A near-to-far non-parametric learning approach for estimating traversability in deformable terrain

It is well recognized that many scientifically interesting sites on Mars are located in rough terrains. Therefore, to enable safe autonomous operation of a planetary rover during exploration, the ability to accurately estimate terrain traversability is critical. In particular, this estimate needs to account for terrain deformation, which significantly affects the vehicle attitude and configuration. This paper presents an approach to estimate vehicle configuration, as a measure of traversability, in deformable terrain by learning the correlation between exteroceptive and proprioceptive information in experiments. We first perform traversability estimation with rigid terrain assumptions, then correlate the output with experienced vehicle configuration and terrain deformation using a multi-task Gaussian Process (GP) framework. Experimental validation of the proposed approach was performed on a prototype planetary rover and the vehicle attitude and configuration estimate was compared with state-of-the-art techniques. We demonstrate the ability of the approach to accurately estimate traversability with uncertainty in deformable terrain.

Traversability estimation for a planetary rover via experimental kernel learning in a Gaussian process framework

A critical requirement for safe autonomous navigation of a planetary rover is the ability to accurately estimate the traversability of the terrain. This work considers the problem of predicting the attitude and configuration angles of the platform from terrain representations that are often incomplete due to occlusions and sensor limitations. Using Gaussian Processes (GP) and exteroceptive data as training input, we can provide a continuous and complete representation of terrain traversability, with uncertainty in the output estimates. In this paper, we propose a novel method that focuses on exploiting the explicit correlation in vehicle attitude and configuration during operation by learning a kernel function from vehicle experience to perform GP regression. We provide an extensive experimental validation of the proposed method on a planetary rover. We show significant improvement in the accuracy of our estimation compared with results obtained using standard kernels (Squared Exponential and Neural Network), and compared to traversability estimation made over terrain models built using state-of-the-art GP techniques.

Orienting preservice teachers towards gifted education : School university partnerships

Addressing the needs of gifted students is predicated on an understanding of many factors not least the nature of giftedness, appropriate curriculum design and specialist pedagogical practices. Knowledge needs to be acquired in context. Preservice teacher education programs tend to focus on pedagogical practices and present preservice teachers with content related to inclusive philosophies, strategies for teaching, and assessment techniques. Many preservice teachers do not have an awareness of the nature of giftedness or understandings around models of curriculum advocated for gifted education, despite practicum experiences and university education. This paper presents two case studies that describe interventions constructed through partnerships with schools to raise awareness of the nature of giftedness and provide concrete experiences for preservice teachers’ interactions with gifted students. It will report strategies through which preservice teachers become engaged with gifted students in regular classrooms. Qualitative and quantitative evidence will be presented on the effectiveness of these models.