In collaborative robotic applications, human and robot have to work together during a whole shift for executing a sequence of jobs. The performance of the human robot team can be enhanced by scheduling the right tasks to the human and the robot. The scheduling should consider the task execution constraints, the variability in the task execution by the human, and the job quality of the human. Therefore, it is necessary to dynamically schedule the assigned tasks. In this paper, we propose a two-layered architecture for task allocation and scheduling in a collaborative cell. Job quality is explicitly considered during the allocation of the tasks and over a sequence of jobs. The tasks are dynamically scheduled based on the real time monitoring of the human's activities. The effectiveness of the proposed architecture is experimentally validated.

The new paradigm of human-robot collaboration has led to the creation of shared work environments in which humans and robots work in close contact with each other. Consequently, the safety regulations have been updated addressing these new scenarios. The mere application of these regulations may lead to a very inefficient behavior of the robot. In order to preserve safety for the human operators and allow the robot to reach a desired configuration in a safe and efficient way, a two layers architecture for trajectory planning and scaling is proposed. The first layer calculates the nominal trajectory and continuously adapts it based on the human behavior. The second layer, which explicitly considers the safety regulations, scales the robot velocity and requests for a new trajectory if the robot speed drops. The proposed architecture is experimentally validated on a Pilz PRBT manipulator

Effective task scheduling in human-robot collaboration (HRC) scenarios is one of the great challenges of collaborative robotics. The shared workspace inside an industrial setting brings a lot of uncertainties that cannot be foreseen. A prior offline task scheduling strategy is ineffective in dealing with these uncertainties. In this paper, a novel online framework to achieve a resilient and reliable task schedule is presented. The framework can deal with deviations that occur during operation, different operator skills, error by the human or robot, and substitution of actors, while maintaining an efficient schedule by promoting parallel human-robot work. First, the collaborative job and the possible deviations are represented by AND/OR graphs. Subsequently, the proposed architecture chooses the most suitable path to improve the collaboration. If some failures occur, the AND/OR graph is adapted locally, allowing the collaboration to be completed. The framework is validated in an industrial assembly scenario with a Franka Emika Panda collaborative robot.

The new face of modern industrial scenarios involves shared workspaces where humans and robots work closely together. To ensure safe human-robot collaboration (HRC), regulations have been updated introducing the ISO/TS15066. However, complying with these regulations often leads to inefficient behavior, such as unnecessarily reducing robot speed or unpredictably changing the robot path, which may negatively affect the operator perception of the robot. In this letter an optimal approach to address together these two issues is proposed. Starting from a desired final configuration, the framework plans a collision-free trajectory for the robot. Subsequently, predictability is taken into account and a set of virtual tubes into which the path of the robot can move is built. Lastly, an optimization problem is solved online to ensure that the robot stays within these tubes and the velocities are compliant with the ISO/TS 15066. The proposed approach has been experimentally validated in two different scenarios: one composed by a mobile manipulator, i.e. a UR10e mounted on a Neobotix MPO-500, and one composed by only a collaborative manipulator, i.e. a UR5e.

The integration of robots into shared workspaces alongside humans is the basis of Human-Robot Collaboration (HRC). This field of research has changed the paradigm of the industrial context, making HRC of pivotal importance for both researchers and the industry. In this context, a suitable task scheduling and trajectory planning strategy are crucial to achieve good performances and create a synergy between the two actors. Indeed, the task scheduling should be able to optimally distribute the tasks between the actors and recover from possible failures, i.e. by rescheduling the tasks. The trajectory planning strategy must comply with the safety standards that impose a reduction of velocity based on human behaviour. To this end, the monitoring system must also be safe-certified; otherwise, safety cannot be guaranteed. This paper proposes a novel architecture that integrates a dynamic task scheduling module with a dynamic trajectory planning module that explicitly considers ISO/TS 15066. For this purpose, the framework exploits a secure and certified monitoring system capable of tracking the human operator even in case of occlusions. The overall platform has been extensively validated both in a real and complex industrial scenario within the context of the ROSSINI EU project, where a dual-arm mobile robot collaborates with a human operator in an automatic machine-tending operation, and in a mock-up scenario.

In this letter, we detail a comprehensive framework for safe and robust planning for robots in presence of model uncertainties. Our framework is based on the recent notion of closed-loop state sensitivity , which is extended in this work to also include uncertainties in the initial state. The proposed framework, which considers the sensitivity of the nominal closed-loop system w.r.t. both model parameters and initial state mismatches, is exploited to compute tubes that accurately capture the worst-case effects of the considered uncertainties. In comparison to the current state-of-the-art for safe and robust planning, the proposed closed-loop state sensitivity framework has the important advantage of computational simplicity and minimal assumptions (and simplifications) regarding the underlying robot closed-loop dynamics. The approach is validated via both extensive simulations and real-world experiments. In the experiments we consider as case study a nonlinear trajectory optimization problem aimed at generating an intrinsically robust and safe trajectory for an aerial robot for safely performing an obstacle avoidance maneuver despite the uncertainties. Simulation and experimental results further confirm the viability and interest of the proposed approach.