An Information-Theoretic Approach for Path Planning in Agents with Computational Constraints

Path and motion planning for autonomous systems has long been an area of research within the robotics and artificial intelligence communities. This has led to the development of a number of frameworks which formulate planning tasks in terms of mathematical optimization problems, which can then be solved by utilizing approaches from optimization theory and optimal control [1, 2]. However, planning in complex domains can be a challenging problem, and requires the agents to spend time and computational resources in order to find solutions, leading to an intrinsic need to balance computational complexity and optimality [3, 4, 5, 6, 7]. Within the path-planning community, this observation has resulted in the development of a number of approaches, which aim to explicitly capture the interplay between complexity and optimality. For example, in [8, 5, 9], the authors utilize wavelets to obtain multi-resolution representations of a two-dimensional environment for path-planning.