Vision and Learning for Deliberative Monocular Cluttered Flight

Cameras provide a rich source of information while being passive, cheap and lightweight for small and medium Unmanned Aerial Vehicles (UAVs). In this work we present the first implementation of receding horizon control, which is widely used in ground vehicles, with monocular vision as the only sensing mode for autonomous UAV flight in dense clutter. We make it feasible on UAVs via a number of contributions: novel coupling of perception and control via relevant and diverse, multiple interpretations of the scene around the robot, leveraging recent advances in machine learning to showcase anytime budgeted cost-sensitive feature selection, and fast non-linear regression for monocular depth prediction. We empirically demonstrate the efficacy of our novel pipeline via real world experiments of more than 2 kms through dense trees with a quadrotor built from off-the-shelf parts. Moreover our pipeline is designed to combine information from other modalities like stereo and lidar as well if available

Dynamic cutting process modelling and its impact on the generation of surface topography and texture in nano/micro cutting

In the nano/micro cutting process, the surface quality is heavily dependent on all the dynamic factors, including those from the material, tooling, process parameters, servo accuracy, mechanical structural stiffness, and non-linear factors as well. The machined surface is generated based on the tool profile and the real tool path combining with the various external and internal disturbances. To bridge the gap between the cutting process and the surface topography/texture generation, an integrated simulation-based approach is presented involving the dynamic cutting process, control/drive system, and the surface generation. The simulations take account of all the intricate aspects of the cutting process resulting in the surface topography and texture formation, such as material heterogeneity, regenerative chatter, built-up edge (BUE), tool wear, spindle runout, environmental vibration, tool interference, etc. Both the frequency ratio method and sampling theoremare used to interpret the surface topography and texture formation. The effects of non-linear factors on the surface generation are simulated and analysed through the power spectral density (PSD) and significance on surface texture. The relationships among cutting force, tool path, and surface profile are discussed in detail. Furthermore, the proposed systematic modelling approach is verified by cutting trials, which provide the coincident results of the surface topography and areal power spectral density (APSD)

Review of the mathematical foundations of data fusion techniques in surface metrology

The recent proliferation of engineered surfaces, including freeform and structured surfaces, is challenging current metrology techniques. Measurement using multiple sensors has been proposed to achieve enhanced benefits, mainly in terms of spatial frequency bandwidth, which a single sensor cannot provide. When using data from different sensors, a process of data fusion is required and there is much active research in this area. In this paper, current data fusion methods and applications are reviewed, with a focus on the mathematical foundations of the subject. Common research questions in the fusion of surface metrology data are raised and potential fusion algorithms are discussed

Modeling of evolving textures using granulometries

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