We investigate the sampling-based optimal path planning problem for robotics in complex and dynamic environments. Most existing sampling-based algorithms neglect environmental information or the information from previous samples. Yet, these pieces of information are highly informative, as leveraging them can provide better heuristics when sampling the next state. In this paper, we propose a novel sampling-based planning algorithm, called RRT*former, which integrates the standard RRT* algorithm with a Transformer network in a novel way. Specifically, the Transformer is used to extract features from the environment and leverage information from previous samples to better guide the sampling process. Our extensive experiments demonstrate that, compared to existing sampling-based approaches such as RRT*, Neural RRT*, and their variants, our algorithm achieves considerable improvements in both the optimality of the path and sampling efficiency. The code for our implementation is available on https://github.com/fengmingyang666/RRTformer.

This paper investigates the problem of safe visual servoing control of manipulators using an uncalibrated eye-in-hand camera based on control barrier functions (CBFs). Traditional CBFs are defined in the workspace, corresponding to the global coordinates of the base frame. However, when the camera’s position or orientation is adjusted for a better field of view, it becomes uncalibrated, making it challenging to obtain the precise positions of the robot and obstacles using onboard sensors like a camera. To address this, we propose a novel visual servoing control barrier function (VS-CBF) for manipulators, which depends only on the image and depth data sensed by an RGB-D camera. Given an uncalibrated camera, we develop an adaptive estimator for the unknown camera parameters. Based on this estimator, we also design a kinematic visual servoing control law as a nominal controller, ensuring the convergence of the robotic system. The safe controller is then obtained by solving a quadratic programming problem that incorporates the designed VS-CBF and the nominal controller. Finally, experimental results conducted on a UR3 manipulator are presented to demonstrate the effectiveness of our approach.

In this paper, we present an open-source pure-Python library called PyPop7 for black-box optimization (BBO). As population-based methods (e.g., evolutionary algorithms, swarm intelligence, and pattern search) become increasingly popular for BBO, the design goal of PyPop7 is to provide a unified API and elegant implementations for them, particularly in challenging high-dimensional scenarios. Since these population-based methods easily suffer from the notorious curse of dimensionality owing to random sampling as one of core operations for most of them, recently various improvements and enhancements have been proposed to alleviate this issue more or less mainly via exploiting possible problem structures: such as, decomposition of search distribution or space, low-memory approximation, low-rank metric learning, variance reduction, ensemble of random subspaces, model self-adaptation, and fitness smoothing. These novel sampling strategies could better exploit different problem structures in high-dimensional search space and therefore they often result in faster rates of convergence and/or better qualities of solution for large-scale BBO. Now PyPop7 has covered many of these important advances on a set of well-established BBO algorithm families and also provided an open-access interface to adding the latest or missed black-box optimizers for further functionality extensions. Its well-designed source code (under GPL-3.0 license) and full-fledged online documents (under CC-BY 4.0 license) have been freely available at https://github.com/Evolutionary-Intelligence/pypop and https://pypop.readthedocs.io, respectively.

BACKGROUND: Few studies have demonstrated that the relationship between m6A-related genes and the prognosis, tumor microenvironment and drug resistance of LC. METHODS: The main results were analyzed with bioinformatics methods. RESULTS: Hence, we found 10 m6A-related genes expressed less in tumor samples in comparison with normal ones. Using consensus clustering, all LC patients were grouped into 2 subgroups according to the overall expression of 10 differential expressed m6A-related genes. In two clusters, the OS and immune characteristics were different. We analyzed the predictive potential of 10 m6A-related genes in the prognosis of LC, and obtained a risk prognosis model on the strength of ZC3H13, CBLL1, ELAVL1 and YTHDF1 as the hub candidate genes through LASSO cox. The expression of 4 hub m6A-related genes was validated by IHC in the HPA database. The infiltration level of dendritic cell, CD4+ T cell and neutrophil that were affected by CNV level of m6A-related genes in LUAD and LUSC patients. Moreover, based on GSCALite database, we found that LUSC patients with hypermethylation tended to have a better overall survival. In terms of drug sensitivity, etoposide correlated negatively with ELAVL1, HNRNPC, RBM15B, YTHDF2 and CBLL1. ZC3H13 had positively association with afatinib, while HNRNPC was positively associated with dasatinib, erlotinib, lapatinib and TGX221. Crizotinib had a negative correlation with ELAVL1, CBLL1, HNRNPC and RBM15B. CONCLUSION: In conclusion, m6A-related genes are important participants in LC and the expression levels of ZC3H13, CBLL1, ELAVL1 and YTHDF1 are significant for prediction and treatment of LC. Researches of drug resistance based on m6A-related genes need to pay more attention for producing new therapeutic strategies of LC and CBLL1 may contribute to target treatment for further research.