Integrated Robot Motion and Process control for manufacturing reshaping

The future of metal manufacturing processes like laser cutting, welding, and additive manufacturing shall rely on intelligent systems spearheaded by Industry 4.0. Such a digital innovation is indeed driving machinery builders to a profound transformation. From custom machines designed and optimized for a specific process, the ambition is to exploit the openness and the large availability of industrial robots to increase flexibility and reconfigurability of multi processes implementations. The challenge is that machinery builders transform themselves into high-knowledge specialized process-driven robot integrators, able to optimize the robot motion with the process controller leveraging on intelligent sensing and cognition. The work describes the multi-annual collaboration of the BLM group and Politecnico di Milano, with the support of CNR, focused on the deployment of a complete working robotic workstation characterized by the full integration of the robot control and motion planning with manufacturing processes

Control and Model-Aided Inertial Navigation of a Nonholonomic Vehicle

International audienceThe present work deals with the control and localization problem of wheeled-mobile robots with nonholonomic constraints. In the proposed method a simple nonlinear control law, composed of a position and heading direction controller, is designed to asymptotically stabilize the position error. The control law takes into account the constraints on the control signals in order to avoid saturation of the actuators. Furthermore, this paper considers a method of using the dynamic vehicle model and vehicle's nonholonomic constraints in order to aid position and attitude estimates provided by an Inertial Navigation System (INS). It is shown that dynamic model and vehicle's nonholonomic constraints can reduce the error growth in robot position estimates. Simulations are included to confirm the effectiveness of the proposed scheme