Adaptive Optimal Receding-Horizon Robot Navigation via Short-Term Policy Development
Anahita Jamshidnejad, Emilio Frazzoli · 2018
We propose a novel optimal receding-horizon navigation approach for a robot in an unknown search environment, towards a known goal position. The search environment includes several obstacles that are distributed at unknown positions. The proposed approach considers multiple objectives, including reference path tracking, reduction of the energy consumption, restraining the robot's mission time, and asymptotic stability towards the goal position. The navigation policy is determined in the detection zone of the robot's detection sensor at particular update time steps for short time. This policy will be updated at the next update time steps. Moreover, we introduce a novel heuristic algorithm for determining the robot's tracking path trajectory that is simply implementable and fast in computations.