Morphology of a robot design is important to its ability to achieve a stated goal and therefore applying machine learning approaches that incorporate morphology in the design space can provide scope for significant advantage. Our study is set in a domain known to be reliant on morphology: flapping wing flight. We developed a parameterised morphology design space that draws features from biological exemplars and apply automated design to produce a set of high performance robot morphologies in simulation. By performing sim2real transfer on a selection, for the first time we measured the shape of the reality gap for variations in design complexity. We found for the flapping wing that the reality gap changes non-monotonically with complexity, suggesting that certain morphology details narrow the gap more than others, and that such details could be identified and further optimised in a future end-to-end automated morphology design process.

Advancements in deep neural networks have contributed to near-perfect results for many computer vision problems, such as object recognition, face recognition, and pose estimation. However, human action recognition is still far from human-level performance. Owing to the articulated nature of the human body, it is challenging to detect an action from multiple viewpoints, particularly from an aerial viewpoint. This is further compounded by a scarcity of datasets that cover multiple viewpoints of actions. To fill this gap and enable research in wider application areas, in this article we present a multiviewpoint outdoor action recognition dataset collected from YouTube and our own drone. The dataset consists of 20 dynamic human action classes, 2324 video clips, and 503 086 frames. All videos are cropped and resized to 720 × 720 without distorting the original aspect ratio of the human subjects in videos. This dataset should be useful to many research areas, including action recognition, surveillance, and situational awareness. We evaluate the dataset with a two-stream convolutional neural network architecture coupled with a recently proposed temporal pooling scheme called kernelized rank pooling that produces nonlinear feature subspace representations. The overall baseline action recognition accuracy is 74.0%.

Objective image quality assessment (IQA) is a challenge facing digital image and video processing systems because image quality is distorted during various applications, including restoration, compression, storage and transmission. Therefore, this study proposes a new methodology based on a fuzzy interface system called quality evaluation system (QES) to measure the total quality index (TQI) of input videos with many distorted situations. Nine quality metrics [peak signal‐to‐noise ratio, visual signal‐to‐noise ratio, weighted signal‐to‐noise ratio, structural similarity (SSIM), multi‐scale SSIM, universal image quality index, visual information fidelity, information fidelity criterion and noise quality measure] were used as inputs for three fuzzy logic controller systems, and their outputs were set as inputs to another fuzzy logic controller system to obtain the TQI of the input video. This process contributes to obtain clear performance of the quality index of the input video despite the failure of some IQA methods in providing quality performance of the input video in some situations. The proposed QES is tested on four videos captured with different digital cameras under different noise levels. Furthermore, the authors evaluated the proposed QES on three databases (TID2008, TID2013 and LIVE) to improve the experimental results. In addition, the authors used wavelet decomposition and image de‐noising to enhance the standard Eulerian video magnification technique. The proposed QES was also used to prove that the authors' magnification system has better magnification quality index than other magnification techniques.

Vital parameter monitoring systems based on video camera imagery is a growing interest field in clinical and biomedical applications. Heart rate (HR) is one of the most important vital parameters of interest in a clinical diagnostic and monitoring system. This study proposed a noncontact HR and beat length measurement system based on both motion magnification and motion detection at four different regions of interest (ROIs) (wrist, arm, neck and leg). A motion magnification based on a Chebyshev filter was utilized in order to magnify heart pulses in different ROIs that are difficult to see with the naked eye. A new measuring system based on motion detection was used to measure HR and beat length by detecting rapid motion areas in the video frame sequences that represent the heart pulses and converting video frames into a corresponding logical matrix. Video quality metrics were also used to compare our magnification system with standard Eulerian video magnification to select which one has better magnification results and gives better results for the heart pulse. The 99.3% limits of agreement between the proposed system and reference measurement fall within[Formula: see text] beats/min based on Bland and Altman test. The proposed system is expected to produce new options for further noncontact information extraction.

Human factors, particularly the consoles and controls of an Unmanned Aerial Vehicle (UAV), have not rated a high enough focus in relation to Unmanned Aerial Vehicle mishaps. The United States Air Force has documented Predator UAV mishaps during their operation over the past 15 years. Through quantitative analysis of these mishaps this paper will demonstrate that human factors have been a prominent causal and contributing factor. We argue that UGV (Unmanned Ground Vehicle) human factors design could learn from this unique dataset.

Much of aerospace academia is anticipating a boom in Unmanned Aircraft (UA) funding and research opportunities. The expectation is built on the premise that UAs will revolutionize aerospace, which is likely based on current trends. There is also an anticipation of an increasing number of new platforms and research investment, which is likely but must be analysed carefully to determine where the opportunities might lie. This paper draws on the state of industry and a systems engineering approach. We explore what aspects of UAs really are the results of aerospace science advances and what aspects will be rather more mundane works of engineering.

Navigation has been a major challenge for the successful operation of an autonomous aircraft. Although success has been achieved using active methods such as radar, sonar, lidar and the global positioning system (GPS), such methods are not always suitable due to their susceptibility to jamming and outages. Vision, as a passive navigation method, is considered as an excellent alternative; however, the development of vision-based autonomous systems for outdoor environments has proven difficult. For flying systems, this is compounded by the additional challenges posed by environmental and atmospheric conditions. In this paper, we present a novel passive vision-based algorithm which is invariant to illumination, scale and rotation. We use a three stage landmark recognition algorithm and an algorithm for waypoint matching. Our algorithms have been tested in both synthetic and real-world outdoor environments demonstrating overall good performance. We further compare our feature matching method with the speed-up robust features (SURF) method with results demonstrating that our method outperforms the SURF method in feature matching as well as computational cost.

Tangential neurons in the fly brain are sensitive to the typical optic flow patterns generated during egomotion. In this study, we examine whether a simplified linear model based on the organization principles in tangential neurons can be used to estimate egomotion from the optic flow. We present a theory for the construction of an estimator consisting of a linear combination of optic flow vectors that incorporates prior knowledge about the distance distribution of the environment and about the noise and egomotion statistics of the sensor. The estimator is tested on a gantry carrying an omnidirectional vision sensor. The experiments show that the proposed approach leads to accurate and robust estimates of rotation rates, whereas translation estimates are of reasonable quality, albeit less reliable.

The tangential neurons in the fly brain are sensitive to the typical optic flow patterns generated during self-motion. In this study, we examine whether a simplified linear model of these neurons can be used to esti- mate self-motion from the optic flow. We present a theory for the con- struction of an estimator consisting of a linear combination of optic flow vectors that incorporates prior knowledge both about the distance distri- bution of the environment, and about the noise and self-motion statistics of the sensor. The estimator is tested on a gantry carrying an omnidirec- tional vision sensor. The experiments show that the proposed approach leads to accurate and robust estimates of rotation rates, whereas transla- tion estimates turn out to be less reliable.

A new approach called bioinspired engineering of exploration systems (BEES) and its value for solving pressing NASA and DoD needs are described. Insects (for example honeybees and dragonflies) cope remarkably well with their world, despite possessing a brain containing less than 0.01% as many neurons as the human brain. Although most insects have immobile eyes with fixed focus optics and lack stereo vision, they use a number of ingenious, computationally simple strategies for perceiving their world in three dimensions and navigating successfully within it. We are distilling selected insect-inspired strategies to obtain novel solutions for navigation, hazard avoidance, altitude hold, stable flight, terrain following, and gentle deployment of payload. Such functionality provides potential solutions for future autonomous robotic space and planetary explorers. A BEES approach to developing lightweight low-power autonomous flight systems should be useful for flight control of such biomorphic flyers for both NASA and DoD needs. Recent biological studies of mammalian retinas confirm that representations of multiple features of the visual world are systematically parsed and processed in parallel. Features are mapped to a stack of cellular strata within the retina. Each of these representations can be efficiently modeled in semiconductor cellular nonlinear network (CNN) chips. We describe recent breakthroughs in exploring the feasibility of the unique blending of insect strategies of navigation with mammalian visual search, pattern recognition, and image understanding into hybrid biomorphic flyers for future planetary and terrestrial applications. We describe a few future mission scenarios for Mars exploration, uniquely enabled by these newly developed biomorphic flyers.

Insects such as ants and bees are capable of surprisingly good navigation, despite the small size and relative simplicity of their brains. Recent experimental research in our laboratory, summarized in this chapter, indicates that honeybees estimate the distance travelled to a food site in terms of the image motion that they experience en route. This finding has inspired us to design and build a robot that navigates using a visually-driven odometer.