A Formal Control Framework of Autonomous Vehicle for Signal Temporal Logic Tasks and Obstacle Avoidance

This article investigates the control problem of making an autonomous vehicle modeled as a nonlinear affine system, achieve both temporal logic tasks and obstacle avoidance. A new formal control framework based on the control barrier function (CBF) and potential field method is proposed, which aims at eliminating the singular points caused by the conflict between the target and the obstacle in traditional CBF methods. A feasibility detection mechanism algorithm is proposed to detect the task in terms of workspace topology and determines the structure of the framework. On the one hand, obstacle avoidance can always be fulfilled such that the safety of the system is guaranteed. On the other hand, satisfaction for the target formulas is discussed in terms of obstacles, workspace topology, and formula preference. The proposed control framework generates controllers based on different feasibility types of tasks automatically, which shows global performance in both safety guarantee and task achievement. Finally, the effectiveness of the overall framework is illustrated by two examples.