SUMMARYIn mobile robot localization with multiple sensors, myriad problems arise as a result of inadequacies associated with each of the individual sensors. In such cases, methodologies built upon the concept of multisensor fusion are well-known to provide optimal solutions and overcome issues such as sensor nonlinearities and uncertainties. Artificial neural networks and fuzzy logic (FL) approaches can effectively model sensors with unknown nonlinearities and uncertainties. In this article, a robust approach for localization (positioning) of a mobile robot in indoor as well as outdoor environments is proposed. The neural network is utilized as a pseudo-sensor that models the global positioning system (GPS) and is used to predict the robot’s position in case of GPS signal loss in indoor environments. The data from proprioceptive sensors such as inertial sensors and GPS are fused using the Kalman and the complementary filter-based fusion schemes in the outdoor case. To eliminate the position inaccuracies due to wheel slippage, an expert FL system (FLS) is implemented and cascaded with the sensor fusion module. The proposed technique is tested both in simulation and in real scenarios of robot movements. The simulations and results from the experimental platform validate the efficacy of the proposed algorithm.

中文翻译：

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GPS、INS 和里程计传感器的信息融合提高移动机器人在室内和室外应用中的定位精度

发明内容在具有多个传感器的移动机器人定位中，由于与每个单独的传感器相关联的不足而出现了无数问题。在这种情况下，众所周知，基于多传感器融合概念的方法可提供最佳解决方案并克服传感器非线性和不确定性等问题。人工神经网络和模糊逻辑 (FL) 方法可以有效地对具有未知非线性和不确定性的传感器进行建模。在本文中，提出了一种在室内和室外环境中对移动机器人进行定位（定位）的稳健方法。神经网络用作模拟全球定位系统 (GPS) 的伪传感器，并用于在室内环境中 GPS 信号丢失的情况下预测机器人的位置。在户外案例中，使用卡尔曼和基于互补滤波器的融合方案对来自惯性传感器和 GPS 等本体感受器的数据进行融合。为了消除由于车轮打滑导致的位置不准确，我们实施了专家 FL 系统 (FLS)，并将其与传感器融合模块级联。所提出的技术在模拟和机器人运动的真实场景中进行了测试。实验平台的仿真和结果验证了所提算法的有效性。所提出的技术在模拟和机器人运动的真实场景中进行了测试。实验平台的仿真和结果验证了所提算法的有效性。所提出的技术在模拟和机器人运动的真实场景中进行了测试。实验平台的仿真和结果验证了所提算法的有效性。