Bin Xu

Learn More
We propose an efficient method for updating a path that was computed using level-set methods. Our approach is suitable for autonomous vehicles navigating in a static environment for which an a priori map of the environment is inaccurate. When the autonomous vehicle detects a new obstacle, our algorithm replans an optimal route without recomputing the entire(More)
For an autonomous vehicle navigating in a static environment for which an a priori map is inaccurate, we propose a hybrid receding horizon control method to determine optimal routes when new obstacles are detected. The hybrid method uses the level sets of the solution to either a global or local Eikonal equation in the formulation of the receding horizon(More)
Towards the goal of achieving truly autonomous navigation for a surface vehicle in maritime environments, a critical task is to detect surrounding obstacles such as the shore, docks, and other boats. In this paper, we demonstrate a real-time vision-based mapping system which detects and localizes stationary obstacles using a single omnidirectional camera(More)
We address the minimal risk motion planning problem in a two dimensional environment in the presence of both moving and static obstacles. Our approach is inspired by recent results due to Vladimirsky [20] in which path planning on time-varying maps is addressed using a new level-set approach, and for which computational costs are remarkably low. Toward(More)
Autonomous operation by a surface vehicle in a maritime setting requires that the surface vehicle detects non-water objects, including shoreline, hazards to navigation, and other moving vessels. In order to assess the utility of an omnidirectional camera for detecting and localizing non- water objects, we rigorously investigate observability of both(More)
We investigate path planning algorithms that are based on level set methods for applications in which the environment is static, but where an a priori map is inaccurate and the environment is sensed in real-time. Our principal contribution is not a new path planning algorithm, but rather a formal analysis of path planning algorithms based on level set(More)
The mining methods for comment text polarity are usually used to adopted supervised learning algorithms, but supervised learning algorithms require significant manual labor marked the training set, and its text set in dealing with will be also faced with dimension disaster, sparse vector, high spatial and temporal complexity, low recall and precision rates(More)
This dissertation addresses path planning for an autonomous vehicle navigating in a two dimensional environment for which an a priori map is inaccurate and for which the environment is sensed in real-time. For this class of application, planning decisions must be made in real-time. This work is motivated by the need for fast autonomous vehicles that require(More)
  • 1