Navigation is an essential task for any mobile robot, whose primary focus is to guide the robot from its initial position to a specific target position while avoiding collisions with obstacles along the way. When talking about reactive navigation, we are referring to the same task described above, but now some restrictions are added to the way in which that task can be performed. Basically, a robot which navigates reactively makes decisions using only the information that, at that moment, its sensors collect. Acting that way, these robots are able to react quickly to any unexpected event (for instance, an obstacle). Unfortunately, the reactive navigation paradigm also has limitations, the most important being its inability to make robots avoid certain obstacles. In this work, we take as our starting point one of the best algorithms to date to make a robot navigate reactively; specifically, this algorithm is the well-known Nearness Diagram (ND). We modify ND to provide it with the ability to overcome complex obstacles, i.e. obstacles of great size and with intricate shapes. Moreover, this modification is carried out without losing the reactive nature of the ND algorithm. improved Nearness Diagram (iND) is the name given to the algorithm resulting from this modification. We test iND under simulation in a set of environments of increasing complexity, and we compare its results with those obtained by two other reactive navigation algorithms: namely, the Virtual Force Field (VFF) algorithm and the original ND algorithm.