arXiv subjects

Marco Faroni

Publications and source records attributed to Marco Faroni.

21 records · Page 2Linked to original sources

arXiv2023

Hiding task-oriented programming complexity: an industrial case study

The ease of use of robot programming interfaces represents a barrier to robot adoption in several manufacturing sectors because of the need for more expertise from the end-users. Current robot programming methods are mostly the past heritage, with robot programmers reluctant to adopt new programming paradigms. This work aims to evaluate the impact on non-expert users of introducing a new task-oriented programming interface that hides the complexity of a programming framework based on ROS. The paper compares the programming performance of such an interface with a classic robot-oriented programming method based on a state-of-the-art robot teach pendant. An experimental campaign involved 22 non-expert users working on the programming of two industrial tasks. Task-oriented and robot-oriented programming showed comparable learning time, programming time and the number of questions raised during the programming phases, highlighting the possibility of a smooth introduction to task-oriented programming even to non-expert users.

cs.RO↗

arXiv2022

Learning Action Duration and Synergy in Task Planning for Human-Robot Collaboration

A good estimation of the actions' cost is key in task planning for human-robot collaboration. The duration of an action depends on agents' capabilities and the correlation between actions performed simultaneously by the human and the robot. This paper proposes an approach to learning actions' costs and coupling between actions executed concurrently by humans and robots. We leverage the information from past executions to learn the average duration of each action and a synergy coefficient representing the effect of an action performed by the human on the duration of the action performed by the robot (and vice versa). We implement the proposed method in a simulated scenario where both agents can access the same area simultaneously. Safety measures require the robot to slow down when the human is close, denoting a bad synergy of tasks operating in the same area. We show that our approach can learn such bad couplings so that a task planner can leverage this information to find better plans.

cs.RO↗

arXiv2022

Safety-aware time-optimal motion planning with uncertain human state estimation

Human awareness in robot motion planning is crucial for seamless interaction with humans. Many existing techniques slow down, stop, or change the robot's trajectory locally to avoid collisions with humans. Although using the information on the human's state in the path planning phase could reduce future interference with the human's movements and make safety stops less frequent, such an approach is less widespread. This paper proposes a novel approach to embedding a human model in the robot's path planner. The method explicitly addresses the problem of minimizing the path execution time, including slowdowns and stops owed to the proximity of humans. For this purpose, it converts safety speed limits into configuration-space cost functions that drive the path's optimization. The costmap can be updated based on the observed or predicted state of the human. The method can handle deterministic and probabilistic representations of the human state and is independent of the prediction algorithm. Numerical and experimental results on an industrial collaborative cell demonstrate that the proposed approach consistently reduces the robot's execution time and avoids unnecessary safety speed reductions.

cs.RO↗