Cognitive Task Planning for Smart Industrial Robots

This research work presents a novel Cognitive Task Planning framework for Smart Industrial Robots. The framework makes an industrial mobile manipulator robot Cognitive by applying Semantic Web Technologies. It also introduces a novel Navigation Among Movable Obstacles algorithm for robots navigating and manipulating inside a firm. The objective of Industrie 4.0 is the creation of Smart Factories: modular firms provided with cyber-physical systems able to strong customize products under the condition of highly flexible mass-production. Such systems should real-time communicate and cooperate with each other and with humans via the Internet of Things. They should intelligently adapt to the changing surroundings and autonomously navigate inside a firm while moving obstacles that occlude free paths, even if seen for the first time. At the end, in order to accomplish all these tasks while being efficient, they should learn from their actions and from that of other agents. Most of existing industrial mobile robots navigate along pre-generated trajectories. They follow ectrified wires embedded in the ground or lines painted on th efloor. When there is no expectation of environment changes and cycle times are critical, this planning is functional. When workspaces and tasks change frequently, it is better to plan dynamically: robots should autonomously navigate without relying on modifications of their environments. Consider the human behavior: humans reason about the environment and consider the possibility of moving obstacles if a certain goal cannot be reached or if moving objects may significantly shorten the path to it. This problem is named Navigation Among Movable Obstacles and is mostly known in rescue robotics. This work transposes the problem on an industrial scenario and tries to deal with its two challenges: the high dimensionality of the state space and the treatment of uncertainty. The proposed NAMO algorithm aims to focus exploration on less explored areas. For this reason it extends the Kinodynamic Motion Planning by Interior-Exterior Cell Exploration algorithm. The extension does not impose obstacles avoidance: it assigns an importance to each cell by combining the efforts necessary to reach it and that needed to free it from obstacles. The obtained algorithm is scalable because of its independence from the size of the map and from the number, shape, and pose of obstacles. It does not impose restrictions on actions to be performed: the robot can both push and grasp every object. Currently, the algorithm assumes full world knowledge but the environment is reconfigurable and the algorithm can be easily extended in order to solve NAMO problems in unknown environments. The algorithm handles sensor feedbacks and corrects uncertainties. Usually Robotics separates Motion Planning and Manipulation problems. NAMO forces their combined processing by introducing the need of manipulating multiple objects, often unknown, while navigating. Adopting standard precomputed grasps is not sufficient to deal with the big amount of existing different objects. A Semantic Knowledge Framework is proposed in support of the proposed algorithm by giving robots the ability to learn to manipulate objects and disseminate the information gained during the fulfillment of tasks. The Framework is composed by an Ontology and an Engine. The Ontology extends the IEEE Standard Ontologies for Robotics and Automation and contains descriptions of learned manipulation tasks and detected objects. It is accessible from any robot connected to the Cloud. It can be considered a data store for the efficient and reliable execution of repetitive tasks; and a Web-based repository for the exchange of information between robots and for the speed up of the learning phase. No other manipulation ontology exists respecting the IEEE Standard and, regardless the standard, the proposed ontology differs from the existing ones because of the type of features saved and the efficient way in which they can be accessed: through a super fast Cascade Hashing algorithm. The Engine lets compute and store the manipulation actions when not present in the Ontology. It is based on Reinforcement Learning techniques that avoid massive trainings on large-scale databases and favors human-robot interactions. The overall system is flexible and easily adaptable to different robots operating in different industrial environments. It is characterized by a modular structure where each software block is completely reusable. Every block is based on the open-source Robot Operating System. Not all industrial robot controllers are designed to be ROS-compliant. This thesis presents the method adopted during this research in order to Open Industrial Robot Controllers and create a ROS-Industrial interface for them

A Real Time Distributed Approach to Collision Avoidance for Industrial Manipulators

Robot interaction with the surrounding environment is an important and newsworthy problem in the context of industrial and service robotics. Collision avoidance gives the robot the ability to avoid contacts with objects around it, but most of the industrial controls implementing collision avoidance checks only the robot Tool Center Point (TCP) over the objects in the cell, without taking into account the shape of the tool, mounted on the robot flange. In this paper a novel approach is proposed, based on an accurate 3D simulation of the robotic cell. A distributed real time computing approach has been chosen to avoid any overloading of the robot controller. The simulator and the client application are implemented in a personal computer, connected via a TCP-IP socket to the robot controller, which hosts and manages the anti-collision policies, based on a proper speed override control. The real time effectiveness of the proposed approach has been confirmed by experimental tests, carried out for a real industrial setup in two different scenarios

An Augmented Interface to Display Industrial Robot Faults

Technology advancement is changing the way industrial factories have to face an increasingly complex and competitive market. The fourth industrial revolution (known as industry 4.0) is also changing how human workers have to carry out tasks and actions. In fact, it is no longer impossible to think of a scenario in which human operators and industrial robots work side-by-side, sharing the same environment and tools. To realize a safe work environment, workers should trust robots as well as they trust human operators. Such goal is indeed complex to achieve, especially when workers are under stress conditions, such as when a fault occurs and the human operators are no longer able to understand what is happening in the industrial manipulator. Indeed, Augmented Reality (AR) can help workers to visualize in real-time robots’ faults. This paper proposes an augmented system that assists human workers to recognize and visualize errors, improving their awareness of the system. The system has been tested using both an AR see-through device and a smartphone

Subject-independent modeling of sEMG signals for the motion of a single robot joint through GMM Modelization

This thesis evaluates the use of a probabilistic model, the Gaussian Mixture Model (GMM), trained through Electromyography (EMG) signals to estimate the bending angle of a single human joint. The GMM is created from the EMG signals collected by different people and the goal is to create a general model based on the data of different subjects. The model is then tested on new, unseen data. The goodness of the estimated data is evaluated by means of Normalized Mean Square Errorope

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

A new HW/SW architecture to move from AGVs towards Autonomous Mobile Robots

This paper proposes the basic concepts of a brand new HW/SW architecture, whose development is in progress through an academic/industrial collaboration, aimed at obtaining a mobile agent capable to merge in itself the standard characteristics of the Automated Guided Vehicles and some potentialities of the Autonomous Mobile Robots, with a particular care for safety issues. Its HW/SW features, together with its mechanical characteristics, make it potentially applicable both in industrial and research contexts

Event-driven industrial robot control architecture for the Adept V+ platform

Modern industrial robotic systems are highly interconnected. They operate in a distributed environment and communicate with sensors, computer vision systems, mechatronic devices, and computational components. On the fundamental level, communication and coordination between all parties in such distributed system are characterized by discrete event behavior. The latter is largely attributed to the specifics of communication over the network, which, in terms, facilitates asynchronous programming and explicit event handling. In addition, on the conceptual level, events are an important building block for realizing reactivity and coordination. Eventdriven architecture has manifested its effectiveness for building loosely-coupled systems based on publish-subscribe middleware, either general-purpose or robotic-oriented. Despite all the advances in middleware, industrial robots remain difficult to program in context of distributed systems, to a large extent due to the limitation of the native robot platforms. This paper proposes an architecture for flexible event-based control of industrial robots based on the Adept V+ platform. The architecture is based on the robot controller providing a TCP/IP server and a collection of robot skills, and a high-level control module deployed to a dedicated computing device. The control module possesses bidirectional communication with the robot controller and publish/subscribe messaging with external systems. It is programmed in asynchronous style using pyadept, a Python library based on Python coroutines, AsyncIO event loop and ZeroMQ middleware. The proposed solution facilitates integration of Adept robots into distributed environments and building more flexible robotic solutions with eventbased logic

Robot Learning by observing human actions

Nowadays, robotics is entering in our life. One can see robot in industries, offices and even in homes. The more robots are in contact with people, the more requests of new capabilities and new features increase, in order to make robots able to act in case of need, help humans or be a companion. Therefore, it becomes essential to have a quick and easy way to teach new skills to robots. That is the aim of Robot Learning from Demonstration. This paradigm allows to directly program new tasks in a robot through demonstrations. This thesis proposes a novel approach to Robot Learning from Demonstration able to learn new skills from natural demonstrations carried out from naive users. To this aim, we introduce a novel Robot Learning from Demonstration framework by proposing novel approaches in all functional sub-units: from data acquisition to motion elaboration, from information modeling to robot control. A novel method is explained to extract 3D motion flow information from both RGB and depth data acquired by using recently introduced consumer RGB-D cameras. The motion data are computed over the time to recognize and classify human actions. In this thesis, we describe new techniques to remap human motion to robotic joints. Our methods allow people to natural interact with robots by re-targeting the whole body movements in an intuitive way. We develop algorithm for both humanoids and manipulators motion and test them in different situations. Finally, we improve modeling techniques by using a probabilistic method: the Donut Mixture Model. This model is able to manage several interpretations that different people can produce performing a task. The estimated model can also be updated directly by using new attempts carried out by the robot. This feature is very important to rapidly obtain correct robot trajectories by means of few human demonstrations. A further contribution of this thesis is the creation of a number of new virtual models for the different robots we used to test our algorithms. All the developed models are compliant with ROS, so they can be used to foster research in the field from all the community of this very diffuse robotics framework. Moreover, a new 3D dataset is collected to compare different action recognition algorithms. The dataset contains both RGB-D information coming directly from the sensor and skeleton data provided by a skeleton tracker

Commercial robotics suites in industry 4.0 framework

As the use of technology has increased to a lot of extent, and dependability on technology is more than ever, the importance of considering the new ways of technology is evident. This thesis provides credible research on commercial robotic suites in industry 4.0. In industry 4.0 revolution, the production process will be revolutionized and production of the products will be performed more efficiently and on time. Through using advanced technology, the production time will significantly reduce allowing organizations to fulfill the demand of the customers more efficiently. Purpose of this thesis is to provide deep insight into the industry 4.0 and how industrial 4.0 frameworks will change the way of production. Advance robotic technologies not only help organizations to reduce their work cost in saving time for production but also help them to produce products, which are high in quality and error free. Using robotics, there are less chances of errors in tasks, and it’s safer to perform certain tasks which are dangerous for humans. So, with the help of advanced robots it is possible to achieve the goal while keeping the human labor safe at the workplace. This thesis has covered many aspects and provided detail information in this regard

Task Oriented Programming and Service Algorithms for Smart Robotic Cells

L'abstract è presente nell'allegato / the abstract is in the attachmen