Control of Large Vehicle-Manipulators with Human Operator

Abstract

This paper addresses a control algorithm for mid-sized heavy-duty vehicles with a working manipulator to support the human operator. Such vehicle-manipulators (VM) are used for ditch cleaning, landscape maintenance or other farming works, like grass mowing. The operation of such systems is challenging, as the human operator has to deal with a dual task: the vehicle has to stay on its path, and the manipulator has to follow its trajectory to fulfil its task. This task is physiologically and physically demanding and requires an expert human worker. To increase the efficiency, to relieve the human worker and to reduce the training time of novice operators, we propose a control concept to automate the vehicle’s steering, in such a way that the vehicle supports the operator. Another advantage of this concept is the lack of the necessity of additional sensors to observe the states of the manipulator. The concept requires the inputs solely from the operator, which is suitable for a real-world application. The dynamic equation of the system is an extended state-of-the-art vehicle model in Frenét-frame. An additional system state is derived from the dynamic model of the vehicle-manipulator to take the human actions into account. This additional state influences the motion of the manipulator when it is required. Such assistance is beneficial if the manipulator cannot follow its trajectory fast enough. The control concept is tested and verified in simulations, where the human is modelled as an optimal controller, to emphasize the advantages of the proposed concept.