The noticeable rise of drones, or UAVs, in business, rescue operations, and general ap- plications necessitates increased safety due to their growing presence. As drones begin to operate alongside humans and existing structures, there is a need for preventing collisions with objects and/or people. This thesis presents a potential solution to the obstacle avoidance problem, enabling drones to avoid possible collisions during navigation. The algorithm relies on an Archimedean spiral to identify an escape point in real time based on environmental data perceived by the depth Camera. During navigation, the algorithm continuously processes the point cloud information, generating a suitable escape point if an obstruc- tion is detected within a safety cylindrical volume.