Comparison of Optimizing Path Planning for Mobile Robots with Obstacle Avoidance

Abstract

This research delves into a thorough examination of two distinct path-planning algorithms, denoted as algorithm A and algorithm B, operating in dynamic environments replete with moving obstacles. The primary objective of the study is to optimize the performance of algorithm A, thereby evolving it into the innovative algorithm B. Employing rigorous simulation examples, this study assesses the performance metrics of both algorithms, shedding light on their respective strengths and limitations. When confronted with dynamic settings featuring swiftly moving obstacles, algorithm B demonstrates a slight advantage owing to its optimized features. Despite this improvement, both algorithms face challenges in densely populated environments, leading to increased failure rates. Key metrics, including failure rate occurrences and navigation efficiency as a function of the distance covered towards the target, offer valuable insights into the nuanced behavior of these algorithms. The study’s outcomes highlight the intricacies associated with the development of path-planning algorithms for real-world applications, particularly in dynamic and densely populated environments. The research accentuates the perpetual necessity for refinement and optimization, focusing specifically on adaptive algorithms capable of effectively managing high-velocity moving obstacles. These insights hold paramount importance in the advancement of intelligent robotic systems, empowering them to navigate intricate and dynamic environments with unparalleled precision and efficiency.