Contributions to active visual estimation and control of robotic systems

Abstract

As every scientist and engineer knows, running an experiment requires a careful and thorough planning phase. The goal of such a phase is to ensure that the experiment will give the scientist as much information as possible about the process that she/he is observing so as to minimize the experimental effort (in terms of, e.g., number of trials, duration of each experiment and so on) needed to reach a trustworthy conclusion. Similarly, perception is an active process in which the perceiving agent (be it a human, an animal or a robot) tries its best to maximize the amount of information acquired about the environment using its limited sensor capabilities and resources. In many sensor-based robot applications, the state of a robot can only be partially retrieved from his on-board sensors. State estimation schemes can be exploited for recovering online the “missing information” then fed to any planner/motion controller in place of the actual unmeasurable states. When considering non-trivial cases, however, state estimation must often cope with the nonlinear sensor mappings from the observed environment to the sensor space that make the estimation convergence and accuracy strongly affected by the particular trajectory followed by the robot/sensor. For instance, when relying on vision-based control techniques, such as Image-Based Visual Servoing (IBVS), some knowledge about the 3-D structure of the scene is needed for a correct execution of the task. However, this 3-D information cannot, in general, be extracted from a single camera image without additional assumptions on the scene. One can exploit a Structure from Motion (SfM) estimation process for reconstructing this missing 3-D information. However performance of any SfM estimator is known to be highly affected by the trajectory followed by the camera during the estimation process, thus creating a tight coupling between camera motion (needed to, e.g., realize a visual task) and performance/accuracy of the estimated 3-D structure. In this context, a main contribution of this thesis is the development of an online trajectory optimization strategy that allows maximization of the converge rate of a SfM estimator by (actively) affecting the camera motion. The optimization is based on the classical persistence of excitation condition used in the adaptive control literature to characterize the well-posedness of an estimation problem. This metric, however, is also strongly related to the Fisher information matrix employed in probabilistic estimation frameworks for similar purposes. We also show how this technique can be coupled with the concurrent execution of a IBVS task using appropriate redundancy resolution and maximization techniques. All of the theoretical results presented in this thesis are validated by an extensive experimental campaign run using a real robotic manipulator equipped with a camera in-hand.