Human-robot collision avoidance
Robots are increasingly present in our lives, sharing the workspace with humans. Existing robots do not have autonomy to perceive its unstructured and time-varying surrounding environment, nor the ability to real-time avoid collisions with humans while keeping the task target. Human-robot collision avoidance is critical for robots acceptance as co-workers. Research will focus on the study of novel real-time collision avoidance techniques based on potential fields. Hypothetical repulsion and attraction vectors are computed considering not only the human-robot minimum distance but also the relative velocities, joint limits, redundancy and the Goals-Non-Reachable-with-Obstacle-nearby (GNRON) effect. A controller using Newton method (Hessian) will allow reducing robot vibration escaping to local minima. Kinematics and dynamics controllers ensure a smooth path control at joint and end-effector level. Robot(s) and human(s) are modelled by geometric primitives to mutually compute the analytical minimum distance between them. The proposed methodology will be validated with a real collaborative robot.
Multi Robot coordination and internal logistics
High loads and/or repetitive tasks that put at risk people’s ergonomy have been automated, mainly in internal logistics. For this scenario, either mobile robotics or mobile manipulators are used in the manipulation and transportation of materials to assist the operator. To maximize the work efficiency, a ROS-based framework incorporating an A* algorithm was used to manage the robot fleet, defining a navigation strategy that avoids collisions by exchanging information between each robotic platform. The centralised information allows to create a global map and a 3D world-model of the logistic environment by fusing the data from each robot. The proposed methodology was tested in simulation with 3 and 10 robots and validated in a real scenario with 4 robots.
Application case to the automotive sector
Following the topic of mobile robot coordination, a case study was performed, targeting the automotive sector. It focused on object manipulation and transportation, given the low automation level of intralogistics operations in the automotive sector. The demonstration environment was carefully designed, to illustrate the intralogistics challenges faced by operators in real industrial scenarios. The fleet of robots was able to successfully cooperate and autonomously navigate across the environment while receiving orders from a supervising software. Moreover, the integration of robotic manipulators allowed them to perform picking tasks and also placing objects in regular boxes, and afterward, storing them on a given position of a shelf, aiming for the construction of kits.