This paper describes the design, fabrication, experimental testing and performance optimization of the morphology of a flapping wing for use on a robot capable of aerial and aquatic modes of locomotion. The focus of the optimization studies is that of wing design for aquatic propulsion. Inspiration for the research stems from numerous avian species which use a flapping wing for the dual purpose of locomotion (propulsion) in both air and water. The main aim of this research is to determine optimal kinematic parameters for marine locomotion that maximize nondimensionalized performance measures (e.g., propulsive efficiency), derived from analysis of avian wing morphing mechanisms that balance competing demands of both aerial and aquatic movement. Optimization of the kinematic parameters enables the direct comparison between outstretched (aerial) and retracted (aquatic) wing morphologies and permits trade-off studies in the design space for future robotic vehicles. Static foils representing the wing in both an extended and retracted orientation have been manufactured and subsequently subjected to testing over a range of kinematics. Details of the purpose built 2 degree-of-freedom (dof) flapping mechanism are presented. The gathered results enable validation of previously developed numerical models as well as quantifying achievable performance measures. This research focuses on the mechanical propulsive efficiencies and thrust coefficients as key performance measures whilst simultaneously considering the required mechanical input torques and the associated thrust produced.
This paper presents a biologically inspired architecture for rapid real-time control of autonomous or semi-autonomous vehicles based on a neural model of the escape response of the American cockroach, Periplaneta americana. The architecture fuses exteroceptive and proprioceptive inputs in a manner similar to the insect to produce commands for collision avoidance and, in some cases, orientation for target strike. It functions as a reflexive subsystem that integrates smoothly with higher-level planning and behavioral control systems. The performance of the reflex is demonstrated in simulation and in hardware experiments on both air and ground vehicles, even in the presence of noisy, false or disruptive sensor data. (AWARDED: BEST PAPER IN JOURNAL, 2012)
Dielectric elastomer electroactive polymers are an emerging class of actuation technology which is inherently compliant and capable of large actuation stresses and strains. Despite promising performance characteristics, their fabrication has been inhibited by two significant factors: (i) the requirement for consistently thin dielectric layers, to minimise activation voltages; (ii) automated production of multilayered configurations, to increase the actuation power. This paper presents a robust, low-cost fabrication technique that overcomes these issues by utilising optimised spray deposition. Spray deposition of silicone dielectric elastomer actuators (DEAs) offers numerous benefits including scalability, flexibility for different DEA configurations and multilayered assembly with a high degree of automation. A predictive model based on the Gaussian distribution is used to characterise the profile of deposited elastomer layers for principal fabrication parameters. This model enables individual dielectric layers to be composed from multiple parallel depositions, which greatly increases scalability as demonstrated by fabricated DEA films with planar dimensions from 25 mm(2) to over 10,000 mm(2). Using the predictive model, a new figure of merit is introduced for analyzing DEA film profiles by considering the estimated mean Maxwell stress that is feasible for a specific dielectric breakdown strength. The analysis suggests that compared to a single deposition, a film composed of four parallel depositions will increase the maximum characteristic DEA dimension by an order of magnitude, while producing a comparable mean Maxwell stress. A significant advantage of the presented spray deposition technique is the semi-automated layering process, creating stratified solid-state actuators. By eliminating the stacking of layers from the fabrication process, inherent electrical isolation, good layer-to-layer bonding and capacity for more complex 3D geometries is achieved. A proof-of-concept multilayer unimorph and stack DEA is presented to validate the fabrication technique through static and dynamic displacement tests.
A strategy is introduced to rank and select principal component transform (PCT) and discrete cosine transform (DCT) transform coefficient features to overcome the curse of dimensionality frequently encountered in implementing multivariate signal classifiers due to small sample sizes. The criteria considered for ranking include the magnitude, variance, interclass separation, and classification accuracies of the individual features. The feature ranking and selection strategy is applied to overcome the dimensionality problem, which often plagues the implementation and evaluation of practical Gaussian signal classifiers. The applications of the resulting PCT- and DCT-Gaussian signal classification strategies are demonstrated by classifying single-channel tongue-movement ear-pressure signals and multichannel event-related potentials. Through these experiments, it is shown that the dimension of the feature space can be decreased quite significantly by means of the feature ranking and selection strategy. The ranking strategy not only facilitates overcoming the dimensionality curse for multivariate classifier implementation but also provides a means to further select, out of a rank-ordered set, a smaller set of features that give the best classification accuracies. Results show that the PCT- and DCT-Gaussian classifiers yield higher classification accuracies than those reported in previous classification studies on the same signal sets. Among the combinations of the two transforms and four feature selection criteria, the PCT-Gaussian classifiers using the maximum magnitude and maximum variance selection criteria gave the best classification accuracies across the two sets of classification experiments. Most noteworthy is the fact that the multivariate Gaussian signal classifiers developed in this paper can be implemented without having to collect a prohibitively large number of training signals simply to satisfy the dimensionality conditions. Consequently, the classification strategies can be beneficial for designing personalized human-machine interface signal classifiers for individuals from whom only a limited number of training signals can reliably be collected due to severe disabilities.
This paper presents a numerical model of a morphing wing supporting the development of a biologically inspired vehicle capable of aerial and aquatic of locomotion. The model draws inspiration from the seabird Uria aalge, the common guillemot. It is implemented within a parametric study associated with aerial and aquatic performance, specifically aiming at minimizing energy of locomotion. The implications of varying wing geometry and kinematic parameters are investigated and presented in the form of nested performance charts. Trends within both the aquatic and aerial model are discussed highlighting the implications of parameter variation on the power requirements associated with both mediums. Conflicts of geometric parameter selection are contrasted between the aerial and aquatic model, as well as other trends that impact the design of concept vehicles with this capability. The model has been validated by implementing a heuristic optimization of its key parameters under conditions akin to those of the actual bird; optimal parameters output by the model correlate to the actual behaviour of the guillemot.
The goal of this paper is to introduce a new strategy to accurately classify event-related potentials (ERPs), recorded using dense electrode arrays, into predefined brain activity categories. The challenge is to exploit the enhanced spatial information offered by dense arrays while overcoming the significant increase in the dimensionality problem introduced by the large increase in the number of channels. These conflicting objectives are achieved by introducing a spatiotemporal-array model to observe the dense-array ERP amplitude variations across channels and time, simultaneously. To account for latency variations and EEG noise in the array elements, each spatiotemporal element in the array is initially modeled as a Gaussian random variable. A two-step process that uses the Kolmogrov-Smirnov test and the Lilliefors test is formulated to select the array elements that have different Gaussian densities across all ERP categories. Selecting spatiotemporal elements that fit the assumed model and also statistically differ across the ERP categories not only ensures high classification accuracies but also decreases the dimensionality significantly. The selection is dynamic in the sense that selecting spatiotemporal-array elements corresponds to selecting ERP samples of different channels at different time instants. Each selected array element is classified using a univariate Gaussian classifier, and the resulting decisions are fused into a decision fusion vector that is classified using a discrete Bayes classifier. By converting an inherently multivariate classification problem into a simpler problem involving only univariate classifications, the dimensionality problem that plagues the design of practical multivariate ERP classifiers is circumvented. Consequently, classifiers can be designed to classify the ERPs that are unique to an individual without having to collect a prohibitively large ERP dataset from him/her. The application of the resulting dynamic-channel-selection-based classification strategy is demonstrated by designing and testing classifiers for eight subjects using ERPs from a Stroop color test and it is shown that the strategy yields high classification accuracies. Finally, it is noted that because of the generalized formulation of the strategy, it can be applied to various other problems involving the classification of multivariate signals acquired from multiple identical or multiple heterogeneous sensors.
Robot teleoperation systems have been limited in their utility due to the need for operator motion, lack of portability and limitation to singular input modalities. In this article, the design and construction of a dual-mode human machine interface system for robot teleoperation addressing all these issues is presented. The interface is capable of directing robotic devices in response to tongue movement and/or speech without insertion of any device in the vicinity of the oral cavity. The interface is centered on the unique properties of the human ear as an acoustic output device. Specifically, we present: (1) an analysis of the sensitivity of human ear canals as acoustic output device; (2) the design of a new sensor for monitoring airflow in the aural canal; (3) pattern recognition procedures for recognition of both speech and tongue movement by monitoring aural flow across several human test subjects; and (4) a conceptual design and simulation of the machine interface system. We believe this work will lay the foundation for a new generation of human machine interface systems for all manner of robotic applications.
The focus of this research is to address the criticality and vulnerability of commercial shipping in the Straits of Mallacca by designing and evaluating competing systems architectures that could provide sufficient maritime domain protection. The category of primary concern was the introduction of a weapon of mass destruction (WMD) in a cargo container. The Maritime Domain Protection (MDP) physical architecture alternatives combined five separate systems: 1) a land-based cargo inspection system, 2) a sensor system, 3) a C3I (command and control, communications, and intelligence) system, 4) a force response system, and 5) a sea-based cargo inspection system. Individual models for each system were developed and combined into an overarching integrated architecture model to evaluate overall performance. Study results based on current technology showed that while solutions were found to effectively reduce risk in the WMD threat scenario, effective suppression came at great expense and included the participation of commercial shipping companies. A range of alternative cost-effective solutions were also found, but with limited performance. Future work involves using the developed architecture as a test bed for evaluating the overall impact and effectiveness of new technologies and research (such as smart containers) on MDP and homeland security.