Real-Time Online Adaptive Feedforward Velocity Control for Unmanned Ground Vehicles

Online adaptation of motion models enables autonomous robots to move more accurate in case of unknown disturbances. This paper proposes a new adaptive compensation feedforward controller capable of online learning a compensation motion model without any prior knowledge to counteract non-modeled disturbance such as slippage or hardware malfunctions. The controller is able to prevent motion errors a priori and is well suited for real hardware due to high adaptation rate. It can be used in conjunction with any motion model as only motion errors are compensated. A simple interface enables quick deployment of other robot systems as demonstrated in Small Size and Rescue Robot RoboCup leagues.