Multi-objective path planning in discrete space

Davoodi, Mansoor; Panahi, Farhad; Mohades, Ali; Hashemi, Seyed Naser

doi:10.1016/j.asoc.2012.07.023

Cited by 83 publications

(38 citation statements)

References 10 publications

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“…In particular, the algorithm optimizes the path length and the path curvature. In Ahmed and Deb (2011), Chang and Liu (2009), and Davoodi et al (2013), different variants of NSGA-II were proposed, which also use two objectives: the path length and the path safety (referred to the obstacles), but ignoring the energy consumption. Other works in which the authors used these same objectives, also without taking into account the energy consumption, can be found in Gong et al (2011), Geng et al (2013, Wang et al (2009), andZhang et al (2013).…”

Section: Related Workmentioning

confidence: 99%

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Solving the multi-objective path planning problem in mobile robotics with a firefly-based approach

et al. 2015

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Currently, autonomous robotics is one of the most interesting and researched areas of technology. At the beginning, robots only worked in the industrial sector but, gradually, they started to be introduced into other sectors such as medicine or social environments becoming part of society. In mobile robots, the path planning (PP) problem is one of the most researched topics. Taking into account that the PP problem is an NP-hard problem, multi-objective evolutionary algorithms (MOEAs) are good candidates to solve this problem. In this work, a new multi-objective approach based on the flashing behavior of fireflies in nature, the multi-objective firefly algorithm (MO-FA), is proposed to solve the PP problem. This proposed algorithm is a swarm intelligence algorithm. The proposed MO-FA handles three different objectives to obtain accurate and efficient solutions. These objectives are the following: the path safety, the path length, and the path smoothness (related to the energy conCommunicated by sumption). Furthermore, and to test the proposed MOEA, we have used eight realistic scenarios for the path's calculation. On the other hand, we also compare our proposal with other approaches of the state of the art, showing the advantages of MO-FA. In particular, to evaluate the obtained results we applied specific quality metrics. Moreover, to demonstrate the statistical evidence of the obtained results, we also performed a statistical analysis. Finally, the study shows that the proposed MO-FA is a good alternative to solve the PP problem.

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Section: Related Workmentioning

confidence: 99%

“…Path planning (PP) is an NP-hard optimization problem (Davoodi et al 2013); for this reason it can be tackled by using MOEAs.…”

Section: Introductionmentioning

confidence: 99%

Solving the multi-objective path planning problem in mobile robotics with a firefly-based approach

et al. 2015

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“…The main evolutionary loop acts on a population of variable-length chromosomes, each one encoding a potential trajectory. More precisely, a chromosomal string includes the float Cartesian coordinates of the inner vertices, stated as decision variables according to (1). The GA is supplemented with all the mechanisms required for handling variable-length chromosomes: i) the construction of the initial paths with different number of turning points (N < N max ) according to a uniform distribution of the vertices within WS; ii) a crossover operator which extends the shorter parent [12] before mating, without altering its phenotype (by duplicating some randomly selected inner vertices).…”

Section: B Overview Of the Moo Gamentioning

confidence: 99%

“…Otherwise, if the vertices of the trajectories are not preset, path planning should be also able to generate their locations. To overcome this impediment, one can artificially generate a set of vertices [1] (e.g. according to a grid from which the vertices found in the forbidden areas are deleted) or the path planning algorithm could be enabled to generate the vertices in continuous space, by itself.…”

Section: Introductionmentioning

confidence: 99%

Pareto genetic path planning hybridized with multi-objective Dijkstra's algorithm

Ferariu

Cimpanu

2014

2014 18th International Conference on System Theory, Control and Computing (ICSTCC)

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This paper presents an evolutionary approach to multi-objective path planning. The paths are defined on continuous scenes with disjoint and/or non-convex obstacles, for robots moving towards their destinations along linearly piecewise trajectories with any number of vertices. The fastest feasible route is genetically selected via a simultaneous minimization of path length and path steering angle. In order to assure an effective partial sorting of the potential solutions, the genetic algorithm makes use of a self-adaptive Pareto ranking scheme, based on individuals' grouping and dominance analysis. Before ranking, all the unfeasible solutions are corrected, by replacing the sub-paths which intersect the obstacles. The feasible segments are chosen from graphs generated with Delaunay triangulation, by applying a new multi-objective ranking-based fastest path procedure derived from the classical Dijsktra's algorithm. This new procedure is compatible with any nonlinear objective functions and allows using the same ranking scheme during the evolutionary search and correction of paths, thus making possible to guarantee the feasibility of the paths without any significant intrusion in the evolutionary exploration. The proposed algorithm can also be used for solving any graphbased path planning problem, involving any number of objectives. The experiments show the usefulness of the suggested techniques on working scenes with different layouts of obstacles.

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“…Many of them use genetic algorithms (GA) [17][18][19][20]. Other proposals apply estimation of distribution algorithms [21] or * searching [22].…”

Section: Introductionmentioning

confidence: 99%

Intelligent UAV Map Generation and Discrete Path Planning for Search and Rescue Operations

2018

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Search and Rescue Operations (SAR) take place in any emergency situation where people are involved and their lives are at risk. These operations are nowadays carried out with the help of advanced technology, such as Unmanned Aerial Vehicles (UAVs). In this work, several methods are proposed to calculate the UAV discrete path planning. Previously, an intelligent characterization of the searching area is performed to estimate a potential risk/occupancy degree of the gridding map. This estimation is mainly based on fuzzy logic, considering different factors. Then, four methods are applied to calculate the path planning: an original proposal called attraction, fuzzy logic, ANFIS, and a PSO algorithm. All of them calculate the location of the waypoints to be followed by the UAVs to minimize the distance and the risk the people is exposed to. Then, these strategies are adapted to the possibility of having more than one UAV searching at the same time, and the swarm formation is discussed. Finally, these four solutions for path planning, including different number of UAVs, are tested in a real simulation scenario, and then the performance of each method is analyzed and compared with the others.

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Multi-objective path planning in discrete space

Cited by 83 publications

References 10 publications

Solving the multi-objective path planning problem in mobile robotics with a firefly-based approach

Solving the multi-objective path planning problem in mobile robotics with a firefly-based approach

Pareto genetic path planning hybridized with multi-objective Dijkstra's algorithm

Intelligent UAV Map Generation and Discrete Path Planning for Search and Rescue Operations

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