Modeling of ship trajectory in collision situations by an evolutionary algorithm

Śmierzchalski, Roman; Michalewicz, Zbigniew

doi:10.1109/4235.873234

Cited by 164 publications

(100 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Tests of the modification version of algorithm which changes the own ship's speed along the trajectory sections were discussed in previous works [13,14,15]. This article is focused on the discussion of experiments of on-line algorithm version.…”

Section: T Otal Cost(s) = Saf E Cond(s) + Econ Cond(s)mentioning

confidence: 99%

“…This demonstrates the correctness of operation of the on-line program version. It should be stressed here that two most complex environments were selected for the present tests, out of all environments earlier studied by the authors (see [13,14,15]). …”

Section: On-line Path Planingmentioning

confidence: 99%

“…In this work, we report on experiments with evolutionary system, which takes into consideration the motion of other ships; see also [12,14,15]. …”

Section: Previous Workmentioning

confidence: 99%

“…The analysis of examples where the speed of the own ship was initially assumed constant clearly shows the need for making this parameter variable along particular trajectory sections. In practice the speed is modified using an additional genetic operator: the speed mutation [13,15]. A set of permissible speed values was defined as ϑ=3.6, 8.6, and 13.6 knots; the mutation operator can select from this set an appropriate speed for any trajectory section under consideration.…”

Section: T Otal Cost(s) = Saf E Cond(s) + Econ Cond(s)mentioning

confidence: 99%

See 3 more Smart Citations

Path Planning in Dynamic Environments

Śmierzchalski

Michalewicz

2005

Innovations in Robot Mobility and Control

View full text Add to dashboard Cite

Abstract. The motion planning problem for mobile robots is typically formulated as follows: given a robot and a description of an environment, plan a path of the robot between two specified locations, which is collision-free and satisfies certain optimization criteria. Traditionally there are two approaches to the problem: Off-line planning, which assumes perfectly known and stable environment, and on-line planning, which focuses on dealing with uncertainties when the robot traverses the environment. On-line planning is also referred to by many researchers as the navigation problem. Additional difficulties in approaching navigation problem is that some environments are dynamic, i.e., the obstacles which are present there, need not be static. In this paper we consider a particular instance of a navigation problem, namely, a problem of computing a near-optimum trajectory of a ship. By taking into account certain boundaries of the maneuvering region, along with navigation obstacles and other moving ships, the problem of avoiding collisions at sea was reduced to a dynamic optimization task with static and dynamic constrains. The paper presents a modified version of the Evolutionary Planner/Navigator algorithm, ϑEP/N++, to address the problem. The introduction of a time parameter, the variable speed of the ship, and time-varying constraints representing movable ships, are the main features of the new system. Sample results, having the form of ship trajectories obtained using the program for navigation situations are also presented.

show abstract

Section: T Otal Cost(s) = Saf E Cond(s) + Econ Cond(s)mentioning

confidence: 99%

Section: On-line Path Planingmentioning

confidence: 99%

“…In this work, we report on experiments with evolutionary system, which takes into consideration the motion of other ships; see also [12,14,15]. …”

Section: Previous Workmentioning

confidence: 99%

Section: T Otal Cost(s) = Saf E Cond(s) + Econ Cond(s)mentioning

confidence: 99%

See 2 more Smart Citations

Path Planning in Dynamic Environments

Śmierzchalski

Michalewicz

2005

Innovations in Robot Mobility and Control

View full text Add to dashboard Cite

show abstract

“…is adapted from Smierzchalski and Michalewicz (2000), d min is the desirable DCPA of the ASV, DCPA i (x) is obtained from the risk assessment submodule on the i th target vessel posing collision risk, and a is a constant scaling parameter. (2) Path smoothness: this objective function will minimise or prevent any abrupt changes to the modified path.…”

Section: The Framework For Path Re-planningmentioning

confidence: 99%

COLREGs-Compliant Path Planning for Autonomous Surface Vehicles: A Multiobjective Optimization Approach

Hu¹,

Naeem²,

Rajabally

et al. 2017

IFAC-PapersOnLine

View full text Add to dashboard Cite

In this paper, a multiobjective optimization framework is proposed for on-line path planning of autonomous surface vehicles (ASVs), where both collision avoidance and COLREGscompliance are taken into account. Special attention has been paid to situational awareness and risk assessment, particularly when the target ship is in breach of the COLREGs rules defined by the International Maritime Organisation. In order to implement COLREGs, the rules together with physical constraints are formulated as mathematical inequalities. A multiobjective optimization problem based on particle swarm optimization is then solved, the solution of which represents a newly-generated path. It is shown through simulations that the proposed method is able to generate COLREGs-compliant and collision-free paths even for non-cooperative targets i.e. vessels that are in breach of COLREGs.

show abstract