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Domain-specific ontologies support system design and can establish a framework for fulfilling user-level, safety, or ethical requirements. The IEEE 7007–2021 Ontological Standard for ethically driven robotics and automation systems is the first industry standard to introduce a structure of ontologies concerning robot ethics and related fields, such as data privacy, transparency, responsibility, and accountability, offering a systems science approach to support the ethically aligned design of complex cyber–physical systems (CPSs) and robots particularly. This article provides a comprehensive overview of the main ontological commitments composing the foundation of the standard, the rationale behind their development, together with use cases of applications. Future directions for ethically aligned robotics and artificial intelligence (AI)-based systems along IEEE 7007–2021 are outlined, taking into account the exponentially growing fields of service and medical robotics.

Recent research and developments in cloud robotics (CR) require appropriate knowledge representation to ensure interoperable data, information, and knowledge sharing within cloud infrastructures. As an important branch of the Internet of Things (IoT), these demands to advance it forward motivates academic and industrial sectors to invest on it. The IEEE ’Ontologies for Robotics and Automation’ Working Group (ORA WG) has been developing standard ontologies for different robotic domains, including industrial and autonomous robots. The use of such robotic standards has the potential to benefit the Cloud Robotic Community (CRC) as well, supporting the provision of ubiquitous intelligent services by the CR-based systems. This paper explores this potential by developing an ontological approach for effective information sharing in cloud robotics scenarios. It presents an extension to the existing ontological standards to cater for the CR domain. The use of the new ontological elements is illustrated through its use in a couple of CR case studies. To the best of our knowledge, this is the first work ever that implements an ontology comprising concepts and axioms applicable to the CR domain.

Abstract: In this paper, we present an extension of the boundary value problem path planner (BVP PP) to control multiple robots in a robot soccer scenario. This extension is called Locally Oriented Potential Field (LOPF) and computes a potential field from the numerical solution of a BVP using local relaxations in different patches of the solution space. This permits that a single solution of the BVP endows distinct robots with different behaviors in a team. We present the steps to implement LOPF as well as several results obtained in simulation. [Copyright &y& Elsevier]

The paper presents a general framework for concurrent navigation and exploration of unknown environments based on discrete potential fields that guide the robot motion. These potentials are obtained from a class of partial differential equation (PDE) problems called boundary value problems (BVP). The boundaries are generated from sensor readings and therefore they change as the robot moves. This framework corresponds to an extension of our previous work (Prestes, E., Idiart, M. A. P., Engel, P. and Trevisan, M.: Exploration technique using potential fields calculated from relaxation methods, in: IEEE/RSJ International Conference on Intelligent Robots and Systems, 2001, p. 2012; Prestes, E., Engel, P. M., Trevisan, M. and Idiart, M. A.: Exploration method using harmonic functions, Robot. Auton. Syst.40(1) (2002), 25–42). Here, we propose that a careful choice of the PDE and the boundary conditions can produce efficient exploratory behaviors in sparse and dense environments. Furthermore, we show how to extend the exploratory behavior to produce new ones by changing dynamically the boundary function (the value of the potential at the boundaries) as the exploration takes course. Our framework is validated through a series of experiments with a real robot in office environments.The paper presents a general framework for concurrent navigation and exploration of unknown environments based on discrete potential fields that guide the robot motion. These potentials are obtained from a class of partial differential equation (PDE) problems called boundary value problems (BVP). The boundaries are generated from sensor readings and therefore they change as the robot moves. This framework corresponds to an extension of our previous work (Prestes, E., Idiart, M. A. P., Engel, P. and Trevisan, M.: Exploration technique using potential fields calculated from relaxation methods, in: IEEE/RSJ International Conference on Intelligent Robots and Systems, 2001, p. 2012; Prestes, E., Engel, P. M., Trevisan, M. and Idiart, M. A.: Exploration method using harmonic functions, Robot. Auton. Syst.40(1) (2002), 25–42). Here, we propose that a careful choice of the PDE and the boundary conditions can produce efficient exploratory behaviors in sparse and dense environments. Furthermore, we show how to extend the exploratory behavior to produce new ones by changing dynamically the boundary function (the value of the potential at the boundaries) as the exploration takes course. Our framework is validated through a series of experiments with a real robot in office environments.

In this work, an intelligent control architecture for controlling multi-robots, denominated ACIn, that integrates planning and RBS, has been proposed. With this objective, some techniques have been investigated for the planning of trajectories, such as Potential Fields and Potential Fields based on the solution of the Boundary Value Problem (BVP). Such techniques, generally used for a single robot, have been adapted to work on multi-robots into ACIn architecture. Then, a modification of the Potential Fields based on BVP, called Locally Oriented Potential Field (LOPF), is used together ACIn to overcome the drawback existing in BVP based techniques, when they are applied for controlling multi robots. To get collaborative interactions among the robots in a team, a rule based system has been also proposed to as part of the ACIn architecture. The objective of RBS is to characterize the functionality of each robot for realizing an specific task. Experiments performed in the robot soccer simulated environment show that ACIn works on satisfactory.