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Fast-paced dynamic environments like robot soccer require highly responsive and dynamic locomotion. We present an implementation of an omnidirectional ZMP-based walk engine for the Nao robot. Using a simple inverted pendulum model, a preview controller generates dynamically balanced center of mass trajectories. To enable path planning, we introduce a system of global and egocentric coordinate frames...
This paper characterises the soil conditions at the Sheringham Shoal wind farm in the North Sea, in the context of designing foundations for offshore wind turbines. The purpose is to provide a realistic reference case study for research and practice in the emerging field of offshore wind energy. The soil data was obtained between 2005 and 2008 in an investigation program involving field measurements...
Reliable and accurate camera calibration usually requires an expert intuition to reliably constrain all of the parameters in the camera model. Existing toolboxes ask users to capture images of a calibration target in positions of their choosing, after which the maximum-likelihood calibration is computed using all images in a batch optimization. We introduce a new interactive methodology that uses...
Multi-robot teams are often constrained by communications; better signal-strength models enable more efficient coordination while still maintaining adequate communication. This work discusses several prediction algorithms applicable to this scenario. Whereas previous approaches typically focus on prediction in the presence of deployed base-stations, we consider the more general problem where all nodes...
Tasks like search‐and‐rescue and urban reconnaissance benefit from large numbers of robots working together, but high levels of autonomy are needed to reduce operator requirements to practical levels. Reducing the reliance of such systems on human operators presents a number of technical challenges, including automatic task allocation, global state and map estimation, robot perception, path planning,...
We introduce a method for efficiently rasterizing large occupancy grids. Efficient Maximum Likelihood Estimation (MLE) of robot trajectories has been shown to be highly scalable using sparse SLAM algorithms such as SqrtSAM, but unfortunately such approaches don't directly provide a rasterized grid map. We harness these existing SLAM methods to compute maximum likelihood (ML) robot trajectories and...
We introduce a method for efficiently rasterizing large occupancy grids. Efficient Maximum Likelihood Estimation (MLE) of robot trajectories has been shown to be highly scalable using sparse SLAM algorithms such as SqrtSAM, but unfortunately such approaches don't directly provide a rasterized grid map. We harness these existing SLAM methods to compute maximum likelihood (ML) robot trajectories and...
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