Improving unmanned armoured mobile robot navigation accuracy using Kalman filter

Abstract

Thailand's remarkable economic progress has earned it recognition as a development success story, but terrorism remains a significant threat, particularly in the southern provinces. To effectively combat terrorism, intelligent systems like military robots are being increasingly utilized worldwide, including the development of vision-based robots and unmanned military equipment. The Thai government aligns with this trend by aiming to manufacture military robots, including armored mobile robot (AMR) equipped with cameras for peacekeeping and surveillance purposes. The development of an automatic tank simulation robot control system integrates global position system (GPS) technology, digital compasses, and lidar sensors to enhance efficiency, directional control, and obstacle avoidance. The study investigates the implementation of Kalman filters to enhance the precision of navigation in AMRs used in military contexts. The suggested system combines GPS, Lidar sensors, and digital compasses, utilizing advanced sensor fusion algorithms to improve directional control and obstacle avoidance. The system's usefulness is demonstrated through field tests and simulations, especially in complicated contexts where conventional approaches may face difficulties. The findings help to the progress of military robotics by providing a strong answer for improving navigation accuracy in real-world situations.