Datensatz

Zusammenfassung

The advances made in the last two decades have allowed robotic platforms, and in particular mobile robots, to successfully address a large variety of tasks, albeit mainly repetitive and simple ones. However, real-world applications typically involve complex decision making processes and non structured environments thus requiring a level of perception/world awareness and cognitive capabilities that cannot yet be provided by a robot. For this reason it is convenient, if not mandatory, to have a human supervising the execution. The robot shared control framework (see, e.g., [1], [2]) represents a promising step in this direction, since it allows to merge robots (limited) autonomy and humans cognitive capabilities. Previous studies have applied this idea to mobile robots navigating in cluttered environments, with an emphasis on bilateral shared control architectures with haptic feedback for the human operator. Typically, the operator commands a motion (desired position, reference velocity) to the robot via a haptic device. The robot executes the command while retaining some autonomy in order to, e.g., avoid obstacles or other dangers. Finally, the loop is closed by rendering on the haptic feedback a force that is proportional to the mismatch between commanded and executed motion in order to increase the operator’s situational awareness. Despite being an effective approach, commanding direct motion inputs requires a high commitment of the human, especially when the task is very complex or the environment is highly cluttered. Therefore, we propose an extension to the shared control in which an operator acts at the planning level, in order to modify some characteristics of the task but without the burden of directly driving the robot [3]. We assume that a task scheduler generates an initial trajectory based only on prior information. The trajectory is described as i) a geometric path controls to the set of parameters x, allowing the user to command some global behavior, e.g. translations or rotations of the curve. At the same time, the robot must track the generated trajectory and, whenever needed, modify it in real time in order to avoid collisions or to reach a nearby target. In particular, the robot performs both a reactive deformation of the reference trajectory and a planning of alternative paths. Finally, the bilateral component of the human-robot interaction is realized by feeding back to the operator a force cue informative of the global deformation acting on the desired path rather than on a local mismatch between commanded and executed position/velocity. Summarizing, the novel elements of this approach are: i) broadening the classical shared control approach by endowing the mobile robot with a higher planning autonomy, ii) allowing a human operator to act at the planning level rather than at the motion control level, iii) generating a force cue informative of the global deformation of the desired path rather than of the mismatch between direct motion commands and their execution. The proposed method has been extensively tested with human/hardware in-the-loop simulations, featuring a physically simulated quadrotor aerial vehicle and a haptic device (see Fig. 1).