A Simulated Motion Planning Algorithm in 2d And 3d Environments Using Hill Climbing

This paper describes a computer simulated artificial intelligence (AI) agent moving in 2D and 3D\udenvironments. In the presented algorithm, the agent can take two operating modes: Manual Mode and Map\udor Autopilot mode. The user can control the agent fully in a manual mode by moving it in all possible\uddirections depending on the environment. Obstacles are sensed by the agent from a certain distance and\udare avoided accordingly. Another important mode is the Map mode. In this mode the user create a custom\udmap where initial position and a goal position are set. The user is able also to assign sudden and\udpredefined obstacles. By finding the shortest path, the agent moves to the goal position avoiding any\udobstacles on its path.\udThe paper documents a set of algorithms that can help the agent to find the shortest path to a predefined\udtarget location in a complex 3D environment, such as cities and mountains, avoiding all predefined and\udsudden obstacles. These obstacles are avoided also in manual mode and the agent moves automatically to a\udsafe location. The implementation is based on the Hill Climbing algorithm (descent version), where the\udagent finds its path to the global minimum (target goal). The Map generation algorithm, which is used to\udassign costs to every location in the map, avoids a lot of the limitations of Hill Climbing. \u