Section: New Results

Sensor-based Robot Control

Determining Singularity Configurations in IBVS

Participant : François Chaumette.

This theoretical study has been achieved through an informal collaboration with Sébastien Briot and Philippe Martinet from IRCCyN in Nantes, France. It concerns the determination of the singularity configurations of image-based visual servoing using tools from the mechanical engineering community and the concept of “hidden” robot. In a first step, we have revisited the welknown case of using three image points as visual feature, and then solved the general case of n image points [22]. The case of three image straight lines has also been solved for the first time [23].

Interval-based IBVS convergence domain computation

Participant : Vincent Drevelle.

This work aims to compute the set of camera poses from which IBVS will converge to the desired pose (that corresponds to the reference image). Starting from a (small) initial attraction domain of the desired pose (obtained using Lyapunov theory), we employ subpavings and guaranteed integration to iteratively increase the proven convergence domain, using a viability-based approach. Image-domain and pose-domain constraints are also enforced, like feature points visibility or workspace boundaries. First results have been obtained for a 3DOF line-scan camera IBVS case [56].

Visual Servoing of Humanoid Robots

Participants : Giovanni Claudio, Don Joven Agravante, Fabien Spindler, François Chaumette.

This study is realized in the scope of the BPI Romeo 2 and H2020 Comanoid projects (see Sections 9.2.7 and

In a first step, we have considered classical kinematic visual servoing schemes for gaze control and manipulation tasks, such as can or box grasping. Two-hand manipulation has also been achieved using a master/slave approach [53][81]. In a second step, we have designed the modeling of the visual features at the acceleration level to embed visual tasks and visual constraints in an existing QP controller [20][80]. Experimental results have been obtained on Romeo (see Section 6.9.4).

Model Predictive Visual Servoing

Participants : Nicolas Cazy, Paolo Robuffo Giordano, François Chaumette.

This study is realized in collaboration with Pierre-Brice Wieber, from Bipop group at Inria Rhône Alpes.

Model Predictive Control (MPC) is a powerful control framework able to take explicitly into account the presence of constraints in the controlled system (e.g., actuator saturations, sensor limitations, and so on). In this research activity, we studied the possibility of using MPC for tackling one of the most classical constraints of visual servoing applications, that is, the possibility to lose tracking of features because of occlusions, limited camera field of view, or imperfect image processing/tracking. The MPC framework depends upon the possibility to predict the future evolution of the controlled system over some time horizon, for correcting the current state of the modeled system whenever new information (e.g., new measurements) become available. We have also explored the possibility of applying these ideas in a multi-robot collaboration scenario where a UAV with a downfacing camera (with limited field of view) needs to provide localization services to a team of ground robots [13].

Model Predictive Control for Visual Servoing of a UAV

Participants : Bryan Penin, Riccardo Spica, François Chaumette, Paolo Robuffo Giordano.

Visual servoing is a welknown class of techniques meant to control the pose of a robot from visual input by considering an error function directly defined in the image (sensor) space. These techniques are particularly appealing since they do not require, in general, a full state reconstruction, thus granting more robustness and lower computational loads. However, because of the quadrotor underaction and inherent sensor limitations (mainly limited camera field of view), extending the classical visual servoing framework to the quadrotor flight control is not straightforward. For instance, for realizing a horizontal displacement the quadrotor needs to tilt in the desired direction. This tilting, however, will cause any downlooking camera to point in the opposite direction with, e.g., possible loss of feature tracking because of the limited camera field of view.

In order to cope with these difficulties and achieve a high-performance visual servoing of quadrotor UAVs, we are exploring the possibility of using techniques borrowed from Model-Predictive Control (MPC) for explicitly dealing with this kind of constraints during flight. Indeed, MPC is a class of (numerical) optimal control techniques able to explicitly take into account state and input constraints, as well as complex (and underactuated) nonlinear dynamics of the controlled system. In particular, the ability to predict, over some future time window, the behavior of the visual features on the image plane will allow the quadrotor to fly “blindly” for some limited phases, for then regaining tracking of any lost feature. This possibility will be crucial for allowing quick maneuvering guided by a direct visual feedback. We have started addressing the case of a simulated planar UAV as a representative case study, and we are now working towards an experimental validation with a real quadrotor UAV equipped with an onboard camera.

Visual-based shared control

Participants : Firas Abi Farraj, Nicolò Pedemonte, Paolo Robuffo Giordano.

This work concerns our activities in the context of the RoMaNS H2020 project (see Section Our main goal is to allow a human operator to be interfaced in an intuitive way with a two-arm system, one arm carrying a gripper (for grasping an object), and the other one carrying a camera for looking at the scene (gripper + object) and providing the needed visual feedback. The operator should be allowed to control the two-arm system in an easy way for letting the gripper approaching the target object, and she/he should also receive force cues informative of how feasible her/his commands are w.r.t. the constraints of the system (e.g., joint limits, singularities, limited camera fov, and so on).

We have started working on this topic by proposing a shared control architecture in which the operator could provide instantaneous velocity commands along four suitable task-space directions not interfering with the main task of keeping the gripper aligned towards the target object (this main task was automatically regulated). The operator was also receiving force cues informative of how much her/his commands were conflicting with the system constraints, in our case joint limits of both manipulators. Finally, the camera was always moving so as to keep both the gripper and the target object at two fixed locations on the image plane [46].

We have then extended this framework in two directions: first, by allowing the possibility of controlling a whole future trajectory for both arms (gripper+camera) while coping with the system constraints. The operator was then receiving an `integral' force feedback along the whole planned trajectory: in this way, the operator's actions and the corresponding force cues were function of a planned trajectory (thus, carrying information over a future time window) that could be manipulated at runtime. Second, we studied how to integrate learning from demonstration into our framework by first using learning techniques for extracting statistical regularities of `expert users' executing successful trajectories for the gripper towards the target object. Then, these learned trajectories were used for generating force cues able to guide novice users during their teleoperation task by the `hands' of the expert users who demonstrated the trajectories in the first place. Both works have been submitted to ICRA'2017.

Direct Visual Servoing

Participants : Quentin Bateux, Eric Marchand.

In the direct visual servoing methods such as photometric framework, the images as a whole are used to define the control law. This can be opposed to the classical visual servoing approaches that relies on geometric features and where image processing algorithms that extract and track visual features are necessary. In [21], we proposed a generic framework to consider histograms as visual features. A histogram is an estimate of the probability distribution of a variable (for example the probability of occurrence in an intensity, color, or gradient orientation in an image). We demonstrated that the framework we proposed applies, but is not limited to, a wide set of histograms and allows the definition of efficient control laws.

Nevertheless, the main drawback for the direct visual servoing class of methods comparing to the classical geometric visual servoing methods is their comparatively limited convergence range. We then proposed in [48] a new direct visual servoing control law that relies on a particle filter to perform non-local and non-linear optimization in order to increase the convergence domain. To each particle considered we associate a virtual camera that predicts the image it should capture by using image transfer techniques. This new control law has been validated on a 6 DOF positioning task performed on our Gantry robot (see Section 6.9.1).

Audio-based Control

Participants : Aly Magassouba, François Chaumette.

This study is concerned with the application of sensor-based control approach to audio sensors. It is made in collaboration with Nancy Bertin from Panama group at Irisa and Inria Rennes-Bretagne Atlantique. Auditory features such as Interaural Time Difference (ITD), Interaural Level Difference (ILD), and sound energy have been modeled and integrated in various control schemes to control the motion of a mobile robot with two microphones onboard [66], [64]. Experiments with Romeo and Pepper (see Section 6.9.4) have also been achieved [65]. They show the robustness of closed loop sensor-based control with respect to coarse modeling and that explicit sound source localization is not a mandatory step for aural servoing.