Planning Navigation Routes in Unknown Environments

Self-driving robots have to fulfill many different operations, as coordinating the motors’ traction, camera movement, or actuator arms mechanics, as well as more high-level operations like driving to different places. Autonomous navigation is of utmost importance for exploration robots, which must drive around exploring areas with unknown terrain conditions, as for example is the case of mars rovers and other space exploration vehicles. Given that the environment is unknown, planning a specific route and driving plan is challenging or even inappropriate due to blocking obstacles in the terrain. To overcome such problems we propose an adaptable plan for driving robots in different situations. Our solutions mixes both global and dynamic planning algorithms to take advantage of available information, if it exist beforehand, and to overcome unknown obstacles if they appear, while still moving towards the goal. In particular, we apply our algorithm to the movement of robots between posts in environments with partial information, as it is the case of space mission competitions. We evaluate our solution in a simulated environment taking into account the effectiveness in fulfilling a mission in the shortest time, using the shortest possible path. Our results show that of the A* algorithm with diagonals in combination with the ABEO algorithm offer the best combination reaching the goal in most cases, in optimal (planning + execution) time.