Navigation Planning with Human-Like Approach

Abstract. In recent years, Arctic glaciers have gradually melted due to the global warming, which makes the exploitation of Arctic and its seabed resources possible. Though numerous disagreements and potentials over Arctic maritime jurisdiction still exist, the surround-Arctic nations have agreed the United Nations' Convention on the Law of the Sea to divide the Arctic Ocean into zones that can be regulated and exploited. The IBRU of Durham University has mapped the known claims, agreed boundaries and potential claims of the surround-Arctic nations in the Arctic to clear the maritime jurisdiction in the region. However, different countries may have different requirements within their jurisdictional areas. Clarifying these requirements is essential for Arctic Navigation of investigation ships and merchant ships for their route planning.In this paper, based on the map of maritime jurisdiction and boundaries in Arctic region (IBRU), we analysed the international conventions and relevant laws of the surround-Arctic nations to find out the rights and obligations of ships in different zones. The limitations on activities and recommendations on navigation planning are marked for different zones according to different purposes, i.e. science or commerce. The map could not only provide navigational guidance for the activities in the Arctic Ocean, but offer references for the countries not surrounding the Arctic in the formulation of the Arctic strategies.

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Robot navigation algorithms using learned spatial graphs

Robotica ◽

10.1017/s0263574700008559 ◽

1986 ◽

Vol 4 (2) ◽

pp. 93-100 ◽

Cited By ~ 54

Author(s):

S. S. Iyengar ◽

C. C. Jorgensen ◽

S. V. N. Rao ◽

C. R. Weisbin

Keyword(s):

Robot Navigation ◽

Sensor Data ◽

Two Dimensional ◽

Continuous Transition ◽

Spatial Graph ◽

Spatial Graphs ◽

Navigation Algorithms ◽

Sensor Information ◽

Navigation Planning ◽

Available Information

SUMMARYFinding optimal paths for robot navigation in a known terrain has been studied for some time but, in many important situations, a robot would be required to navigate in completely new or partially explored terrain. We propose a method of robot navigation which requires no pre-learned model, makes maximal use of available information, records and synthesizes information from multiple journeys, and contains concepts of learning that allow for continuous transition from local to global path optimality. The model of the terrain consists of a spatial graph and a Voronoi diagram. Using acquired sensor data, polygonal boundaries containing perceived obstacles shrink to approximate the actual obstacles surfaces, free space for transit is correspondingly enlarged, and additional nodes and edges are recorded based on path intersections and stop points. Navigation planning is gradually accelerated with experience since improved global map information minimizes the need for further sensor data acquisition. Our method currently assumes obstacle locations are unchanging, navigation can be successfully conducted using two-dimensional projections, and sensor information is precise.

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