Temporal Logic Robot Task Planning with Active Acquisition of Information

This paper proposes a mission planning algorithm for the robot to operate in an environment with interference. The robot is equipped with proximity sensor to roughly estimate the environment and can apply active perception action to detect the environment. The goal of the robots is to accomplish complex tasks, captured by co-safe Linear Temporal Logic (scLTL) formulas. With the interference created by environment, general observation-based solution will cause a huge cost even if it can complete the task. When the robot without active perception encounters a set of states that the proximity observation can not tell, it will choose a conservative but costlier action. We aim to design an active perception plan for the robot to offset the impact which can reduce the cost. Our algorithm can trade off security and cost, to find a strategy to accomplish the complex task. We illustrate our method on motion planning case studies and show that the proposed algorithm can address complex planning tasks with smaller costs through active perception action.