Robots are nowadays faced with the challenge of handling deformable objects in industrial operations. In particular, the problem of shape control, which aims at giving a specific deformation state to an object, has gained interest recently in the research community. Among the proposed solutions, approaches based on finite elements proved accurate and reliable but also complex and computationally-intensiveIn order to mitigate these drawbacks, we propose a scheme for shape control that does not require to run a real-time simulation or to solve an implicit optimization problem for computing the control outputs. It is based on a partition of the nodal coordinates that allows deriving a control law directly from tangent stiffness matrices. This formulation is also coupled with the introduction of reduced finite element models. Simulation and experimental results in the context of linear deformable object manipulation demonstrate the interest of the proposed approach.

In this paper, the control problem for a group of mobile robots keeping a geometric formation is considered. The proposed architecture of control allows to each robot to avoid obstacles and to rejoin the desired formation. To not complicate the control of such a system, it is proposed to divide the overall complex task into two basic tasks: attraction to a dynamical target, and obstacle avoidance. Thus, a desired geometric shape is defined and each robot has to track one node of this mobile shape. Each robot has to be autonomously able to avoid disturbing obstacles and to rejoin the formation in a reactive manner. Moreover, it chooses the optimal avoidance side thanks to limit-cycle method in order to reach as rapidly as possible its virtual target. The proposed control architecture is implemented in a distributed manner. In addition, this architecture uses the same control law (Lyapunov stable) for the two elementary tasks, and the switching from one task to another occurs only by changing the set-points. Experimental results validate the proposed control architecture.

In this paper, we present a method to efficiently manage visual memory for autonomous vehicle navigation in large scale environments. It relies on two crucial issues for real-time navigation: an efficient organisation of the memory and small computational cost. A software platform (SoViN) dedicated to visual memory management and navigation strategies (including vision-based memory building, localization and navigation) has been developed to fulfill these requirements. We show that using this software architecture makes possible real-time navigation in large-scale outdoor situation using a single camera and natural landmarks.