Implementation of Kalman Filter on PSoC-5 Microcontroller for Mobile Robot Localization 

Robots facilitate exploration of hazardous environments during response to catastrophe. Autonomous robotic platforms involved in search and rescue operations require accurate position and orientation (localization) information for self-navigation from its current position to its subsequent destination. A Hybrid Routing Algorithm Model has been proposed by the SPACE (structures, pointing and control engineering) URC (university research center) at California State University of Los Angeles. This model envisions three-layered terrain mapping with obstacle representations from various information sources such as satellites, UAVs and onboard range sensors. A* path-finding algorithm is applied to the outer two layers of the model (Layer 1 and Layer 2), while dynamic A* algorithm is responsible for innermost layer (Layer 3) navigation. The mobile robot localization information is computed using data obtained from a 9 Degrees of Freedom Inertial Measurement Unit. While gyroscope sensors provide the system the instantaneous radial velocity of a turning platform, these sensors are also susceptible to drift. Accelerometers are extremely sensitive to vibrations, and along with fluctuating magnetic fields, both accelerometers and magnetometers exhibit noisy behaviors when localizing the robot. Since the IMU contains all three sensors, a Kalman Filter is implemented on a PSoC-5 microcontroller to fuse data from the IMU sensors. This reduces standard deviation between measurements and improves reported heading accuracy, hence provides reliable information on the robot!ˉs loclization and improves mapping.