In manufacturing, kitting is the process of grouping separate items together to be supplied as one unit to the assembly line. This is a key logistic task, which is usually performed manually by human operators. However, picking objects from the warehouse implies a great repetitiveness in arm motion. Moreover, the weight and position of items may increase the physical strain and induce the development of work-related musculoskeletal disorders. The inclusion of a collaborative robot in the process may help to reduce the operator's effort and increase productivity. This paper introduces an online scheduling algorithm to guide the picking operations of the human and the robot. The proposed approach has been experimentally evaluated and compared with an offline scheduler, as well as with the baseline case of manual kitting.

A crucial problem in human-robot collaboration is to achieve seamless coordination among the agents. Robots have to adapt to human behaviour, which is highly uncertain. In fact, humans can perform each task in many ways and with different speeds, occasional errors and short pauses. This paper offers a robust method to monitor the advancement of the current human activity in real-time in order to predict its duration. The algorithm learns online templates of new variants of the task and uses them as references for a Dynamic Time Warping-based algorithm. The proposed strategy has been tested within a realistic assembly task. Results show its ability to give accurate predictions also in case of peculiar variants, such as those associated with errors.

In collaborative robotics applications, human behaviour is a major source of uncertainty. Predicting the evolution of the current human activity might be beneficial to the effectiveness of task planning, as it enables a higher level of coordination of robot and human activities. This paper addresses the problem of monitoring the advancement of human tasks in real-time giving an estimate of their expected duration. The proposed method relies on dynamic time warping to align the current activity with a reference template. No training phase is required, as the prototypical execution is learnt online from previous instances of the same activity. The applicability and performance of the method within an industrial context have been verified on a realistic assembly task.

Handling liquids with spilling avoidance is a topic of interest for a broad range of fields, both in industry and in service robotic applications. In this paper we present a new control architecture for motion planning of industrial robots, able to tackle the problem of liquid transfer with sloshing control. We do not focus on a complete sloshing suppression, but we show how to enforce an anti spilling constraint. This less conservative approach allows to impose higher accelerations, reducing motion time. A constraint-based approach, amenable to an Online implementation, has been developed. The proposed controller generates trajectories in real time, in order to follow a reference path, while being compliant to the spilling avoidance constraint. The approach has been validated on a 6 degree of freedom industrial ABB robot.