Towards fully autonomous energy efficient Coverage Path Planning for autonomous mobile robots on 3D terrain

Coverage Path Planning (CPP) is an essential problem in many applications of robotics, including but not limited to autonomous de-mining and farming. Most works on CPP address time efficiency or coverage completeness in a bi-dimensional and flat environment, not taking the terrain relief into account. In this paper we use a Genetic Algorithm to optimize the solution to the CPP problem in terms of energy consumption, taking into account the constraints of natural terrains: obstacles and relief. Instead of requiring an elevation map of the environment, we also propose an autonomous sparse sampling of the environment which is used in conjunction with Kriging to successfully model the relief of the environment. Field tests confirm our energy consumption model for the robot, and simulation results show that our approach is effective in reducing energy consumption of a mobile robot performing CPP.