Improving teleoperation with models and tasks [electronic resource]
Keywords: Mechanical Engineering

In modern times, humans are capable of reaching beyond their innate physical limits by employing robotic devices to conduct tasks once considered impossible. While simple and repetitive tasks do not require human intervention, more complex tasks still need human intelligence for successful execution. This is especially true in unstructured environments, where tasks need to be continuously evaluated against the developing environment. In other words, a human operator is needed to perform any meaningful operation in such environments. Teleoperation combines human intelligence and robots' mechanical advantages to facilitate operations in challenging environments. In traditional teleoperation, the master and slave robots exchange low level position and force sensor measurements to establish a connection. Such information is temporal in a sense that it is expected to flow continuously between the two devices. Naturally, the teleoperator is highly susceptible to lags in the system causing instability. A common practice to get around the issue is to reduce the feedback, which creates distortion in perception as the feedback signal often needs to be severely attenuated to guarantee stability. As a result, subtle nuances in the feedback are easily washed out and become undetectable, resulting in interactions that do not feel natural to the user. Perhaps, the unnatural feelings come from the mismatch between how humans interact with the environment and how the teleoperator is set up to perform remote tasks. Humans are known to develop internal models from extensive interactions. Such models take account of the dynamics of human body and the environment to plan and optimally execute a task. During interactions, the behavioral plan is continuously reinforced or adjusted based on sensory feedbacks. Knowing what to expect from the environment is crucial for humans to carry out natural interactions. Model-mediation attempts to replicate this process in teleoperation, so that interactions are stable despite lags in the system, and therefore feel more natural to the user. The fundamental assumption of model-mediation is that the environment, and hence its model description, does not change rapidly. Slow model changes reduce the bandwidth requirement of the teleoperator, allowing it to be more tolerant to lags in the system coming from communication delays, large slave inertia or friction, among others. In fact, the model-mediation architecture promotes independent master and slave operations through the uses of models and tasks, so that local interactions do not depend on feedbacks from remote interactions. The separation is achieved by local interpretations of the abstract model and task information, and contributes to the increased tolerance to lags. Compared to traditional teleoperators, the model-mediated teleoperator shows better performance and stability against various types of environments. Contacts with the rigid environment are stable, and the user does not feel any extra force in free space, therefore combining the best of the traditional teleoperators while improving their shortcomings at the same time.