Optimal coverage trajectories for a UGV with tradeoffs for energy and time

Area coverage is a common task for an unmanned ground vehicle (UGV) that requires time and energy to complete. We have developed a novel cost function that can be used to optimally traverse a path that covers a region. The UGV model and cost function are developed theoretically and verified experimentally. Our cost function weights force inputs, area covered and motor efficiency to create an optimal trajectory. This trajectory is constrained to follow a coverage path described in the literature. The path is modified based on the cost function by replacing turn-in-place maneuvers by moving turns. Tradeoffs are presented for three cases: (1) drive motor efficiency is not considered, (2) the motors are most efficient at the maximum velocity, and (3) the motors are most efficient below the maximum velocity. Optimality tradeoffs include the time required to cover the region, and the energy required to complete the trajectory. Experimental results using an iRobot Packbot are presented.