Purpose IEEE Ontologies for Robotics and Automation Working Group were divided into subgroups that were in charge of studying industrial robotics, service robotics and autonomous robotics. This paper aims to present the work in-progress developed by the autonomous robotics (AuR) subgroup. This group aims to extend the core ontology for robotics and automation to represent more specific concepts and axioms that are commonly used in autonomous robots. Design/methodology/approach For autonomous robots, various concepts for aerial robots, underwater robots and ground robots are described. Components of an autonomous system are defined, such as robotic platforms, actuators, sensors, control, state estimation, path planning, perception and decision-making. Findings AuR has identified the core concepts and domains needed to create an ontology for autonomous robots. Practical implications AuR targets to create a standard ontology to represent the knowledge and reasoning needed to create autonomous systems that comprise robots that can operate in the air, ground and underwater environments. The concepts in the developed ontology will endow a robot with autonomy, that is, endow robots with the ability to perform desired tasks in unstructured environments without continuous explicit human guidance. Originality/value Creating a standard for knowledge representation and reasoning in autonomous robotics will have a significant impact on all R&A domains, such as on the knowledge transmission among agents, including autonomous robots and humans. This tends to facilitate the communication among them and also provide reasoning capabilities involving the knowledge of all elements using the ontology. This will result in improved autonomy of autonomous systems. The autonomy will have considerable impact on how robots interact with humans. As a result, the use of robots will further benefit our society. Many tedious tasks that currently can only be performed by humans will be performed by robots, which will further improve the quality of life. To the best of the authors’knowledge, AuR is the first group that adopts a systematic approach to develop ontologies consisting of specific concepts and axioms that are commonly used in autonomous robots.

Optimal search strategies where targets are observed at several different angles are found. Targets are assumed to exhibit rectangular symmetry and have a uniformlydistributed orientation. By rectangular symmetry, it is meant that one side of a target is the mirror image of its opposite side. Finding an optimal solution is generally a hard problem. Fortunately, symmetry principles allow analytical and intuitive solutions to be found. One such optimal search strategy consists of choosing n angles evenly separated on the half-circle and leads to a lower bound of the probability of not detecting targets. As no prior knowledge of the target orientation is required, such search strategies are also robust, a desirable feature in search and detection missions.

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Robots and Systems (IROS), pp. 1359-1364, 2012 Towards an ontology for autonomous robots Paull, Liam; Severac, Gaetan; Raffo, Guilherme V.; Angel, Julian Mauricio; Boley, Harold; Durst, Phillip J.; Gray, Wendell; Habib, Maki; Nguyen, Bao; Ragavan, S. Veera; Saeedi G., Sajad; Sanz, Ricardo; Seto, Mae; Stefanovski, Aleksandar; Trentini, Michael; Li, Howard Abstract-The IEEE RAS Ontologies for Robotics and Automation Working Group is dedicated to developing a methodology for knowledge representation and reasoning in robotics and automation. As part of this working group, the Autonomous Robots sub-group is tasked with developing ontology modules for autonomous robots. This paper describes the work in progress on the development of ontologies for autonomous systems. For autonomous systems, the focus is on the cooperation, coordination, and communication of multiple unmanned aerial vehicles (UAVs), unmanned ground vehicles (UGVs), and autonomous underwater vehicles (AUVs). At the global mission level, the system ontologies must be able to model entities and relationship of multiple autonomous systems. At the individual system level, the ontologies must model the decision-making ability, control strategies, sensing abilities, map building, environment perception, motion planning, communication, autonomous behaviors and so on. The ontologies serve as a framework for working out concepts of employment with multiple vehicles for a variety of operational scenarios with emphasis on collaborative and cooperative missions.

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