Solving the Path Planning Problem in Mobile Robotics with the Multi-Objective Evolutionary Algorithm
Path planning problems involve finding a feasible path from the starting point to the target point. In mobile robotics, path planning (PP) is one of the most researched subjects at present. Since the path planning problem is an NP-hard problem, it can be solved by multi-objective evolutionary algori...
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| Published in | Applied sciences Vol. 8; no. 9; p. 1425 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
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MDPI AG
01.09.2018
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| Online Access | Get full text |
| ISSN | 2076-3417 2076-3417 |
| DOI | 10.3390/app8091425 |
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| Abstract | Path planning problems involve finding a feasible path from the starting point to the target point. In mobile robotics, path planning (PP) is one of the most researched subjects at present. Since the path planning problem is an NP-hard problem, it can be solved by multi-objective evolutionary algorithms (MOEAs). In this article, we propose a multi-objective method for solving the path planning problem. It is a population evolutionary algorithm and solves three different objectives (path length, safety, and smoothness) to acquire precise and effective solutions. In addition, five scenarios and another existing method are used to test the proposed algorithm. The results show the advantages of the algorithm. In particular, different quality metrics are used to assess the obtained results. In the end, the research indicates that the proposed multi-objective evolutionary algorithm is a good choice for solving the path planning problem. |
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| AbstractList | Path planning problems involve finding a feasible path from the starting point to the target point. In mobile robotics, path planning (PP) is one of the most researched subjects at present. Since the path planning problem is an NP-hard problem, it can be solved by multi-objective evolutionary algorithms (MOEAs). In this article, we propose a multi-objective method for solving the path planning problem. It is a population evolutionary algorithm and solves three different objectives (path length, safety, and smoothness) to acquire precise and effective solutions. In addition, five scenarios and another existing method are used to test the proposed algorithm. The results show the advantages of the algorithm. In particular, different quality metrics are used to assess the obtained results. In the end, the research indicates that the proposed multi-objective evolutionary algorithm is a good choice for solving the path planning problem. The energy loss of a mobile robot is related to the length of the path—the longer the path, the greater the loss of energy. Besides the path length, the energy loss is also related to the smoothness of the path. MinDis(pi pi+1,Oj) is the minimum distance between segment pi pi+1 and obstacle Oj . [...]Min0≤i≤nMin1≤j≤m{MinDis(pi pi+1,Oj)} is the shortest distance between the obstacles and the path—the larger the value, the safer the path. .×[aid ,rd]. [...]the dominant proportions of two groups of non-dominated solutions can be measured by the set coverage metric (scm). [...]scm(A,B) can be shown as scm(A,B)={b∈B|∃a∈A:a⪯b}B. If scm(A,B)=1 , any solution in A is better than the solution in B. On the contrary, if scm(A,B)=0 , this implies that any solution in B is better than the solution in A. Since relationship (⪯) denotes a weak dominance relation and is not symmetric, scm(A,B) and scm(B,A) need to be calculated. |
| Author | Sun, Jian-Qiao Xue, Yang |
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| Snippet | Path planning problems involve finding a feasible path from the starting point to the target point. In mobile robotics, path planning (PP) is one of the most... The energy loss of a mobile robot is related to the length of the path—the longer the path, the greater the loss of energy. Besides the path length, the energy... |
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| SubjectTerms | Elitism Genetic algorithms Geometry Heuristic Methods multi-objective evolutionary algorithm multi-objective optimization Neural networks Objectives Optimization algorithms path planning Product design refiner operators robotics Robots |
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| Title | Solving the Path Planning Problem in Mobile Robotics with the Multi-Objective Evolutionary Algorithm |
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