# Adaptive Navigation in Collaborative Robots: A Reinforcement Learning and Sensor Fusion Approach
## 2 Literature Review  
One of the recent technologies being integrated into the autonomous vehicle system is the application of Unmanned Ground Vehicles. It can be seen [16] how GPS fundamentally guides UGVs from a source location to a target location while making sure that obstacles are evaded through ultrasonic sensors. The system is already preset with the GPS coordinates so that the vehicle will adhere to a certain route and adjust whenever it detects an obstacle all throughout. It has such sensors to present a relatively low-cost alternative to more expensive and complex sensors, like LiDAR [16]. The practical application of the ultrasonic sensors involves implementing an additional autonomous navigation system that improves the reliability and efficiency of UGVs in providing safety and guaranteeing correctness. Another approach can be to [17,18] consider integrating GPS with magnetometers for better autonomous navigation. Position data can be obtained from the GPS while the orientation data can be captured by using the magnetometer. The LabVIEW HMI [17] can use such systems providing an easy-to-use platform that allows for the monitoring and control of the navigation process. The GPS and magnetometer coupling is best suited to be applied in urban or foliage-dense environments when a consistent, precise heading must be maintained or when GPS signals are weak and obstructed. Further, research has [19] examined how an autonomous vehicle should cope with the possibility of losing its GNSS signals either within the environment or within specific zones. The motivation was to complement navigation with multiple ultrasonic sensors so that the vehicle would not rely solely on GPS. This provides a solution to estimate the navigation states of land vehicles in GNSS-denied environments. An Extended Kalman Filter (EKF) is applied to fuse low-cost ultrasonic sensors with an Inertial Measurement Unit (IMU). The rear wheels are mounted with ultrasonic sensors that measure the range difference in determining the angular velocity and forward velocity of the vehicle. The two experimental tests showed that the information required to navigate considerably improved the position accuracy during GNSS outages. Where another implementation [20] explains that it is not possible for typical navigation systems that are dependent on GPS to work indoors or in closed environments; therefore, some other methods are to be adopted to use the UAV safely. Using magnetic field mapping as a means of navigation that uses the recording and visualization of changes in the local magnetic field with high resolution to develop magnetic maps. These maps act as a reference framework for UAVs to navigate autonomously within complex indoor spaces. The importance of doing proper calibration of the magnetometer lies in the fact that nonuniform results were obtained from the magnetometers of different batches of production. It is so important, particularly in the generation of correct magnetic field maps. It also shows the importance of real-time magnetic data visualization with tools such as MATLAB and Voxel for instant environment estimation and detection of magnetic anomalies. More emphasis is put into research related to the introduction of permanent magnets into the magnetic field, which in this case, will be taken as intentional perturbations; the anomalies act as a beacon that would give the UAVs their position and map in the environment. The observations conclude that with more exploration, magnetic field mapping will be a reliable way for indoor navigation in the systems of UAVs and provide precision control even if GPS is not available. Though our implementation makes use of GPS data, reliance on GPS could be reduced by using alternate methods [19,20]. On the other hand, various implementations [21] provide an extensive review of various techniques for mobile robot navigation, organized into classical and reactive strategies.