Abstracting Multidimensional Concepts for Multilevel Decision Making in Multirobot Systems

Multirobot control architectures often require robotic tasks to be well defined before allocation. In complex missions, it is often difficult to decompose an objective into a set of well defined tasks; human operators generate a simplified representation based on experience and estimation. The result is a set of robot roles, which are not best suited to accomplishing those objectives. This thesis presents an alternative approach to generating multirobot control algorithms using task abstraction. By carefully analysing data recorded from similar systems a multidimensional and multilevel representation of the mission can be abstracted, which can be subsequently converted into a robotic controller. This work, which focuses on the control of a team of robots to play the complex game of football, is divided into three sections: In the first section we investigate the use of spatial structures in team games. Experimental results show that cooperative teams beat groups of individuals when competing for space and that controlling space is important in the game of robot football. In the second section, we generate a multilevel representation of robot football based on spatial structures measured in recorded matches. By differentiating between spatial configurations appearing in desirable and undesirable situations, we can abstract a strategy composed of the more desirable structures. In the third section, five partial strategies are generated, based on the abstracted structures, and a suitable controller is devised. A set of experiments shows the success of the method in reproducing those key structures in a multirobot system. Finally, we compile our methods into a formal architecture for task abstraction and control. The thesis concludes that generating multirobot control algorithms using task abstraction is appropriate for problems which are complex, weakly-defined, multilevel, dynamic, competitive, unpredictable, and which display emergent properties

A psychology based approach for longitudinal development in cognitive robotics.

A major challenge in robotics is the ability to learn, from novel experiences, new behavior that is useful for achieving new goals and skills. Autonomous systems must be able to learn solely through the environment, thus ruling out a priori task knowledge, tuning, extensive training, or other forms of pre-programming. Learning must also be cumulative and incremental, as complex skills are built on top of primitive skills. Additionally, it must be driven by intrinsic motivation because formative experience is gained through autonomous activity, even in the absence of extrinsic goals or tasks. This paper presents an approach to these issues through robotic implementations inspired by the learning behavior of human infants. We describe an approach to developmental learning and present results from a demonstration of longitudinal development on an iCub humanoid robot. The results cover the rapid emergence of staged behavior, the role of constraints in development, the effect of bootstrapping between stages, and the use of a schema memory of experiential fragments in learning new skills. The context is a longitudinal experiment in which the robot advanced from uncontrolled motor babbling to skilled hand/eye integrated reaching and basic manipulation of objects. This approach offers promise for further fast and effective sensory-motor learning techniques for robotic learning

The Presence of Weak Active Galactic Nuclei in High Redshift Star Forming Galaxies

We present [OIII 5007A] observations of the star forming galaxy HDF-BMZ1299 (z=1.598) using Keck Observatory's Adaptive Optics system with the near-infrared integral field spectrograph OSIRIS. Using previous Halpha and [NII] measurements of the same source, we are able for the first time to use spatially resolved observations to place a high-redshift galaxy's substructure on a traditional HII diagnostic diagram. We find that HDF-BMZ1299's spatially concentrated nebular ratios in the central ~1.5 kiloparsec (0."2) are best explained by the presence of an AGN: log([NII]/Halpha)=-0.22+/-0.05 and 2sigma limit of log([OIII]/Hbeta)>0.26. The dominant energy source of this galaxy is star formation, and integrating a single aperture across the galaxy yields nebular ratios that are composite spectra from both AGN and HII regions. The presence of an embedded AGN in HDF-BMZ1299 may suggest a potential contamination in a fraction of other high-redshift star forming galaxies, and we suggest that this may be a source of the "elevated" nebular ratios previously seen in seeing-limited metallicity studies. HDF-BMZ1299's estimated AGN luminosity is L_Halpha = 3.7e41 erg/s and L_[OIII] = 5.8e41 erg/s, making it one of the lowest luminosity AGN discovered at this early epoch.Comment: 15 pages, 4 figures, ApJ Accepted, new version to be published (updated text, figures, and table

A Modular Digital Twinning Framework for Safety Assurance of Collaborative Robotics

Digital twins offer a unique opportunity to design, test, deploy, monitor, and control real-world robotic processes. In this paper we present a novel, modular digital twinning framework developed for the investigation of safety within collaborative robotic manufacturing processes. The modular architecture supports scalable representations of user-defined cyber-physical environments, and tools for safety analysis and control. This versatile research tool facilitates the creation of mixed environments of Digital Models, Digital Shadows, and Digital Twins, whilst standardising communication and physical system representation across different hardware platforms. The framework is demonstrated as applied to an industrial case-study focused on the safety assurance of a collaborative robotic manufacturing process. We describe the creation of a digital twin scenario, consisting of individual digital twins of entities in the manufacturing case study, and the application of a synthesised safety controller from our wider work. We show how the framework is able to provide adequate evidence to virtually assess safety claims made against the safety controller using a supporting validation module and testing strategy. The implementation, evidence and safety investigation is presented and discussed, raising exciting possibilities for the use of digital twins in robotic safety assurance

Verified Synthesis of Optimal Safety Controllers for Human-Robot Collaboration

In human-robot collaboration, software-based safety controllers are used to improve operational safety, e.g., by triggering shutdown mechanisms or emergency stops to avoid accidents. Complex robotic tasks and increasingly close human-robot interaction pose new challenges to controller developers and certification authorities. Key among these challenges is the need to assure the correctness of safety controllers under explicit (and preferably weak) assumptions. To address this need, we introduce and evaluate a tool-supported approach for safety controller synthesis and deployment. The new approach focuses on human-robot collaboration in manufacturing, and is informed by the process, risk analysis, and relevant safety regulations for the target application. Controllers are selected from a design space of feasible controllers according to a set of optimality criteria, formally verified against correctness criteria, and translated into executable code and validated in a digital twin. The resulting controller can detect the occurrence of hazards, move the process into a safe state, and, in certain circumstances, return the process to an operational state from which it can resume its original task

Wood traits explain microbial but not termite‐driven decay in Australian tropical rainforest and savanna

1. Variation in decay rates across woody species is a key uncertainty in predicting the fate of carbon stored in deadwood, especially in the tropics. Quantifying the relative contributions of biotic decay agents, particularly microbes and termites, under different climates and across species with diverse wood traits could help explain this variation. 2. To fill this knowledge gap, we deployed woody stems from 16 plant species native to either rainforest (n = 10) or savanna (n = 6) in northeast Australia, with and without termite access. For comparison, we also deployed standardized, non-native pine blocks at both sites. We hypothesized that termites would increase rates of deadwood decay under conditions that limit microbial activity. Specifically, termite contributions to wood decay should be greater under dry conditions and in wood species with traits that constrain microbial decomposers. 3. Termite discovery of stems was surprisingly low with only 17.6% and 22.6% of accessible native stems discovered in the rainforest and savanna respectively. Contrary to our hypothesis, stems discovered by termites decomposed faster only in the rainforest. Termites discovered and decayed pine blocks at higher rates than native stems in both the rainforest and savanna. 4. We found significant variation in termite discovery and microbial decay rates across native wood species within the same site. Although wood traits explained 85% of the variation in microbial decay, they did not explain termite-driven decay. For stems undiscovered by termites, decay rates were greater in species with higher wood nutrient concentrations and syringyl:guiacyl lignin ratios but lower carbon concentrations and wood densities. 5. Synthesis. Ecosystem-scale predictions of deadwood turnover and carbon storage should account for the impact of wood traits on decomposer communities. In tropical Australia, termite-driven decay was lower than expected for native wood on the ground. Even if termites are present, they may not always increase decomposition rates of fallen native wood in tropical forests. Our study shows how the drivers of wood decay differ between Australian tropical rainforest and savanna; further research should test whether such differences apply world-wide