Hierarchical path planning techniques for Intelligent Autonomous Mobile Robots (IAMRs) attempt to subdivide the overall task of path planning into several layers. This subdivision is made more difficult by the physical robot's computational architecture. The most important problem with classic hierarchies is their inability to cope with a diversity of environments and obstacles. In this research, we present a hierarchical path planning paradigm which uses a distributed blackboard architecture as its foundation. We subdivide the path planning problem into three levels: a global path planner, a local planner, and a set of constraint analyzers. By creating a uniform distributed blackboard that spans many processors in a network, we have formed the basis of a uniform operating environment for experimenting with layered path planning, as well as other mobile robot issues. And by dividing the planning into three layers instead of two, the system is able to handle much more complicated and diverse environments.