Dimensional Environment

Dimensional environments, primarily focusing on 2D spaces, are studied to optimize robot navigation, coverage, and resource allocation. Research employs optimization algorithms (e.g., MINLP, max-flow) and reinforcement learning techniques (e.g., Q-learning, Monte Carlo Tree Search) to address challenges like path planning in obstacle-filled environments, efficient multi-target tracking, and robust localization amidst dynamic obstacles. These advancements have implications for robotics, autonomous systems, and computational neuroscience, improving efficiency and robustness in various applications. Furthermore, extending these models to 3D environments and incorporating complex terrain is an emerging area of focus.