Automatic Configuration of Multi-Agent Model Predictive Controllers based on Semantic Graph World Models

We propose a shared semantic map architecture to construct and configure Model Predictive Controllers (MPC) dynamically, that solve navigation problems for multiple robotic agents sharing parts of the same environment. The navigation task is represented as a sequence of semantically labeled areas in the map, that must be traversed sequentially, i.e. a route. Each semantic label represents one or more constraints on the robots' motion behaviour in that area. The advantages of this approach are: (i) an MPC-based motion controller in each individual robot can be (re-)configured, at runtime, with the locally and temporally relevant parameters; (ii) the application can influence, also at runtime, the navigation behaviour of the robots, just by adapting the semantic labels; and (iii) the robots can reason about their need for coordination, through analyzing over which horizon in time and space their routes overlap. The paper provides simulations of various representative situations, showing that the approach of runtime configuration of the MPC drastically decreases computation time, while retaining task execution performance similar to an approach in which each robot always includes all other robots in its MPC computations

Automatic Configuration of Multi-Agent Model Predictive Controllers based on Semantic Graph World Models

We propose a shared semantic map architecture to construct and configure Model Predictive Controllers (MPC) dynamically, that solve navigation problems for multiple robotic agents sharing parts of the same environment. The navigation task is represented as a sequence of semantically labeled areas in the map, that must be traversed sequentially, i.e. a route. Each semantic label represents one or more constraints on the robots' motion behaviour in that area. The advantages of this approach are: (i) an MPC-based motion controller in each individual robot can be (re-)configured, at runtime, with the locally and temporally relevant parameters; (ii) the application can influence, also at runtime, the navigation behaviour of the robots, just by adapting the semantic labels; and (iii) the robots can reason about their need for coordination, through analyzing over which horizon in time and space their routes overlap. The paper provides simulations of various representative situations, showing that the approach of runtime configuration of the MPC drastically decreases computation time, while retaining task execution performance similar to an approach in which each robot always includes all other robots in its MPC computations

Tolerability of breast ductal lavage in women from families at high genetic risk of breast cancer

<p>Abstract</p> <p>Background</p> <p>Ductal lavage (DL) has been proposed as a minimally-invasive, well-tolerated tool for obtaining breast epithelial cells for cytological evaluation of breast cancer risk. We report DL tolerability in <it>BRCA1/2 </it>mutation-positive and -negative women from an IRB-approved research study.</p> <p>Methods</p> <p>165 <it>BRCA1/2 </it>mutation-positive, 26 mutation-negative and 3 mutation unknown women underwent mammography, breast MRI and DL. Psychological well-being and perceptions of pain were obtained before and after DL, and compared with pain experienced during other screening procedures.</p> <p>Results</p> <p>The average <b><it>anticipated </it></b>and <b><it>experienced </it></b>discomfort rating for DL, 47 and 48 (0–100), were significantly higher (<it>p </it>< 0.01) than the <b><it>anticipated </it></b>and <b><it>experienced </it></b>discomfort of mammogram (38 and 34), MRI (36 and 25) or nipple aspiration (42 and 27). Women with greater pre-existing emotional distress experienced more DL-related discomfort than they anticipated. Women reporting DL-related pain as worse than expected were nearly three times more likely to refuse subsequent DL than those reporting it as the same or better than expected. Twenty-five percent of participants refused repeat DL at first annual follow-up.</p> <p>Conclusion</p> <p>DL was anticipated to be and experienced as <b>more </b>uncomfortable than other procedures used in breast cancer screening. Higher underlying psychological distress was associated with decreased DL tolerability.</p

Tracking contact transitions during force-controlled compliant motion using an interacting multiple model estimator

This work concerns both monitoring of contact transitions and estimation of the unknown first-order geometric parameters during force-controlled motions. The robotic system is required to move an object among a sequence of contact configurations with the environment, under partial knowledge of geometric parameters (positions and orientations) of the manipulated objects and of the environment itself. An example of a compliant motion task with multiple contacts is considered, that of moving a cube into a corner. It is shown that by describing the contact configurations with different models, and by using the multiple model approach it is possible: i) to detect effectively at each moment the current contact configuration and ii) to estimate accurately the unknown parameters. The reciprocity constraints between ideal reaction forces and velocities are used as measurement equations. An Interacting Multiple Model (IMM) estimator is implemented and its performance is evaluated based on experimental data

Contact Transitions Tracking During Force-Controlled Compliant Motion Using an Interacting Multiple Model Estimator

This paper proposes a multiple model approach for detection and estimation of contact transitions in forced controlled robot