Hierarchical Sensor-Robot Control for On-Demand Sensing in a Partially Known Environment

To enable industrial robot autonomy without traditional manual programming, current approaches involve a carefully modeled environment or dedicated sensor feedback. This paper explores a novel alternative regime: on-demand sensing, in which a fleet of sensorless robots operating in un-modeled environments adapt to frequently changing repetitive tasks by requesting temporary access to a shared mobile sensor. A Hierarchical Sensor-Robot Control scheme is developed to enable an ad hoc team to cooperatively solve a task, at which point the sensor is dismissed while the robot repeats the task safely in open loop. An outer loop simultaneously optimizes the sensor pose and the parameters of an inner loop robot controller, which is encoded with potential fields. Simulation results demonstrate the algorithm converging for a realistic problem after just three outer-loop iterations.