Robust Distance-Based Formation Control of Multiple Rigid Bodies with Orientation Alignment

This paper addresses the problem of distance- and orientation-based formation control of a class of second-order nonlinear multi-agent systems in 3D space, under static and undirected communication topologies. More specifically, we design a decentralized model-free control protocol in the sense that each agent uses only local information from its neighbors to calculate its own control signal, without incorporating any knowledge of the model nonlinearities and exogenous disturbances. Moreover, the transient and steady state response is solely determined by certain designer-specified performance functions and is fully decoupled by the agents' dynamic model, the control gain selection, the underlying graph topology as well as the initial conditions. Additionally, by introducing certain inter-agent distance constraints, we guarantee collision avoidance and connectivity maintenance between neighboring agents. Finally, simulation results verify the performance of the proposed controllers.Comment: IFAC Word Congress 201

Position and Orientation Based Formation Control of Multiple Rigid Bodies with Collision Avoidance and Connectivity Maintenance

This paper addresses the problem of position- and orientation-based formation control of a class of second-order nonlinear multi-agent systems in a $3$D workspace with obstacles. More specifically, we design a decentralized control protocol such that each agent achieves a predefined geometric formation with its initial neighbors, while using local information based on a limited sensing radius. The latter implies that the proposed scheme guarantees that the initially connected agents remain always connected. In addition, by introducing certain distance constraints, we guarantee inter-agent collision avoidance as well as collision avoidance with the obstacles and the boundary of the workspace. The proposed controllers employ a novel class of potential functions and do not require a priori knowledge of the dynamical model, except for gravity-related terms. Finally, simulation results verify the validity of the proposed framework

Current management of the gastrointestinal complications of systemic sclerosis.

Systemic sclerosis is a multisystem autoimmune disorder that involves the gastrointestinal tract in more than 90% of patients. This involvement can extend from the mouth to the anus, with the oesophagus and anorectum most frequently affected. Gut complications result in a plethora of presentations that impair oral intake and faecal continence and, consequently, have an adverse effect on patient quality of life, resulting in referral to gastroenterologists. The cornerstones of gastrointestinal symptom management are to optimize symptom relief and monitor for complications, in particular anaemia and malabsorption. Early intervention in patients who develop these complications is critical to minimize disease progression and improve prognosis. In the future, enhanced therapeutic strategies should be developed, based on an ever-improving understanding of the intestinal pathophysiology of systemic sclerosis. This Review describes the most commonly occurring clinical scenarios of gastrointestinal involvement in patients with systemic sclerosis as they present to the gastroenterologist, with recommendations for the suggested assessment protocol and therapy in each situation

Tomographic Image Reconstruction with a Spatially Varying Gamma Mixture Prior

A spatially varying Gamma mixture model prior is employed for tomographic image reconstruction, ensuring effective noise elimination and the preservation of region boundaries. We define a line process, modeling edges between image segments, through appropriate Markov random field smoothness terms which are based on the Student’s t-distribution. The proposed algorithm consists of two alternating steps. In the first step, the mixture model parameters are automatically estimated from the image. In the second step, the reconstructed image is estimated by optimizing the maximum-a-posteriori criterion using the one-step-late expectation–maximization and preconditioned conjugate gradient algorithms. Numerical experiments on various photon-limited image scenarios show that the proposed model outperforms the compared state-of-the-art reconstruction models. © 2018, Springer Science+Business Media, LLC, part of Springer Nature