The novel paradigm of collaborative automation, with machines and industrial robots that synergically share the same workspace with human workers, requires to rethink how activities are prioritized in order to account for possible variabilities in their durations. This article proposes a scheduling method for collaborative assembly tasks that allows to optimally plan assembly activities based on the knowledge acquired during runtime and so adapts to variations along the life cycle of a manufacturing process. The scheduler is based on time Petri nets and the output plan is optimized by minimizing the idle time of each agent. The experimental validation carried out on a realistic industrial use-case consisting of a small assembly line with two robots and a human operator confirms the effectiveness of the approach.

Prediction of the human behaviour is essential for allowing an efficient human-robot collaboration. This was confirmed recently showing how scheduling approaches can significantly increase the productivity of a robotic cell by planning the robotic actions in a way as much as possible compliant with the human predicted behaviour. This work proposes an innovative approach for human activity prediction, exploiting both a-priori information and knowledge revealed during operation. The resulting approach is proved to achieve good performance through both off-line simulated sequences and in a realistic co-assembly involving a human operator and a dual arm collaborative robot.

The coexistence of humans and robots in the future production plants is one of the pillars of Industry 4.0. Humans and robots will collaborate to accomplish common tasks in order to mutually compensate their deficiencies. In recent years, many efforts have been spent to develop safe motion planning strategies, designed to prevent robots from injuring humans. Most of the previous techniques are classifiable as reactive, since the considered motion controllers impose some local corrective actions in order to dodge the space occupied by the human. In this paper, a proactive approach is adopted, optimizing robotic paths according to a prediction of the volume occupied by the human when collaborating with the robot. The validity of the approach is shown in a realistic use-case involving the collaboration of a human operator with a 7 degrees robotic arm, the ABB YuMi.

Letting humans and robots share a common space for collaboration is considered a consolidated practice. The trajectories followed by the robot must be safe for the human mate, especially when the robot holds dangerous tools or parts. At the same time, the productivity must be preserved, without imposing too restrictive limitations on the robot's movements. This article proposes the use of Gaussian Processes to predict the motion of an operator in a robotic cell, with the aim of controlling the robot speed and avoid collisions. An adaptive approach is proposed and the model for the human motion is persistently re-updated. The resulting approach will be demonstrated to be less conservative than previous ones, while at the same time to preserve the safety of the operator. Real experiments have been conducted on the 7 d.o.f. ABB YuMi robot.

Rapidly Random exploring Trees are popular algorithms in the field of motion planning. A feasible path connecting two different poses is found by incrementally building a tree data structure. They are powerful and flexible, but also computationally intense, requiring thousands of iterations before their termination. The aim of this article is to show the capabilities of MT-RRT, a general purpose library which exploits four different multithreading strategies to speed up the planning process of Rapidly Random exploring Trees. MT-RRT will be proved to significantly reduce the computation time on various benchmarks.

Scan chain-based testing is a de facto standard for guaranteeing quality of manufactured digital circuits. However, functional approaches are often used to complement test suites, especially when analog circuitry is integrated in the chip. Software-Based Self-Test (SBST) can be used to increase defect coverage also in digital parts, or to replace part of the scan pattern set to reduce tester requirements, or to complement the defect coverage achieved by structural techniques when advanced semiconductor technologies introduce new defect types. This paper deals with SBST targeting transition delay faults, and describes a case of study based on a peripheral module integrated in a System on Chip (SoC). A method to develop an effective functional test is first described. A comparative analysis of the delay faults detected by scan and SBST is then presented, together with some discussion about the obtained results.

It is widely agreed that future manufacturing environments will be populated by humans and robots sharing the same workspace. However, the real collaboration can be sporadic, especially in the case of assembly tasks, which might involve autonomous operations to be executed by either the robot or the human worker. In this scenario, it might be beneficial to predict the actions of the human in order to control the robot both safely and efficiently. In this paper, we propose a method to predict human activity patterns in order to early infer when a specific collaborative operation will be requested by the human and to allow the robot to perform alternative autonomous tasks in the meanwhile. The prediction algorithm is based on higher-order Markov chains and is experimentally verified in a realistic scenario involving a dual-arm robot employed in a small part collaborative 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.

Collaborative robotics over the last few years has gained increasing interest in the industrial scenario. Co-bots can be equipped with vision sensors and cognitive software layers, allowing the robot to figure out human intentions. To make this level of perception possible, human pose estimation algorithms are required. Several techniques have been already proposed to tackle this problem, which however present some weaknesses in particular when occlusions occur. This work proposes an algorithm for human pose estimation in the situations of partial occlusion, based on particle filter techniques. We have proved its validity in a realistic human-robot coexistence scenario, where a human and a dual arm robot have to perform tasks in a shared workspace.

This paper addresses time-optimal path-constrained trajectory planning. Given a geometric path for a manipulator, this paper focuses on the selection of the time law along the path. This law minimizes the time required to complete the path and at the same time is consistent with constraints, both at kinematic and dynamic levels. To obtain the optimal law a decision algorithm for the acceleration along the path has been developed. Remarkably, the algorithm is amenable to online implementation, thus allowing for path replanning. An experimental validation on an ABB IRB140 robot is shown.