Motion Planning Algorithm

Motion planning algorithms aim to compute safe and efficient paths for robots navigating complex environments, addressing challenges like obstacle avoidance, dynamic environments, and multi-robot coordination. Current research emphasizes improving the speed and optimality of algorithms, particularly in high-dimensional spaces, through techniques such as sampling-based methods (e.g., RRT*, BIT*), optimization-based approaches (e.g., iLQR), and the integration of machine learning (e.g., reinforcement learning, neural networks). These advancements are crucial for enabling autonomous systems in various applications, from self-driving cars and robotic manipulation to aerial and underwater robotics.