An Integrated Multi-Sensor Framework for Autonomous Mobile Robot Navigation Using TEB-CA and Adaptive Locomotion
Annotatsiya
Autonomous navigation is a core capability for contemporary operational robots. With limited human oversight, these robots are able to operate in industrial plants, hospitals, and cities. Furthermore, to be multifunctional, this type of robot must integrate accurate localization, dependable obstacle detection, and flexible coordination across a wide range of different settings. This paper presents an integrated framework that achieves these three goals through the fusion of modern robotics techniques. In the initial phase, the framework is based on a multi-sensor fusion approach which employs LiDAR, IMUs and vision sensors for accurate SLAM. This method guarantees better precision and robustness whether the environment is structured or unstructured. The remaining part of the chapter entails a TEB-ORCA based navigation algorithm. We show that the proposed local planning concept can safely and efficiently steer the mobile robot within dynamic environments, especially those environments crowded with obstacles where unintended collisions may ensue. This third part discusses a six-wheeled robot mobile base that can navigate rough terrain and climb curbs. Using deep learning to predict road conditions lets the robot change its gait to suit the environment .The construction of the system's feasibility has been fulfilled and tested in Gazebo simulation tool .Through experimental test results, we see this solution can achieve highly accurate localization, reliable obstacle avoidance and terrain traversable responses compared to other navigation approaches Such a multi-relationship design will lay the foundation for future service robots and industrial robots to be seamlessly integrated into smart cities, actual applications.