Nowadays, factories are required to increase production flexibility in order to manufacture small-lot variants, rapidly adapting to customer demands. Furthermore, manufacturing may involve complex manipulation tasks, usually performed by human workers. In such a context, traditional robotic systems are not competitive due to the huge costs of installation, maintenance and adaptation. A new generation of robots, equipped with multiple arms, is appearing as an attractive alternative because of their potential versatility and ability to execute intricate manipulation tasks. To facilitate the integration of these robots in a work-cell and a rapid adaptation to different tasks, easy-to-use programming interfaces and a high degree of autonomy are mandatory. Autonomous task and motion planning are particularly relevant in this context. In this paper, we present our recent progress in this direction. Hardware and software developments are explained in the context of a pilot dual-arm robot station that is being integrated in the production line of a big airplane manufacturer. First experimental results are also presented.

To make production lines more flexible, dual-arm robots are good candidates to be deployed in autonomous assembly units. In this paper, we propose a sparse kinematic control strategy, that minimizes the number of joints actuated for a coordinated task between two arms. The control strategy is based on a hierarchical sparse QP architecture. We present experimental results that highlight the capability of this architecture to produce sparser motions (for an assembly task) than those obtained with standard controllers.

A robust dynamic feedback controller is designed and implemented, based on the dynamic model of the six-wheel skid-steering RobuROC6 robot, performing high speed turns. The control inputs are respectively the linear velocity and the yaw angle. The main object of this paper is to elaborate a sliding mode controller, proved to be robust enough to ignore the knowledge of the forces within the wheel-soil interaction, in the presence of sliding phenomena and ground level fluctuations. Finally, a 3D simulation is performed with an accurate physical engine to evaluate the efficiency of this designed control law.

This paper deals with design and implementation of a stabilization algorithm for a car like robot performing high speed turns. The control of such a kind of system is rather difficult because of the complexity of the physical wheel- soil interaction model. In this paper, it is planned to analyze the complex dynamic model of this process to elaborate a stabilization algorithm only based on the measurement of the system yaw rate. Finally, a 3D simulation is performed to evaluate the efficiency of this designed stabilization algorithm.

This paper introduces an original, dextrous, active and modular minimally invasive instrument dedicated to coronary artery bypass grafting surgery. Its design is obtained by a generic evolutionary optimization process using Pareto-based multi objective genetic algorithms and including highly realist simulations and experimental models of the surgical gesture. The optimal instrument has 5 intra-body DOFs, is actuated by brush-less micro motors and is position controlled. Its optimization, mechanical design and performances are discussed.