Optimization-based Motion Planning and Runtime Monitoring for Robotic Agent with Space and Time Tolerances

Abstract

We present an optimization-based approach for robot planning, monitoring and self-correction problems under signal temporal logic specifications (STL). The STL specifications are translated into mixed-integer linear constraints, and we generate the reference trajectory by solving a mixed-integer-linear-programming (MILP) to maximize the overall space and time tolerances. During runtime execution, a prediction module is constantly evaluating the robustness degree of the predicted trajectory, and a self-correction module based on event-triggered model predictive control (MPC) has been designed to predict and correct possible future violations of the specifications. Simulation results show that with our approach, the robotic agent is able to generate a path that satisfies the STL specifications while maximizing space and time tolerances, and able to make corrections when there are possible violations of the specifications during runtime execution.