Behavior-based switch-time MPC for mobile robots

Model predictive control can be computationally intensive as it has to compute an optimal control trajectory at each time instant. As such, we present a method in which parametrized behaviors are introduced as a level of abstraction to give a finite representation to the control trajectory optimization. As these control laws can be designed to accomplish different tasks, the robot is able to use the presented framework to tune the parameters online to achieve desirable results. Moreover, we build on switch-time optimization techniques to allow the model predictive control framework to optimize over a series of given behaviors, allowing for an added level of adaptability. We illustrate the utility of the framework through the control of a nonholonomic mobile robot.