000007731 001__ 7731
000007731 005__ 20240626123056.0
000007731 02470 $$2doi$$a10.24868/issn.2631-8741.2018.018
000007731 035__ $$a2536957
000007731 037__ $$aGENERAL
000007731 245__ $$aA random sampling based algorithm for ship path planning with obstacles
000007731 269__ $$a2018-10-03
000007731 336__ $$aConference Proceedings
000007731 520__ $$aThe paper presents a path planning algorithm for ship guidance in presence of obstacles, based on an ad hoc modified version of the Rapidly-exploring Random Tree (RRT*) algorithm. The proposed approach is designed to be part of a decision support system for the bridge operators, in order to enhance traditional navigation. Focusing on the maritime field, a review of the scientific literature dealing with motion planning is presented, showing potential benefits and weaknesses of the different approaches. Among the several methods, details on RRT and RRT* algorithms are given. The ship path planning problem is introduced and discussed, formulating suitable cost functions and taking into account both topological and kinematic constraints. Eventually, an existing time domain ship simulator is used to test the effectiveness of the proposed algorithm over a number of realistic operation scenarios. The obtained results are presented and critically discussed. 
000007731 542__ $$fCC-BY-NC-ND-4.0
000007731 6531_ $$aCollision Avoidance
000007731 6531_ $$aShip Simulator
000007731 6531_ $$aPath Planning
000007731 6531_ $$aRRT
000007731 7001_ $$aZaccone, R$$uDept. of Electrical, Electronic, Telecommunications, Naval Architecture and Marine Engineering (DITEN), Polytechnic School of Genoa University, Genova, Italy.
000007731 7001_ $$aMartelli, M$$uDept. of Electrical, Electronic, Telecommunications, Naval Architecture and Marine Engineering (DITEN), Polytechnic School of Genoa University, Genova, Italy.
000007731 773__ $$tConference Proceedings of iSCSS
000007731 773__ $$jiSCSS 2018
000007731 789__ $$whttps://zenodo.org/record/2536957$$2URL$$eIsIdenticalTo
000007731 85641 $$uhttps://www.imarest.org/iscss$$yConference website
000007731 8564_ $$9123b6efe-7bfc-445c-b0a7-1df2228bf4f8$$s1715449$$uhttps://library.imarest.org/record/7731/files/ISCSS%202018%20Paper%20067%20Martelli%20SDG%20FINAL.pdf