Robot path planning in an environment with many terrains based on interval multi-objective PSO

Geng, Nan; Gong, Dunwei; Zhang, Yong

doi:10.1109/cec.2013.6557652

Cited by 8 publications

(6 citation statements)

References 14 publications

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“…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). On the other hand, Sedaghat also applied NSGA-II and proposed a variant of these two previous objectives, in particular she used the path length in combination with the path difficulty to solve the problem in Sedaghat (2011), also ignoring the energy consumption.…”

Section: Related Workmentioning

confidence: 96%

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: 96%

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

et al. 2015

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“…The original MOSOS algorithm works on the cooperative behavior seen among organisms in nature. During the search process, each organism benefits from continuous interaction with others in three different phases [24]: mutualism, commensalism and parasitism. Mutualism allows the two sides to benefit from each other; commensalism benefits one party, while the other party is not affected; parasitism benefits one party, and the other party suffers.…”

Section: Modified Sos For Mtwdtspmentioning

confidence: 99%

“…Group intelligent optimization methods (such as Bayesian approach [15]) are employed to solve the problem with independent task points in a static environment within limited space, which can reduce the complexity of modeling and calculation. In recent years, heuristic and bio-inspired algorithms based group intelligent optimization methods have been adopted to solve the multi-objective optimization problem, including the simulated annealing algorithm [16], the Tabu search algorithm [17], genetic algorithms (NSGA-II) [18,19], the artificial fish school algorithm [20], ant colony algorithm [21] and the particle swarm optimization (niching PSO) [22,23,24,25]. These algorithms perform better than most traditional mathematical techniques in solving these problems, because they do not require substantial gradient information.…”

Section: Introductionmentioning

confidence: 99%

Multi-UAV Reconnaissance Task Assignment for Heterogeneous Targets Based on Modified Symbiotic Organisms Search Algorithm

Chen

Nan

Yang

2019

Sensors

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This paper considers a reconnaissance task assignment problem for multiple unmanned aerial vehicles (UAVs) with different sensor capacities. A modified Multi-Objective Symbiotic Organisms Search algorithm (MOSOS) is adopted to optimize UAVs’ task sequence. A time-window based task model is built for heterogeneous targets. Then, the basic task assignment problem is formulated as a Multiple Time-Window based Dubins Travelling Salesmen Problem (MTWDTSP). Double-chain encoding rules and several criteria are established for the task assignment problem under logical and physical constraints. Pareto dominance determination and global adaptive scaling factors is introduced to improve the performance of original MOSOS. Numerical simulation and Monte-Carlo simulation results for the task assignment problem are also presented in this paper, whereas comparisons with non-dominated sorting genetic algorithm (NSGA-II) and original MOSOS are made to verify the superiority of the proposed method. The simulation results demonstrate that modified SOS outperforms the original MOSOS and NSGA-II in terms of optimality and efficiency of the assignment results in MTWDTSP.

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“…In [26], the researchers proposed a path planning method based on interval multiobjective PSO; this method concentrates on three objectives: path time, path safety, and path length. In the optimization of path safety, this method takes the road condition into consideration, which has enlightening values to our works, but the applied objects of these methods are traditional wheeled robots; so, this method is not suitable for MLR.…”

Section: Introductionmentioning

confidence: 99%

Path Planning Algorithm for Multi-Locomotion Robot Based on Multi-Objective Genetic Algorithm with Elitist Strategy

Liu

Wang

et al. 2022

Micromachines

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The multi-locomotion robot (MLR), including bionic insect microrobot, bionic animal robot and so on, should choose different locomotion modes according to the obstacles it faces. However, under different locomotion modes, the power consumption, moving speed, and falling risk of MLR are different, and in most cases, they are mutually exclusive. This paper proposes a path planning algorithm for MLR based on a multi-objective genetic algorithm with elitist strategy (MLRMOEGA), which has four optimization objectives: power consumption, time consumption, path falling risk, and path smoothness. We propose two operators: a map analysis operator and a population diversity expansion operator, to improve the global search ability of the algorithm and solve the problem so that it is easy to fall into the local optimal solution. We conduct simulations on MATLAB, and the results show that the proposed algorithm can effectively optimize the objective function value compared with the traditional genetic algorithm under the equal weight of the four optimization objectives, and, under alternative weights, the proposed algorithm can effectively generate the corresponding path of the decision maker’s intention under the weight of preference. Compared with the traditional genetic algorithm, the global search ability is improved effectively.

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Robot path planning in an environment with many terrains based on interval multi-objective PSO

Cited by 8 publications

References 14 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

Multi-UAV Reconnaissance Task Assignment for Heterogeneous Targets Based on Modified Symbiotic Organisms Search Algorithm

Path Planning Algorithm for Multi-Locomotion Robot Based on Multi-Objective Genetic Algorithm with Elitist Strategy

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