Receptor trafficking controls weak signal delivery: a strategy used by c-Met for STAT3 nuclear accumulation

C-Met, the receptor of hepatocyte growth factor (HGF), through overexpression or mutation, is a major protooncogene that provides an attractive molecular target for cancer therapy. HGF/c-Met–induced tumorigenesis is dependent, in part, on the transcription factor and oncogene signal transducer and activator of transcription 3 (STAT3), which is believed to be activated by the receptor at the plasma membrane and then to travel to the nucleus where it acts. We demonstrate that although the robust signal to STAT3 elicited from the cytokine oncostatin-M does indeed support this mechanism of STAT3 action, for the weaker STAT3 signal emanating from c-Met, the activated receptor itself needs to be delivered to a perinuclear endosomal compartment to sustain phosphorylated STAT3 in the nucleus. This is signal specific because c-Met–induced extracellular signal-regulated kinase nuclear accumulation does not require receptor trafficking to the perinuclear compartment. This response is triggered from peripheral endosomes. Thus, control of growth factor receptor traffic determines the nature of the signal output, providing novel opportunities for intervention

An Observer-based Longitudinal Control of Car-like Vehicles Platoon Navigating in an Urban Environment

International audienceIn this paper, we study longitudinal motion controlof car-like vehicles platoon navigating in an urban environmentwith minimum communication links. To achieve a higher trafficflow, a constant-spacing policy between successive vehicles iscommonly used but this is at a cost of an increased number ofcommunication links as any vehicle information must broadcastto all its followers. Therefore, we propose a distributed observer-based control law that depends both on communicated andmeasured information. Our formulation allows designing thecontrol law directly in the curvilinear coordinates. Internal andstring stability analysis are conducted. We provide simulationresults, through dynamic vehicular mobility simulator, to illus-trate the feasibility of the proposed approach and corroborate our theoretical findings

Multi-sensor data fusion in sensor-based control: application to multi-camera visual servoing

International audienceA low-level sensor fusion scheme is presented for the positioning of a multi-sensor robot. This non-hierarchical framework can be used for robot arms or other velocity- controlled robots, and is part of the task function approach. A stability analysis is presented for the general case, then several control laws illustrate the versatility of the framework. This approach is applied to the multi-camera eye-in-hand/eye- to-hand configuration in visual servoing. Experimental results point out the feasibility and the effectiveness of the proposed control laws. Mono-camera and multi-camera schemes are compared, showing that the proposed sensor fusion scheme improves the behavior of a robot arm

Avoiding joint limits with a low-level fusion scheme

International audienceJoint limits avoidance is a crucial issue in sensor- based control. In this paper we propose an avoidance strategy based on a low-level data fusion. The joint positions of a robot arm are considered as features that are continuously added to the control scheme when they approach the joint limits, and removed when the position is safe. We expose an optimal tuning of the avoidance scheme, ensuring the main task is disturbed as little as possible. We propose additional strategies to solve the particular cases of unsafe desired position and local minima. The control scheme is applied to the avoidance of joint limits while performing visual servoing. Both simulation and experimental results illustrate the validity of our approach

Integrating Features Acceleration in Visual Predictive Control

International audienceThis paper proposes new prediction models for Visual Predictive Control that can lead to both better motions in the feature space and shorter sensor trajectories in 3D. Contrarily to existing first-order models based only on the interaction matrix, it is proposed to integrate acceleration information provided by second-order models. This allows to better estimate the evolution of the image features, and consequently to evaluate control inputs that can properly steer the system to a desired configuration. By means of simulations, the performances of these new predictors are shown and compared to those of a classical model. Included experiments using both image point features and polar coordinates confirm the validity and generality of the approach, showing that the increased complexity of the predictors does not prevent real-time implementations

Improving Relaxation-based Constrained Path Planning via Quadratic Programming

International audienceMany robotics tasks involve a set of constraints that limit the valid configurations the system can assume. Some of these constraints, such as loop-closure or orientation constraints to name some, can be described by a set of implicit functions which cause the valid Configuration Space of the robot to collapse to a lower-dimensional manifold. Sampling-based planners, which have been extensively studied in the last two decades, need some adaptations to work in this context. A proposed approach, known as relaxation, introduces constraint violation tolerances, thus approximating the manifold with a non-zero measure set. The problem can then be solved using classical approaches from the randomized planning literature. The relaxation needs however to be sufficiently high to allow planners to work in a reasonable amount of time, and violations are counterbalanced by controllers during actual motion. We present in this paper a new component for relaxation-based path planning under differentiable constraints. It exploits Quadratic Optimization to simultaneously move towards new samples and keep close to the constraint manifold. By properly guiding the exploration, both running time and constraint violation are substantially reduced

Constrained Path Planning using Quadratic Programming

International audienceSampling-based planning algorithms have been extensively exploited to solve a wide variety of problems. In recent years, many efforts have been dedicated to extend these tools to solve problems involving constraints, such as geometric loop-closure, which lead the valid Configuration Space (CS) to collapse to a lower-dimensional manifold. One proposed solution considers an approximation of the constrained Configuration Space that is obtained by relaxing constraints up to a desired tolerance. The resulting set has then non-zero measure, allowing to exploit classical planning algorithms to search for a path connecting two given states. When constraints involve kinematic loops in the system, relaxation generally bears to undesired contact forces, which need to be compensated during execution by a proper control action. We propose a new tool that exploits relaxation to plan in presence of constraints. Local motions inside the approximated manifold are found as the result of an iterative scheme that uses Quadratic Optimization to proceed towards a new sample without falling outside the relaxed region. By properly guiding the exploration, paths are found with smaller relaxation factors and the need of a dedicated controller to compensate errors is reduced. We complete the analysis by showing the feasibility of the approach with experiments on a real platform

A Comparison of Visual Servoing from Features Velocity and Acceleration Interaction Models

International audienceVisual Servoing has been widely investigated inthe last decades as it provides a powerful strategy for robotcontrol. Thanks to the direct feed-back from a set of sensors,it allows to reduce the impact of some modeling errors and toperform tasks even in uncertain environments. The commonlyexploited approach in this field is to use a model that expressesthe rate of change of a set of features as a function of sensortwist. These schemes are commonly used to obtain a velocitycommand, which needs to be tracked by a low-level controller.Another approach that can be exploited consists in goingone step further and to consider an acceleration model forthe features. This strategy allows also to obtain a naturaland direct link with the dynamic model of the controlledsystem. This study aims at comparing the use of velocity andacceleration-based models in feed-back linearization for VisualServoing. We consider the case of a redundant manipulator anddiscuss what this implies for both control techniques. By meansof simulations, we show that controllers based on featuresacceleration give better results than those based on velocityin presence of noisy feedback signals

Integrating Features Acceleration in Visual Predictive Control

International audienceThis paper proposes new prediction models for Visual Predictive Control that can lead to both better motions in the feature space and shorter sensor trajectories in 3D. Contrarily to existing first-order models based only on the interaction matrix, it is proposed to integrate acceleration information provided by second-order models. This allows to better estimate the evolution of the image features, and consequently to evaluate control inputs that can properly steer the system to a desired configuration. By means of simulations, the performances of these new predictors are shown and compared to those of a classical model. Included experiments using both image point features and polar coordinates confirm the validity and generality of the approach, showing that the increased complexity of the predictors does not prevent real-time implementations

Constrained Path Planning using Quadratic Programming

International audienceSampling-based planning algorithms have been extensively exploited to solve a wide variety of problems. In recent years, many efforts have been dedicated to extend these tools to solve problems involving constraints, such as geometric loop-closure, which lead the valid Configuration Space (CS) to collapse to a lower-dimensional manifold. One proposed solution considers an approximation of the constrained Configuration Space that is obtained by relaxing constraints up to a desired tolerance. The resulting set has then non-zero measure, allowing to exploit classical planning algorithms to search for a path connecting two given states. When constraints involve kinematic loops in the system, relaxation generally bears to undesired contact forces, which need to be compensated during execution by a proper control action. We propose a new tool that exploits relaxation to plan in presence of constraints. Local motions inside the approximated manifold are found as the result of an iterative scheme that uses Quadratic Optimization to proceed towards a new sample without falling outside the relaxed region. By properly guiding the exploration, paths are found with smaller relaxation factors and the need of a dedicated controller to compensate errors is reduced. We complete the analysis by showing the feasibility of the approach with experiments on a real platform