Framework to Improve Mobile Robot's Navigation Using Wireless Sensor Modules

As on-going research, this paper presents a framework to improve wireless mobile robot’s navigational accuracy in diverse indoor environments where the signals are affected by various types of interference including electromagnetic, multi-path, and fading and scattering. In particular, indoor construction environments pose unique challenges to accurate wireless navigation due to their relative complexity and inherently dynamic nature. Several integrated location and orientation sensors including a digital compass, a gyroscope, wheel encoders, an accelerometer, and Ultra Wideband (UWB) position tracking sensors are introduced in this paper. A distinct cause of error for each sensor is studied based on location, traveling distance, and rotational angle. To improve the position data accuracy, statistical methods such as outlier analysis and the Kalman Filter are applied in this research. A framework for position and orientation error compensation between relative and absolute sensors is described with preliminary research results indicating that position and orientation errors can be statistically adjusted in real time.