SUMMARYThis paper presents the results to date of the RepRap project – an ongoing project that has made and distributed freely a replicating rapid prototyper. We give the background reasoning that led to the invention of the machine, the selection of the processes that we and others have used to implement it, the designs of key parts of the machine and how these have evolved from their initial concepts and experiments, and estimates of the machine's reproductive success out in the world up to the time of writing (about 4500 machines in two and a half years).

Carbon nanofibres (CNFs) and graphite flake microparticles were added to thermoplastic polystyrene polymer with the aim of making new conductive blends suitable for 3D‐printing. Various polymer/carbon blends were evaluated for suitability as printable, electroactive material. An electrically conducting polystyrene composite was developed and used with commercially available polystyrene (HIPS) to manufacture electrodes suitable for electrochemical experiments. Electrodes were produced and evaluated for cyclic voltammetry of aqueous 1,1’‐ferrocenedimethanol and differential pulse voltammetry detection of aqueous Pb2+ via anodic stripping. A polystyrene/CNF/graphite (80/10/10 wt%) composite provides good conductivity and a stable electrochemical interface with well‐defined active geometric surface area. The printed electrodes form a stable interface to the polystyrene shell, give good signal to background voltammetric responses, and are reusable after polishing.

Discrepancies of materials, tools, and factory environments, as well as human intervention, make variation an integral part of the manufacturing process of any component. In particular, the assembly of large volume, aerospace parts is an area where significant levels of form and dimensional variation are encountered. Corrective actions can usually be taken to reduce the defects, when the sources and levels of variation are known. For the unknown dimensional and form variations, a tolerancing strategy is typically put in place in order to minimize the effects of production inconsistencies related to geometric dimensions. This generates a challenging problem for the automation of the corresponding manufacturing and assembly processes. Metrology is becoming a major contributor to being able to predict, in real time, the automated assembly problems related to the dimensional variation of parts and assemblies. This is done by continuously measuring dimensions and coordinate points, focusing on the product's key characteristics. In this paper, a number of metrology focused activities for large-volume aerospace products, including their implementation and application in the automation of manufacturing and assembly processes, are reviewed. This is done by using a case study approach within the assembly of large-volume aircraft wing structures.

The application of a novel fully 3-D printed carbon nanofiber-graphite-polystyrene electrode has been investigated for the trace determination of Zn 2+ by differential pulse anodic stripping voltammetry. The possibility of utilising a carbon pseudo-reference electrode was found to be successful. The effect of accumulation potential and time were investigated and optimised. Using an accumulation potential of-2.9 V (vs. C) and an accumulation time of 75 s a single sharp anodic stripping peak was recorded exhibiting a linear response from 12.7 µg/L to 450 µg/L. The theoretical detection limit (3σ) was calculated as 8.6 µg/L. Using the optimised conditions a mean recovery of 97.8 %, (%CV = 2.0 %, n = 5) for a tap water sample fortified at 0.990 µg/mL was obtained indicating the method holds promise for the determination of Zn 2+ in such samples.

This paper introduces a novel probabilistic method for robot based object segmentation. The method integrates knowledge of the robot's motion to determine the shape and location of objects. This allows a robot with no prior knowledge of its workspace to isolate objects against their surroundings by moving them and observing their visual feedback. The main contribution of the paper is to improve upon current methods by allowing object segmentation in changing environments and moving backgrounds. The approach allows optimal values for the algorithm parameters to be estimated. Empirical studies against alternatives demonstrate clear improvements in both planar and three dimensional motion.

Human Locomotion Mode Recognition (LMR) has the potential to be used as a control mechanism for lower-limb active prostheses. Active prostheses can assist and restore a more natural gait for amputees, but as a medical device it must minimize user risks, such as falls and trips. As such, any control system must have high accuracy and robustness, with a detailed understanding of its internal operation. Long Short-Term Memory (LSTM) machine-learning networks can perform LMR with high accuracy levels. However, the internal behavior during classification is unknown, and they struggle to generalize when presented with novel users. The target problem addressed in this paper is understanding the LSTM classification behavior for LMR. A dataset of six locomotive activities (walking, stopped, stairs and ramps) from 22 non-amputee subjects is collected, capturing both steady-state and transitions between activities in natural environments. Non-amputees are used as a substitute for amputees to provide a larger dataset. The dataset is used to analyze the internal behavior of a reduced complexity LSTM network. This analysis identifies that the model primarily classifies activity type based on data around early stance. Evaluation of generalization for unseen subjects reveals low sensitivity to hyper-parameters and over-fitting to individuals’ gait traits. Investigating the differences between individual subjects showed that gait variations between users primarily occur in early stance, potentially explaining the poor generalization. Adjustment of hyper-parameters alone could not solve this, demonstrating the need for individual personalization of models. The main achievements of the paper are (i) the better understanding of LSTM for LMR, (ii) demonstration of its low sensitivity to learning hyper-parameters when evaluating novel user generalization, and (iii) demonstration of the need for personalization of ML models to achieve acceptable accuracy.

A mathematical formalism is sketched for representing relational structure between agents. n-ary relations, n > 2, require hypernetworks, which generalize binary relation networks. n-ary relations on sets create structure at higher levels of representation to the elements in multilevel systems. The state of a system is represented by its multilevel relational structure. The dynamics of a system are represented by state changes through time. These can be continuous with no change in the hypernetwork topology, but often they are not. Controlling such systems involves taking actions intended to result in desirable state changes. The concept of multilevel hypernetwork can be applied to multiagent systems in general. ACM Reference Format:Johnson, J. and Iravani, P. 2007. The multilevel hypernetwork dynamics of complex systems of robot soccer agents.

We present a Bayesian approach to tactile object recognition that improves on state-of-the-art in using singletouch events in two ways. First by improving recognition accuracy from about 90% to about 95%, using about half the number of touches. Second by reducing the number of touches needed for training from about 200 to about 60. In addition, we use a new tactile sensor that is less than one tenth of the cost of widely available sensors. The paper describes the sensor, the likelihood function used with the Naive Bayes classifier, and experiments on a set of ten real objects. We also provide preliminary results to test our approach for its ability to generalise to previously unencountered objects.