilitates the derivation of an efficient approach to the dual-robot cooperative localization based on Gauss-Hermite quadrature Kalman filter (QKF).Experimental results demonstrate that the proposed approach has much higher positioning accuracy than the QKF cooperative localization approach based on unconstrained models,and reduces the computational complexity largely.This paves the way for the real-time cooperative localization in practical applications.