Robust Motion Planning employing Signal Temporal Logic
For robotic motion planning, this work provides a convex optimization approach to handle temporal specifications with robustness, though it is an incremental combination of existing methods.
This paper introduces a robustness metric called Discrete Average Space Robustness for Signal Temporal Logic specifications and combines it with Model Predictive Control to formulate motion planning as a convex Linear Program, achieving natural robustness against noise.
Motion planning classically concerns the problem of accomplishing a goal configuration while avoiding obstacles. However, the need for more sophisticated motion planning methodologies, taking temporal aspects into account, has emerged. To address this issue, temporal logics have recently been used to formulate such advanced specifications. This paper will consider Signal Temporal Logic in combination with Model Predictive Control. A robustness metric, called Discrete Average Space Robustness, is introduced and used to maximize the satisfaction of specifications which results in a natural robustness against noise. The comprised optimization problem is convex and formulated as a Linear Program.