Linear-Matrix-Inequality-Based Solution to Wahba’s Problem

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140644/1/1.g000132.pd

Passivity based nonlinear model predictive control (PNMPC) of multi-robot systems for space applications

In the past 2 decades, there has been increasing interest in autonomous multi-robot systems for space use. They can assemble space structures and provide services for other space assets. The utmost significance lies in the performance, stability, and robustness of these space operations. By considering system dynamics and constraints, the Model Predictive Control (MPC) framework optimizes performance. Unlike other methods, standard MPC can offer greater robustness due to its receding horizon nature. However, current literature on MPC application to space robotics primarily focuses on linear models, which is not suitable for highly non-linear multi-robot systems. Although Nonlinear MPC (NMPC) shows promise for free-floating space manipulators, current NMPC applications are limited to unconstrained non-linear systems and do not guarantee closed-loop stability. This paper introduces a novel approach to NMPC using the concept of passivity to multi-robot systems for space applications. By utilizing a passivity-based state constraint and a terminal storage function, the proposed PNMPC scheme ensures closed-loop stability and a superior performance. Therefore, this approach offers an alternative method to the control Lyapunov function for control of non-linear multi-robot space systems and applications, as stability and passivity exhibit a close relationship. Finally, this paper demonstrates that the benefits of passivity-based concepts and NMPC can be combined into a single NMPC scheme that maintains the advantages of each, including closed-loop stability through passivity and good performance through one-line optimization in NMPC

Multi-agent Motion Planning for Dense and Dynamic Environments via Deep Reinforcement Learning

This paper introduces a hybrid algorithm of deep reinforcement learning (RL) and Force-based motion planning (FMP) to solve distributed motion planning problem in dense and dynamic environments. Individually, RL and FMP algorithms each have their own limitations. FMP is not able to produce time-optimal paths and existing RL solutions are not able to produce collision-free paths in dense environments. Therefore, we first tried improving the performance of recent RL approaches by introducing a new reward function that not only eliminates the requirement of a pre supervised learning (SL) step but also decreases the chance of collision in crowded environments. That improved things, but there were still a lot of failure cases. So, we developed a hybrid approach to leverage the simpler FMP approach in stuck, simple and high-risk cases, and continue using RL for normal cases in which FMP can't produce optimal path. Also, we extend GA3C-CADRL algorithm to 3D environment. Simulation results show that the proposed algorithm outperforms both deep RL and FMP algorithms and produces up to 50% more successful scenarios than deep RL and up to 75% less extra time to reach goal than FMP.Comment: IEEE Robotics and Automation Letters (2020

Training a terrain traversability classifier for a planetary rover through simulation

A classifier training methodology is presented for Kapvik, a micro-rover prototype. A simulated light detection and ranging scan is divided into a grid, with each cell having a variety of characteristics (such as number of points, point variance and mean height) which act as inputs to classification algorithms. The training step avoids the need for time-consuming and error-prone manual classification through the use of a simulation that provides training inputs and target outputs. This simulation generates various terrains that could be encountered by a planetary rover, including untraversable ones, in a random fashion. A sensor model for a three-dimensional light detection and ranging is used with ray tracing to generate realistic noisy three-dimensional point clouds where all points that belong to untraversable terrain are labelled explicitly. A neural network classifier and its training algorithm are presented, and the results of its output as well as other popular classifiers show high accuracy on test data sets after training. The network is then tested on outdoor data to confirm it can accurately classify real-world light detection and ranging data. The results show the network is able to identify terrain correctly, falsely classifying just 4.74% of untraversable terrain

Progestogenic effects of tibolone on human endometrial cancer cells

Tibolone, a synthetic steroid acting in a tissue-specific manner and used in hormone replacement therapy, is converted into three active metabolites: a Delta(4) isomer (exerting progestogenic and androgenic effects) and two hydroxy metabolites, 3 alpha-hydroxytibolone (3 alpha-OH-tibolone) and 3beta-OH-tibolone (exerting estrogenic effects). In the present study an endometrial carcinoma cell line (Ishikawa PRAB-36) was used to investigate the progestogenic properties of tibolone and its metabolites. This cell line contains progesterone receptors A and B, but lacks estrogen and androgen receptors. When tibolone was added to the cells, complete conversion into the progestogenic/androgenic Delta(4) isomer was observed within 6 d. Furthermore, when cells were cultured with tibolone or when the Delta(4) isomer or the established progestagen medroxyprogesterone acetate was added to the medium, marked inhibition of growth was observed. Interestingly, 3 beta-OH-tibolone also induces some inhibition of growth. These growth inhibitions were not observed in progesterone receptor-negative parental Ishikawa cells, and progestagen-induced growth inhibition of PRAB-36 cells could readily be reversed using the antiprogestagen Org-31489. Upon measuring the expression of two progesterone-regulated genes (fibronectin and IGF-binding protein-3), tibolone, the Delta(4) isomer and medroxyprogesterone acetate showed similar gene expression regulation. These results indicate that tibolone, the Delta(4) metabolite, and to some extent 3 beta-OH-tibolone exert progestogenic effects. Tibolone and most likely 3 beta-OH-tibolone are converted into the Delta(4) metabolite

Consequences of loss of progesterone receptor expression in development of invasive endometrial cancer

PURPOSE: In endometrial cancer, loss of progesterone receptors (PR) is associated with more advanced disease. This study aimed to investigate the mechanism of action of progesterone and the loss of its receptors (PRA and PRB) in development of endometrial cancer. EXPERIMENTAL DESIGN: A 9600-cDNA microarray analysis was performed to study regulation of gene expression in the human endometrial cancer subcell line Ishikawa PRAB-36 by the progestagen medroxy progesterone acetate (MPA). Five MPA-regulated genes were selected for additional investigation. Expression of these genes was studied by Northern blot and by immunohistochemistry in Ishikawa subcell lines expressing different PR isoforms. Additionally, endometrial cancer tissue samples were immunohistochemically stained to study the in vivo protein expression of the selected genes. RESULTS: In the PRAB-36 cell line, MPA was found to regulate the expression of a number of invasion- and metastasis-related genes. On additional investigation of five of these genes (CD44, CSPG/Versican, Tenascin-C, Fibronectin-1, and Integrin-beta 1), it was observed that expression and progesterone regulation of expression of these genes varied in subcell lines expressing different PR isoforms. Furthermore, in advanced endometrial cancer, it was shown that loss of expression of both PR and E-cadherin was associated with increased expression CD44 and CSPG/Versican. CONCLUSION: The present study shows that progestagens exert a modulatory effect on the expression of genes involved in tumor cell invasion. As a consequence, loss of PR expression in human endometrial cancer may lead to development of a more invasive phenotype of the respective tumor