This paper introduces a new discrete event system (DES) model for supervising and controlling trajectory planning tasks and robot motion using automata. This model is proposed based on a new approach, namely mask description. Masks are constructed adaptively and are modified based on the error between the original path and the planned path. The model acts in two phases, mask construction phase (MCP) and end-effecter positioning phase (E2P2). In MCP, it tries to plan a path, and in E2P2 it tries to place the end-effecter along the sequence of points on the path. The model describes a path in the Cartesian space and moves the end-effecter in the joint variable space, and therefore, MCP plans the path as accurate as Cartesian space description, and E2P2 position the robot as fast as joint space description. Results show that the proposed adaptive masking is remarkably efficient in computing.