Suitable task allocation in intelligent systems for assistive environments

The growing need of technological assistance to provide support to people with special needs demands for systems more and more efficient and with better performances. With this aim, this work tries to advance in a multirobot platform that allows the coordinated control of different agents and other elements in the environment to achieve an autonomous behavior based on the user’s needs or will. Therefore, this environment is structured according to the potentiality of each agent and elements of this environment and of the dynamic context, to generate the adequate actuation plans and the coordination of their execution.Peer ReviewedPostprint (author's final draft

Ontological framework to improve motion planning of manipulative agents through semantic knowledge-based reasoning

This paper describes the actions taken in developing a framework that aims to improve the motion planning of a manipulative robotic agent through reasoning based on semantic knowledge. The Semantic Web Rule Language (SWRL) was employed to draw new insights from the existing information about the robotic system and its environment. Recent ontology-based standards have been developed (IEEE 1872-2015; IEEE 1872.2-2021; IEEE 7007-2021), and others are currently under development (IEEE P1872.1; IEEE P1872.3) to improve robot performance in task execution. Ontological knowledge “semantic map" was generated using a deep neural network trained to detect and classify objects in the environment where the manipulator agent acts. Manipulation constraints were deduced, and the environment corresponding to the agent’s manipulation workspace was created so the planner could interpret it to generate a collision-free path. Several SPARQL queries were used to explore the semantic map and allow ontological reasoning. The proposed framework was implemented and validated in a real experimental setting, using the ROSPlan planning framework to perform the planning tasks. This ontology-based framework proved to be a promising strategy. E.g., it allows the robotic manipulative agent to interact with objects, e.g., to choose a mobile phone or a water bottle, using semantic information from the environment to solve the requested tasks.This work is financed by national funds through FCT - Foundation for Science and Technology, I.P., through IDMEC, under LAETA, project UIDB/50022/2020. The work of Rodrigo Bernardo was supported by the PhD Scholarship BD/6841/2020 from FCT. This work has received funding from: the project 0770_EUROAGE2_4_E (POCTEP Programa Interreg V-A Spain-Portugal), and the European Union’s Horizon 2020 programme under StandICT.eu 2023 (under Grant Agreement No.: 951972).info:eu-repo/semantics/publishedVersio

A review and comparison of ontology-based approaches to robot autonomy

Within the next decades, robots will need to be able to execute a large variety of tasks autonomously in a large variety of environments. To relax the resulting programming effort, a knowledge-enabled approach to robot programming can be adopted to organize information in re-usable knowledge pieces. However, for the ease of reuse, there needs to be an agreement on the meaning of terms. A common approach is to represent these terms using ontology languages that conceptualize the respective domain. In this work, we will review projects that use ontologies to support robot autonomy. We will systematically search for projects that fulfill a set of inclusion criteria and compare them with each other with respect to the scope of their ontology, what types of cognitive capabilities are supported by the use of ontologies, and which is their application domain.Peer ReviewedPostprint (author's final draft

A novel framework to improve motion planning of robotic systems through semantic knowledge-based reasoning

The need to improve motion planning techniques for manipulator robots, and new effective strategies to manipulate different objects to perform more complex tasks, is crucial for various real-world applications where robots cooperate with humans. This paper proposes a novel framework that aims to improve the motion planning of a robotic agent (a manipulator robot) through semantic knowledge-based reasoning. The Semantic Web Rule Language (SWRL) was used to infer new knowledge based on the known environment and the robotic system. Ontological knowledge, e.g., semantic maps, were generated through a deep neural network, trained to detect and classify objects in the environment where the robotic agent performs. Manipulation constraints were deduced, and the environment corresponding to the agent’s manipulation workspace was created so the planner could interpret it to generate a collision-free path. For reasoning with the ontology, different SPARQL queries were used. The proposed framework was implemented and validated in a real experimental setup, using the planning framework ROSPlan to perform the planning tasks. The proposed framework proved to be a promising strategy to improve motion planning of robotics systems, showing the benefits of artificial intelligence, for knowledge representation and reasoning in robotics.info:eu-repo/semantics/publishedVersio

ROS-based Solution for Robotic Services in Cloud Computing

Robot Operating System (ROS) is becoming a widely-used environment for devel- oping robot software systems. It provides unique features such as message-passing between processes and code reuse between robots. The new trend in ROS-based robotic systems is facing the development and delivery of effective services by com- bining the advantages of both cloud robotics and web services. Cloud robotics is the way that allows robots to overcome their limitations of pro- cessing and knowledge by boosting computational and cognitive capabilities. On the other hand, as an implementation of Service-Oriented Architecture (SOA), web Services allow mainly different ROS codes to be discovered over the internet for their reuse. However, the characterization, description, and discovery of the ROS service capability for the offered robotic functionality are still issues that are not fully ad- dressed. In this context, we focus in this thesis on developing an architecture for roboti software provisioning to both software developers and robots by exploiting the op- portunities of ROS, web services, and cloud robotics. We propose a complete SOA approach for cloud robotics, in which ROS-based robotic tasks are defined as web services. The approach focuses on defining the service cycle process of describing, discovering, and selecting services. Two characterizations for ROS web services are proposed. The service characterizations describe the semantic representation of the robot task from ROS itself. In each case, we present a strategy that allows users todiscover the relevant robotic service that can match their queries and robots

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

Multi-agent robotic systems and exploration algorithms: Applications for data collection in construction sites

The construction industry has been notoriously slow to adopt new technology and embrace automation. This has resulted in lower efficiency and productivity compared to other industries where automation has been widely adopted. However, recent advancements in robotics and artificial intelligence offer a potential solution to this problem. In this study, a methodology is proposed to integrate multi-robotic systems in construction projects with the aim of increasing efficiency and productivity. The proposed approach involves the use of multiple robot and human agents working collaboratively to complete a construction task. The methodology was tested through a case study that involved 3D digitization of a small, occluded space using two robots and one human agent. The results show that integrating multi-agent robotic systems in construction can effectively overcome challenges and complete tasks efficiently. The implications of this study suggest that multi-agent robotic systems could revolutionize the industry

Robot Workspace Monitoring using a Blockchain-based 3D Vision Approach

Blockchain has been used extensively for financial purposes, but this technology can also be beneficial in other contexts where multi-party cooperation, security and decentralization of the data is essential. Properties such as immutability, accessibility and non-repudiation and the existence of smart-contracts make blockchain technology very interesting in robotic contexts that require event registration or integration with Artificial Intelligence. In this paper, we propose a system that leverages blockchain as a ledger to register events and information to be processed by Oracles and uses smart-contracts to control robots by adjusting their velocity, or stopping them, if a person enters the robot working space without permission. We show how blockchain can be used in computer vision problems by interacting with multiple external parties, Oracles, that perform image analysis and how it is possible to use multiple smart-contracts for different tasks. The method proposed is shown in a scenario representing a factory environment, but since it is modular, it can be easily adapted and extended for other contexts, allowing for simple integration and maintenance.info:eu-repo/semantics/publishedVersio