A robotic telescope for university-level distance teaching

We present aspects of the deployment of a remotely operable telescope for teaching practical science to distance learning undergraduate students. We briefly describe the technical realisation of the facility, PIRATE, in Mallorca and elaborate on how it is embedded in the Open University curriculum. The PIRATE teaching activities were studied as part of a wider research project into the importance of realism, sociability and metafunctionality for the effectiveness of virtual and remote laboratories in teaching practical science. We find that students accept virtual experiments (e.g. a telescope simulator) when they deliver genuine, "messy" data, clarify how they differ from a realistic portrayal, and are flagged as training tools. A robotic telescope is accepted in place of on-site practical work when realistic activities are included, the internet connection is stable, and when there is at least one live video feed. The robotic telescope activity should include group work and facilitate social modes of learning. Virtual experiments, though normally considered as asynchronous tools, should also include social interaction. To improve student engagement and learning outcomes a greater situational awareness for the robotic telescope setting should be devised. We conclude this report with a short account of the current status of PIRATE after its relocation from Mallorca to Tenerife and its integration into the OpenScience Observatories

Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms

The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications

Virtual assembly with biologically inspired intelligence

This paper investigates the introduction of biologically inspired intelligence into virtual assembly. It develops a approach to assist product engineers making assembly-related manufacturing decisions without actually realizing the physical products. This approach extracts the knowledge of mechanical assembly by allowing human operators to perform assembly operations directly in the virtual environment. The incorporation of a biologically inspired neural network into an interactive assembly planner further leads to the improvement of flexible product manufacturing, i.e., automatically producing alternative assembly sequences with robot-level instructions for evaluation and optimization. Complexity analysis and simulation study demonstrate the effectiveness and efficiency of this approach