The aim of this study is to disseminate a novel path planner which is particularly used for offline robots to build more efficient collision-free trajectories in terms of the length in more skilled fashion. Robotic path planning as one of the most important problems has been under investigation by a variety of researchers within the last few decades. Path planner refers to a unit which is responsible to perform a series of operations on the robots’ environment with the sole purpose of building proper trajectories from a start point to the goal configuration. A robot’s planner generally consists of some or all of the following units: analyzing the workspace, mapping the workspace into an alternative methodology such as a graph, optimizing the constructed map, and calculating and refining the desired trajectory. Various path planners have been proposed based on the different robots’ functionalities as well as the environmental specifications. A path planner basically uses a methodology inspiring a scientific theory or event to build optimal paths. This research tends to propose a novel path planner which is able to be successfully applied on a variety of workspaces with different constraints. Moreover, the presented planner successfully builds the shortest collision-free trajectories from an initial to the goal configurations. In addition, compared to the other majority of path planners, the illustrated path planner uses less environmental global information to build optimal paths. This leads the planner unit to allocate less of system resources such as memory, hence, increasing the performance of the planner in terms of preserving more system resources.