Singularity Avoidance for Cart-Mounted Hand-Guided Collaborative Robots: A Variational Approach

Most collaborative robots (cobots) can be taught by hand guiding: essentially, by manually jogging the robot, an operator teaches some configurations to be employed as via points. Based on those via points, Cartesian end-effector trajectories such as straight lines, circular arcs or splines are then constructed. Such methods can, in principle, be employed for cart-mounted cobots (i.e., when the jogging involves one or two linear axes, besides the cobot axes). However, in some applications, the sole imposition of via points in Cartesian space is not sufficient. On the contrary, albeit the overall system is redundant, (i) the via points must be reached at the taught joint configurations, and (ii) the undesirable singularity (and near-singularity) conditions must be avoided. The naive approach, consisting of setting the cart trajectory beforehand (for instance, by imposing a linear-in-time motion law that crosses the taught cart configurations), satisfies the first need, but does not guarantee the satisfaction of the second. Here, we propose an approach consisting of (i) a novel strategy for decoupling the planning of the cart trajectory and that of the robot joints, and (ii) a novel variational technique for computing the former in a singularity-aware fashion, ensuring the avoidance of a class of workspace singularity and near-singularity configurations

Implementation of a collaborative robot application for closures' quality control

Among the processes that SACMI IMOLA s.c.r.l has internally, quality control certainly plays a fundamental role. In particular, Between all the automatic machines that the company produces, the CCM48, a continuous compression moulding machine able, though 48 pistons, to create over 2000 plastic closures / min with max closure diameter 38 mm, is under the lens. The intent to make the machine competitive on the market leads to the necessity to create an excellent product. This makes quality control of the latter, an aspect of fundamental importance. In this regard, today the closure's control quality procedure is made by two operators that manually, cooperate together. The task is characterized by: LOW frequency, the quality control analysis is realized only once a day on a batch; HIGH repetability, the complete procedure need to be iterate for each cap. Moreover, the operations that are performed by the operators, might be automated through: selection of proper vision sensors for images acquisition, computer vision's algorithms for defects detection and robotic product handling. This is precisely the work that has been carried out in this industrial thesis. The goal is to increase the level of automation in the inspection of the product to highlight possible defects in the process and therefore in the machine. For this purpose, as will be highlighted later, it has been evaluated the possibility to install a collaborative robot to perform this task. The main reason is due to the necessity to do not enormously modify the environment, considering to let the operator works with the robot in a shared workspace. All the source code used to simulate the environment is available on the personal gitHub account \url{https://github.com/EdoardoG94/ThesisFolder}

Computer- and robot-assisted Medical Intervention

Medical robotics includes assistive devices used by the physician in order to make his/her diagnostic or therapeutic practices easier and more efficient. This chapter focuses on such systems. It introduces the general field of Computer-Assisted Medical Interventions, its aims, its different components and describes the place of robots in that context. The evolutions in terms of general design and control paradigms in the development of medical robots are presented and issues specific to that application domain are discussed. A view of existing systems, on-going developments and future trends is given. A case-study is detailed. Other types of robotic help in the medical environment (such as for assisting a handicapped person, for rehabilitation of a patient or for replacement of some damaged/suppressed limbs or organs) are out of the scope of this chapter.Comment: Handbook of Automation, Shimon Nof (Ed.) (2009) 000-00

Perception of Deformable Objects and Compliant Manipulation for Service Robots

Abstract We identified softness in robot control as well as robot perception as key enabling technologies for future service robots. Compliance in motion control compensates for small errors in model acquisition and estimation and enables safe physical interaction with humans. The perception of shape similarities and defor-mations allows a robot to adapt its skills to the object at hand, given a description of the skill that generalizes between different objects. In this paper, we present our approaches to compliant control and object manipulation skill transfer for service robots. We report on evaluation results and public demonstrations of our ap-proaches. 1

An Overview of Industrial Robots Control and Programming Approaches

Nowadays, manufacturing plants are required to be flexible to respond quickly to customer demands, adapting production and processes without affecting their efficiency. In this context, Industrial Robots (IRs) are a primary resource for modern factories due to their versatility which allows the execution of flexible, reconfigurable, and zero-defect manufacturing tasks. Even so, the control and programming of the commercially available IRs are limiting factors for their effective implementation, especially for dynamic production environments or when complex applications are required. These issues have stimulated the development of new technologies that support more efficient methods for robot control and programming. The goal of this research is to identify and evaluate the main approaches proposed in scientific papers and by the robotics industry in the last decades. After a critical review of the standard IR control schematic, the paper discusses the available control alternatives and summarizes their characteristics, range of applications, and remaining limitations

Augmented reality (AR) for surgical robotic and autonomous systems: State of the art, challenges, and solutions

Despite the substantial progress achieved in the development and integration of augmented reality (AR) in surgical robotic and autonomous systems (RAS), the center of focus in most devices remains on improving end-effector dexterity and precision, as well as improved access to minimally invasive surgeries. This paper aims to provide a systematic review of different types of state-of-the-art surgical robotic platforms while identifying areas for technological improvement. We associate specific control features, such as haptic feedback, sensory stimuli, and human-robot collaboration, with AR technology to perform complex surgical interventions for increased user perception of the augmented world. Current researchers in the field have, for long, faced innumerable issues with low accuracy in tool placement around complex trajectories, pose estimation, and difficulty in depth perception during two-dimensional medical imaging. A number of robots described in this review, such as Novarad and SpineAssist, are analyzed in terms of their hardware features, computer vision systems (such as deep learning algorithms), and the clinical relevance of the literature. We attempt to outline the shortcomings in current optimization algorithms for surgical robots (such as YOLO and LTSM) whilst providing mitigating solutions to internal tool-to-organ collision detection and image reconstruction. The accuracy of results in robot end-effector collisions and reduced occlusion remain promising within the scope of our research, validating the propositions made for the surgical clearance of ever-expanding AR technology in the future

Human-Robot Collaboration Enabled By Real-Time Vision Tracking

The number of robotic systems in the world is growing rapidly. However, most industrial robots are isolated in caged environments for the safety of users. There is an urgent need for human-in-the-loop collaborative robotic systems since robots are very good at performing precise and repetitive tasks but lack the cognitive ability and soft skills of humans. To fill this need, a key challenge is how to enable a robot to interpret its human co-worker’s motion and intention. This research addresses this challenge by developing a collaborative human-robot interface via innovations in computer vision, robotics, and system integration techniques. Specifically, this work integrates a holistic framework of cameras, motion sensors, and a 7-degree-of-freedom robotic manipulator controlled by vision data processing and motion planning algorithms implemented in the open-source robotics middleware Robot Operating System (ROS)