This paper presents a mobile robot navigation control system based on integration of laser SLAM localization and real-time obstacle avoidance control to provide personnel guidance for daily-life services. The LiDAR SLAM localization system is implemented in a ROS software architecture, in which Cartographer SLAM is adopted and the adaptive Monte Carlo localization is employed onboard the robot. An integrated guidance system is proposed in this paper to combine obstacle avoidance and SLAM so that the robot can move to the desired location without colliding with any unexpected obstacles. A safety-weight parameter is used to integrate goal seeking controller with the obstacle avoidance controller. The experimental results show that the robot can localize itself and navigate to the target location while avoiding obstacles on the path.

Summary: Large-scale phosphoproteomics studies have improved our understanding of dynamic cellular signaling, but the downstream analysis of phosphoproteomics data is still a bottleneck. We develop DynaPho, a useful web-based tool providing comprehensive and in-depth analyses of time-course phosphoproteomics data, making analysis intuitive and accessible to non-bioinformatics experts. The tool currently implements five analytic modules, which reveal the transition of biological pathways, kinase activity, dynamics of interaction networks and the predicted kinase-substrate associations. These features can assist users in translating their larger-scale time-course phosphoproteomics data into valuable biological discoveries. Availability and implementation: DynaPho is freely available at http://dynapho.jhlab.tw/ . Contact: [email protected] or [email protected] . Supplementary information: Supplementary data are available at Bioinformatics online.