Path planning for space robots: Based on knowledge extrapolation and risk factors

The existing path planning algorithms for mobile robots operating in different unknown environments do not incorporate the learning and knowledge extrapolation methods.These algorithms do not provide any insight into human behaviour and thinking in everyday life. A robot in an unknown environment will have to reach its goal from anywhere and also it should be able to reach its goal safely. So it is advantageous if the path planning algorithm is able to extrapolate the data from the knowledge bank of the algorithm which is updated with the robot´s experience during its previous runs. This paper proposes a new paradigm which integrates the learning and knowledge extrapolation methods with the existing path planning algorithms to help the robot reach its goal safely and also in least possible time. Simulation results show an improvement of fifteen percent average reduction in the distance travelled by the robot to reach the goal and also ensures its safety. This paradigm can be implemented in any of the existing path planning algorithms.