다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

Mobile robots often operate in domains that are only incompletely known, for example, when they have to move from given start coordinates to given goal coordinates in unknown terrain. In this case, they need to be able to replan quickly as their knowledge of the terrain changes. Stentz' Focussed Dynamic A/sup */ (D/sup */) is a heuristic search method that repeatedly determines a shortest path from the current robot coordinates to the goal coordinates while the robot moves along the path. It is able to replan faster than planning from scratch since it modifies its previous search results locally. Consequently, it has been extensively used in mobile robotics. In this article, we introduce an alternative to D/sup */ that determines the same paths and thus moves the robot in the same way but is algorithmically different. D/sup */ Lite is simple, can be rigorously analyzed, extendible in multiple ways, and is at least as efficient as D/sup */. We believe that our results will make D/sup */-like replanning methods even more popular and enable robotics researchers to adapt them to additional applications.

Fast replanning for navigation in unknown terrain

We present a graph-based planning and replanning algorithm able to produce bounded suboptimal solutions in an anytime fashion. Our algorithm tunes the quality of its solution based on available search time, at every step reusing previous search efforts. When updated information regarding the underlying graph is received, the algorithm incrementally repairs its previous solution. The result is an approach that combines the benefits of anytime and incremental planners to provide efficient solutions to complex, dynamic search problems. We present theoretical analysis of the algorithm, experimental results on a simulated robot kinematic arm, and two current applications in dynamic path planning for outdoor mobile robots.

/pdf/anytime-dynamic-a-an-anytime-replanning-algorithm-3c2kt7cp9q.pdf

Anytime dynamic A*: an anytime, replanning algorithm

Incremental heuristic search methods use heuristics to focus their search and reuse information from previous searches to find solutions to series of similar search tasks much faster than is possible by solving each search task from scratch. In this paper, we apply Lifelong Planning A* to robot navigation in unknown terrain, including goal-directed navigation in unknown terrain and mapping of unknown terrain. The resulting D* Lite algorithm is easy to understand and analyze. It implements the same behavior as Stentz' Focussed Dynamic A* but is algorithmically different. We prove properties about D* Lite and demonstrate experimentally the advantages of combining incremental and heuristic search for the applications studied. We believe that these results provide a strong foundation for further research on fast replanning methods in artificial intelligence and robotics.

D*lite

Autonomous navigation in cross-country environments presents many new challenges with respect to more traditional, urban environments. The lack of highly structured components in the scene complicates the design of even basic functionalities such as obstacle detection. In addition to the geometric description of the scene, terrain typing is also an important component of the perceptual system. Recognizing the different classes of terrain and obstacles enables the path planner to choose the most efficient route toward the desired goal.

This paper presents new sensor processing algorithms that are suitable for cross-country autonomous navigation. We consider two sensor systems that complement each other in an ideal sensor suite: a color stereo camera, and a single axis ladar. We propose an obstacle detection technique, based on stereo range measurements, that does not rely on typical structural assumption on the scene (such as the presence of a visible ground plane)s a color-based classification system to label the detected obstacles according to a set of terrain classess and an algorithm for the analysis of ladar data that allows one to discriminate between grass and obstacles (such as tree trunks or rocks), even when such obstacles are partially hidden in the grass. These algorithms have been developed and implemented by the Jet Propulsion Laboratory (JPL) as part of its involvement in a number of projects sponsored by the US Department of Defense, and have enabled safe autonomous navigation in high-vegetated, off-road terrain.

/pdf/obstacle-detection-and-terrain-classification-for-autonomous-56lyfce1fy.pdf

Obstacle Detection and Terrain Classification for Autonomous Off-Road Navigation

The Demo III program has as its primary focus the development of autonomous mobility for a small rugged cross country vehicle. Enabling vision based terrain perception technology for classification of scene geometry and material is currently under development at JPL. In this paper we report recent progress on both stereo-based obstacle detection and terrain cover color-based classification. Our experiments show that the integration of geometric description and terrain cover characterization may be the key to enabling successful autonomous navigation in cross-country vegetated terrain.

https://www-robotics.jpl.nasa.gov/publications/Arturo_Rankin/IVS00.pdf

Terrain perception for DEMO III

https://www-robotics.jpl.nasa.gov/publications/Arturo_Rankin/UrbanRobotPaper0700.pdf

A portable, autonomous, urban reconnaissance robot

Obstacle detection (OD) is one of the main components of the control system of autonomous vehicles. In the case of indoor/urban navigation, obstacles are typically defined as surface points that are higher than the ground plane, but in cross-country and unstructured environments the notion of "ground plane" is often not meaningful. We introduce a fast, fully 3D OD technique that overcomes such a problem, reducing the risk of false-negatives while keeping the same rate of false-positives. A simple addition to our algorithm allows one to segment obstacle points into clusters, where each cluster identifies an isolated obstacle in 3D space. Obstacle segmentation corresponds to finding the connected components of a suitable graph, an operation that can be performed at a minimal additional cost during the computation of obstacle points. Rule-based classification using 3D geometrical measures derived for each segmented obstacle is then used to reject false-obstacles (for example, objects that are small in volume, or of low height). Results for a number of scenes of natural terrain are presented, and compared with a pre-existing obstacle detection algorithm.

https://www-robotics.jpl.nasa.gov/publications/Arturo_Rankin/ATalukder-IEEEIV-2002.pdf

Fast and reliable obstacle detection and segmentation for cross-country navigation

In January 2004, NASA's twin Mars exploration rovers (MERs), spirit and opportunity, began searching the surface of Mars for evidence of past water activity. In order to localize and approach scientifically interesting targets, the rovers employ an on-board navigation system. Given the latency in sending commands from Earth to the Martian rovers (and in receiving return data), a high level of navigational autonomy is desirable. Autonomous navigation with hazard avoidance (AutoNav) is currently performed using a local path planner called GESTALT (grid-based estimation of surface traversability applied to local terrain). GESTALT uses stereo cameras to evaluate terrain safety and avoid obstacles. GESTALT works well to guide the rovers around narrow and isolated hazards, however, it is susceptible to failure when clusters of closely spaced, non-traversable rocks form extended obstacles. In May 2005, a new technology task was initiated at the Jet Propulsion Laboratory to address this limitation. A version of the Carnegie Mellon University Field D* global path planner has been integrated into MER flight software, enabling simultaneous local and global planning during AutoNav. A revised version of AutoNav was uploaded to the rovers during the summer of 2006. This paper describes how global planning was integrated into the MER flight software, and presents results of testing the improved AutoNav system using the MER Surface System TestBed rover.

/pdf/global-path-planning-on-board-the-mars-exploration-rovers-t0lp1gx6i4.pdf

Global Path Planning on Board the Mars Exploration Rovers

Detecting water hazards is a significant challenge to unmanned ground vehicle autonomous off-road navigation. This paper focuses on detecting the presence of water during the daytime using color cameras. A multi-cue approach is taken. Evidence of the presence of water is generated from color, texture, and the detection of reflections in stereo range data. A rule base for fusing water cues was developed by evaluating detection results from an extensive archive of data collection imagery containing water. This software has been implemented into a run-time passive perception subsystem and tested thus far under Linux on a Pentium based processor.

Arturo L. Rankin

Papers

Terrain perception for DEMO III

A portable, autonomous, urban reconnaissance robot

Fast and reliable obstacle detection and segmentation for cross-country navigation

Global Path Planning on Board the Mars Exploration Rovers

Daytime Water Detection by Fusing Multiple Cues for Autonomous Off-Road Navigation