Automotive navigation for mobile robots: Comprehensive review - Mayar Abdullah Taleb; Gyula Korsoveczki; Géza Husi (Results in Engineering)
Effective navigation of mobile robots in a dynamic environment poses complex challenges, including mapping, localization, and path planning. These factors are interdependent and require robust solutions for successful robot navigation. The complexity of the task increases due to the unpredictability of dynamic obstacles that resemble humans or other robots. This research proposes an in-depth investigation into these navigation challenges, emphasizing the development of hybrid algorithms that manage interdependencies more effectively. The aim is to evaluate the performance of various navigation algorithms against a set of clearly defined metrics such as success rate, path efficiency, computational cost, and adaptability to environmental changes. This paper will give special attention to algorithms incorporating machine learning and real-time data analysis to manage dynamic obstacles better; also, it will explore the adaptability and scalability of these algorithms in simulated environments of varying complexity and size, preparing them for real-world applicability. This research intends to bridge the gap between theoretical algorithms and their implementation in real-world scenarios through theoretical analysis, comparative evaluation, and practical field tests. By achieving these objectives, we anticipate offering comprehensive insights and best practices for mobile robot navigation in dynamic environments, paving the way for innovative solutions that enhance robot autonomy and efficiency.