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Agriculture provides an unique opportunity for the development of robotic systems; robots must be developed which can operate in harsh conditions and in highly uncertain and unknown environments. One particular challenge is performing manipulation for autonomous robotic harvesting. This paper describes recent and current work to automate the harvesting of iceberg lettuce. Unlike many other produce, iceberg is challenging to harvest as the crop is easily damaged by handling and is very hard to detect visually. A platform called Vegebot has been developed to enable the iterative development and field testing of the solution, which comprises of a vision system, custom end effector and software. To address the harvesting challenges posed by iceberg lettuce a bespoke vision and learning system has been developed which uses two integrated convolutional neural networks to achieve classification and localization. A custom end effector has been developed to allow damage free harvesting. To allow this end effector to achieve repeatable and consistent harvesting, a control method using force feedback allows detection of the ground. The system has been tested in the field, with experimental evidence gained which demonstrates the success of the vision system to localize and classify the lettuce, and the full integrated system to harvest lettuce. This study demonstrates how existing state‐of‐the art vision approaches can be applied to agricultural robotics, and mechanical systems can be developed which leverage the environmental constraints imposed in such environments.

In this paper the development of a Wireless Sensor Network (WSN) for construction noise identification and sound locating is investigated using the novel application of Bluetooth Low Energy (BLE). Three WSNs using different system-on-chip (SoC) devices and networking protocols have been prototyped using a Raspberry Pi as the gateway in the network. The functionality of the system has been demonstrated with data logging experiments and comparisons has been made between the different WSN systems developed to identify the relative advantages of BLE. Experiments using the WSN for vehicle noise identification and sound location have further demonstrated the potential of the system. This paper demonstrates the versatility of a BLE WSNs and the low power consumption that is achievable with BLE devices for noise detection applications.x

Discusses technological innovations in the robotics industry and explores where the industry is headed with emphasis on soft robotics. © 1994-2011 IEEE.

In nature, manipulators have evolved into different morphologies with varying rigidity to accomplish different tasks. Soft and continuum tentacles of the octopus, rigid and strong pincers of the crab and ligamentous jointed fingers of the human demonstrate the relationship between the complexity of a host’s task space and the design of its manipulator. Thus, the purpose of use a robotic manipulator should be considered as an important design parameter which governs the choice of appropriate materials and design rules. For tasks which require delicacy and strength at the same time, such as human-machine interaction, agriculture or robotic surgery hybrid soft-rigid manipulator designs should be investigated. Here, we present four design principles for building hybrid robot manipulators which incorporate soft and rigid materials and demonstrate each principle with working examples. © Springer International Publishing AG 2017.

Background Population screening and monitoring of cardiovascular risk is suboptimal in Australian primary care. The role of community pharmacy has increased considerably, but without any policy framework for development. The aim of this study was to explore the nature of community pharmacy-based screening models in Australia, capacity to increase delivery of pharmacy screening, and barriers and enablers to increasing capacity. Methods An online survey weblink was emailed to pharmacy managers at every quality-accredited pharmacy in Australia by the Quality Pharmacy Care Program. The 122-item survey explored the nature of screening services, pharmacy capacity to deliver services, and barriers and enablers to service delivery in considerable detail. Adaptive questioning was used extensively to reduce the participant burden. Pharmacy location details were requested to facilitate geo-coding and removal of duplicate entries. A descriptive analysis of responses was undertaken. Results There were 294 valid responses from 4890 emails, a 6% response rate. Most pharmacies (79%) had private counselling areas. Blood pressure assessment was nearly universal (96%), but other common risk factor assessments were offered by a minority. Most did not charge for assessments, and 59% indicated capacity to provide multiple risk factor assessments. Fewer than one in five (19%) reported any formal arrangements with general practice for care coordination. Financial viability was perceived as a key barrier to service expansion, amid concerns of patient willingness to pay. Support from government and non-governmental organisations for their role was seen as necessary. Conclusion There appears to be a critical mass of pharmacies engaging in evidence-based and professional services. Considerable additional support appears required to optimise performance across the profession.

Robotic technologies are being increasingly applied to agriculture, in particular to harvesting. Some types of produce such as iceberg lettuce require additional processing after harvesting in order to satisfy the needs of the end-user or customer. Lettuce must have its outer leaves removed, a task that is currently performed manually. The leaf removal task represents a challenging vision and manipulation problem: the lettuce is in a random pose on a flat surface, from which the outermost leaves must be removed quickly and without causing damage. This letter presents a novel vision pipeline and suction removal system that enables robotic lettuce leaf removal. A suction nozzle and control procedure are used for the removal itself, relying on the orientation estimation and stem detection provided by the vision pipeline. To the best of the author's knowledge, this is the first robotic lettuce leaf peeling system. © 2016 IEEE.

Many complex physical systems exhibit a rich variety of discrete behavioural modes. Often, the system complexity limits the applicability of standard modelling tools. Hence, understanding the underlying physics of different behaviours and distinguishing between them is challenging. Although traditional machine learning techniques could predict and classify behaviour well, typically they do not provide any meaningful insight into the underlying physics of the system. In this paper we present a novel method for extracting physically meaningful clusters of discrete behaviour from limited experimental observations. This method obtains a set of physically plausible functions that both facilitate behavioural clustering and aid in system understanding. We demonstrate the approach on the V-shaped falling paper system, a new falling paper type system that exhibits four distinct behavioural modes depending on a few morphological parameters. Using just 49 experimental observations, the method discovered a set of candidate functions that distinguish behaviours with an error of 2.04%, while also aiding insight into the physical phenomena driving each behaviour. © 2019 Howison et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Free-falling paper shapes exhibit rich, complex and varied behaviours that are extremely challenging to model analytically. Physical experimentation aids in system understanding, but is time-consuming, sensitive to initial conditions and reliant on subjective visual behavioural classification. In this study, robotics, computer vision and machine learning are used to autonomously fabricate, drop, analyse and classify the behaviours of hundreds of shapes. The system is validated by reproducing results for falling discs, which exhibit four falling styles: tumbling, chaotic, steady and periodic. A previously determined mapping from a non-dimensional parameter space to behaviour groups is shown to be consistent with these new experiments for tumbling and chaotic behaviours. However, steady or periodic behaviours are observed in previously unseen areas of the parameter space. More complex hexagon, square and cross shapes are investigated, showing that the non-dimensional parameter space generalizes to these shapes. The system highlights the potential of robotics for the investigation of complex physical systems, of which falling paper is one example, and provides a template for future investigation of such systems. The dynamics of paper shapes in free fall are still not fully understood, despite being discussed for more than 150 years. Collecting large amounts of data has the potential to give us new insights and a robotics system could generate and analyse data in large quantities.

In this review we introduce the framework of reality-assisted evolution to summarize a growing trend towards combining model-based and model-free approaches to improve the design of physically embodied soft robots. In silico, data-driven models build, adapt and improve representations of the target system using real-world experimental data. By simulating huge numbers of virtual robots using these data-driven models, optimization algorithms can illuminate multiple design candidates for transference to the real world. In reality, large-scale physical experimentation facilitates the fabrication, testing and analysis of multiple candidate designs. Automated assembly and reconfigurable modular systems enable significantly higher numbers of real-world design evaluations than previously possible. Large volumes of ground-truth data gathered via physical experimentation can be returned to the virtual environment to improve data-driven models and guide optimization. Grounding the design process in physical experimentation ensures the complexity of virtual robot designs does not outpace the model limitations or available fabrication technologies. We outline key developments in the design of physically embodied soft robots under the framework of reality-assisted evolution., Comment: Final manuscript, accepted for publication in Artificial Life

Behavioral diversity seen in biological systems is, at the most basic level, driven by interactions between physical materials and their environment. In this context we are interested in falling paper systems, specifically the V-shaped falling paper (VSFP) system that exhibits a set of discrete falling behaviors across the morphological parameter space. Our previous work has investigated how morphology influences dominant falling behaviors in the VSFP system. In this article we build on this analysis to investigate the nature of behavioral transitions in the same system. First, we investigate stochastic behavior transitions. We demonstrate how morphology influences the likelihood of different transitions, with certain morphologies leading to a wide range of possible paths through the behavior-space. Second, we investigate deterministic transitions. To investigate behaviors over longer time periods than available in falling experiments we introduce a new experimental platform. We demonstrate how we can induce behavior transitions by modulating the energy input to the system. Certain behavior transitions are found to be irreversible, exhibiting a form of hysteresis, while others are fully reversible. Certain morphologies are shown to behave like simplistic sequential logic circuits, indicating that the system has a form of memory encoded into the morphology–environment interactions. Investigating the limits of how morphology–environment interactions induce non-trivial behaviors is a key step for the design of embodied artificial life-forms.

Medical palpation is a diagnostic technique in which physicians use the sense of touch to manipulate the soft human tissue. This can be done to enable the diagnosis of possibly life-threatening conditions, such as cancer. Palpation is still poorly understood because of the complex interaction dynamics between the practitioners' hands and the soft human body. To understand this complex of soft body interactions, we explore robotic palpation for the purpose of diagnosing the presence of abnormal inclusions, or tumors. Using a Bayesian framework for training and classification, we show that the exploration of soft bodies requires complex, multi-axis, palpation trajectories. We also find that this probabilistic approach is capable of rapidly searching the large action space of the robot. This work progresses "robotic"palpation, and it provides frameworks for understanding and exploiting soft body interactions. © Luca Scimeca et al. 2022; Published by Mary Ann Liebert, Inc. 2022.