Path planning is a fundamental problem in many areas, ranging from robotics and artificial intelligence to computer graphics and animation. Although there is extensive literature for computing optimal, collision-free paths, there is relatively little work that explores the satisfaction of spatial constraints between objects and agents at the global navigation layer. This paper presents a planning framework that satisfies multiple spatial constraints imposed on the path. The type of constraints specified can include staying behind a building, walking along walls, or avoiding the line of sight of patrolling agents. We introduce two hybrid environment representations that balance computational efficiency and search space density to provide a minimal, yet sufficient, discretization of the search graph for constraint-aware navigation. An extended anytime dynamic planner is used to compute constraint-aware paths, while efficiently repairing solutions to account for varying dynamic constraints or an updating world model. We demonstrate the benefits of our method on challenging navigation problems in complex environments for dynamic agents using combinations of hard and soft, attracting and repelling constraints, defined by both static obstacles and moving obstacles.