This paper presents CoCube a tabletop modular robotics platform designed for robotics education and multirobot algorithm research. CoCube is characterized by its low cost low floors high ceilings and wide walls offering flexibility and broad applicability across various use cases. The platform comprises four key components: CoCube robots which integrate wireless communication movement and interaction; CoModules which provide versatile external functionality; CoMaps which enable high-precision localization via microdot patterns on regular printed paper; and CoTags for interaction. CoCube operates on MicroBlocks a blocks programming language for physical computing inspired by MIT Scratch a widely-used coding language with a simple visual interface that makes programming accessible to young learners. It offers users both flexibility and ease of use with advanced API support for more complex applications. This paper details the design of the CoCube platform and demonstrates its potential in both educational and research contexts.

Despeckling is an important preprocessing step for synthetic aperture radar (SAR) image understanding and interpretation, and it can be regarded as an ill-posed inverse problem that heavily depends on the exploration of proper priors on clean images. Motivated by remarkable capability of deep generative priors, we aim to leverage diffusion denoising probabilistic model (DDPM) to achieve a good tradeoff between speckle suppression and detail preservation. To do so, we design a diffusion posterior sampling scheme based on the approximation of likelihood score function via estimation of reverse transition distribution and utilization of multiplicative noise model. The proposed method has two distinct superiorities and the first one lies in the pretrained model, escaping from the limitations of the pairs of clean and speckled images and the high computation complexity of supervised training. More importantly, the proposed method needs no transforms of SAR images, avoiding changing the statistical distribution of speckle and approximating the complex distribution in transform domain. Finally, we empirically validate the proposed method on synthetic and real SAR images, and the visual effects and the quantitative evaluation of the experimental results indicate the superiority of the proposed method over the state-of-the-art compared approaches. Besides, the robustness of the proposed method is also validated via analyzing its performance under the conditions of different speckle intensities, different initial samples (ISs) of diffusion models, and different values of the algorithm parameters.

Image fusion (IF) aims to integrate valuable information from multiple sources, and the fusion of optical image and synthetic aperture radar is at the core of remote sensing IF. Despite the remarkable capability of deep learning in IF, it often depends on costly datasets, lacks explicit interpretability, and has instable training process. Diffusion models, such as diffusion denoising probabilistic model and its continuous form (i.e., score-based diffusion model), exhibit powerful generative ability and offers novel ideas to address the hurdles above. To this end, it is kernel and challenge to robustly estimate the posterior distribution of ground truth w.r.t given measurements. In this letter, we propose a variational diffusion IF method for robust estimation of posterior distribution. First of all, an optimization model is developed in the framework of variational inference, where the fidelity term comes from traditional optimization fusion model and the regularization term is actually a weighted integral form of the score function matching. And then, a fast numerical algorithm is designed via first-order stochastic optimization, significantly reducing computational complexity. Finally, the proposed method is validated in experiments with WHU-OPT-SAR and SEN1-2 real datasets, and the results show that the proposed method not only outperforms the state-of-the-art methods in terms of fused image quality, but also almost has half of computational complexity of existing diffusion model IF methods.