Bi-objective path planning using deterministic algorithms

Path planning has become a central problem in motion planning. The classic version of the problem aims to find an obstacle-free path with the minimum length for a given workspace containing a set of obstacles and two sources and destination points. However, some real world applications consider maxi...

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Vydané v:Robotics and autonomous systems Ročník 93; s. 105 - 115
Hlavný autor: Davoodi, Mansoor
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier B.V 01.07.2017
Predmet:
Approximation
Bi-objective optimization
Clearance
Pareto optimal
Path planning
Bi-objective optimization
Path planning
Approximation
Clearance
Pareto optimal
ISSN:0921-8890, 1872-793X
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Popis
Shrnutí:Path planning has become a central problem in motion planning. The classic version of the problem aims to find an obstacle-free path with the minimum length for a given workspace containing a set of obstacles and two sources and destination points. However, some real world applications consider maximizing the path clearance (i.e., the distance between the robot and obstacles) as the secondary objective. This bi-objective path planning problem has been studied using evolutionary and other heuristic algorithms which do not guarantee achieving Pareto optimal paths. In this paper, we first study this problem using deterministic algorithms. Next, we propose an efficient algorithm for the problem in the grid workspace. We then propose an O(n3) time algorithm for the problem under the Manhattan distance in a continuous workspace containing n vertical segments as obstacles. Finally, we show the obtained solutions are proper approximation for the problem under the Euclidean distance. •Introducing a bi-objective length and clearance path planning (bi-PP) model.•Proposing an algorithm for solving the bi-PP in grid and finding Pareto fronts.•Proposing an algorithm for solving the bi-PP in continuous Manhattan space.•Approximating Pareto solutions of the bi-PP under the Euclidean distance.
ISSN:0921-8890
1872-793X
DOI:10.1016/j.robot.2017.03.021