Recent Advances in Autonomous Navigation for Robots- A Comprehensive Review

Abstract

Recent advances in autonomous navigation for robots have seen significant progress in the development of robust and efficient navigation systems. Breakthroughs in sensor fusion techniques, machine learning algorithms, and computer vision techniques have driven these advancements. This has enabled robots to navigate complex and dynamic environments with greater precision and adaptability. Key areas of progress include simultaneous localization and mapping (SLAM) algorithms, deep reinforcement learning for navigation, and the integration of multi-sensor data for improved localization and obstacle avoidance. These developments have the potential to revolutionize various industries, including manufacturing, logistics, and service robotics, by enabling robots to operate more autonomously and effectively in real-world scenarios. Further, the latest developments in autonomous navigation algorithms for robots have significantly improved their ability to operate autonomously in diverse and challenging environments, bringing us closer to a future where robots can seamlessly navigate and interact with the world around them. Next, we will discuss the diverse applications of robots in various industries. We will also address the unresolved challenges and future prospects. To conclude, we will summarize the main discoveries and underscore the importance of autonomous navigation for the future of robotics.