Multi-Robot Path Planning Based on Multi-Objective Particle Swarm Optimization

Thabit, Sahib; Mohades, Ali

doi:10.1109/access.2018.2886245

Cited by 58 publications

(21 citation statements)

References 29 publications

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“…On the other hand, some research provide methods, which are PSO-based algorithms in a network condition [101][102][103][104]. Some research has focused on solving the multi-robots CFPP problem while using PSO [105,106]. In Table 6, additional information from previous research related to PSO is shown.…”

Section: Refmentioning

confidence: 99%

A Performance Review of Collision-Free Path Planning Algorithms

Shin

Chae

2020

Electronics

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Path planning for mobile agents is one of the areas that has drawn the attention of researchers’, as evidenced in the large number of papers related to the collision-free path planning (CFPP) algorithm. The purpose of this paper is to review the findings of those CFPP papers and the methodologies used to generate possible solutions for CFPP for mobile agents. This survey shows that the previous CFPP papers can be divided based on four characteristics. The performance of each method primarily used to solve CFPP in previous research is evaluated and compared. Several methods are implemented and tested in same computing environment to compare the performance of generating solution in specified spatial environment with different obstacles or size. The strengths and weakness of each methodology for CFPP are shown through this survey. Ideally, this paper will provide reference for new future research.

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Section: Refmentioning

confidence: 99%

A Performance Review of Collision-Free Path Planning Algorithms

Shin

Chae

2020

Electronics

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“…In regard to multi-objective optimization in multi-robot systems, MOPSO [40] and multi-ACO [41] have already been applied to path planning problem. Broadly speaking, the metaheuristic algorithms are often applied in path planning problems compared to other issues, mainly, because optimization is the core study for finding a short and smooth path.…”

Section: Related Workmentioning

confidence: 99%

Multi-Robot Exploration Based on Multi-Objective Grey Wolf Optimizer

et al. 2019

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In this paper, we used multi-objective optimization in the exploration of unknown space. Exploration is the process of generating models of environments from sensor data. The goal of the exploration is to create a finite map of indoor space. It is common practice in mobile robotics to consider the exploration as a single-objective problem, which is to maximize a search of uncertainty. In this study, we proposed a new methodology of exploration with two conflicting objectives: to search for a new place and to enhance map accuracy. The proposed multiple-objective exploration uses the Multi-Objective Grey Wolf Optimizer algorithm. It begins with the initialization of the grey wolf population, which are waypoints in our multi-robot exploration. Once the waypoint positions are set in the beginning, they stay unchanged through all iterations. The role of updating the position belongs to the robots, which select the non-dominated waypoints among them. The waypoint selection results from two objective functions. The performance of the multi-objective exploration is presented. The trade-off among objective functions is unveiled by the Pareto-optimal solutions. A comparison with other algorithms is implemented in the end.

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“…Therefore, a multi-objective evolutionary algorithm (MOEA) has been utilized in the proposed GA to approximate the Pareto optimal solution of any given environment settings for hTetro. Similar approaches that model multi-objective optimization problems (MOOP) for robot path planning tasks and attempt to solve them through evolutionary algorithms are shown in the works of [46] and [47].…”

Section: Introductionmentioning

confidence: 98%

Multi-Objective Genetic Algorithm-Based Autonomous Path Planning for Hinged-Tetro Reconfigurable Tiling Robot

Elara

Nhan

et al. 2020

IEEE Access

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Reconfigurable robots have received broad research interest due to the high dexterity they provide and the complex actions they could perform. Robots with reconfigurability are perfect candidates in tasks like exploration or rescue missions in environments with complicated obstacle layout or with dynamic obstacles. However, the automation of reconfigurable robots is more challenging than fix-shaped robots due to the increased possible combinations of robot actions and the navigation difficulty in obstaclerich environments. This paper develops a systematic strategy to construct a model of hinged-Tetromino (hTetro) reconfigurable robot in the workspace and proposes a genetic algorithm-based method (hTetro-GA) to achieve path planning for hTetro robots. The proposed algorithm considers hTetro path planning as a multi-objective optimization problem and evaluates the performance of the outcome based on four customized fitness objective functions. In this work, the proposed hTetro-GA is tested in six virtual environments with various obstacle layouts and characteristics and with different population sizes. The algorithm generates Pareto-optimal solutions that achieve desire robot configurations in these settings, with O-shaped and I-shaped morphologies being the more efficient configurations selected from the genetic algorithm. The proposed algorithm is implemented and tested on real hTetro platform, and the framework of this work could be adopted to other robot platforms with multiple configurations to perform multi-objective based path planning. INDEX TERMS Reconfigurable robot, Tiling robotics, Multi-objective path planning, Genetic algorithm, NSGA-II.

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Multi-Robot Path Planning Based on Multi-Objective Particle Swarm Optimization

Cited by 58 publications

References 29 publications

A Performance Review of Collision-Free Path Planning Algorithms

A Performance Review of Collision-Free Path Planning Algorithms

Multi-Robot Exploration Based on Multi-Objective Grey Wolf Optimizer

Multi-Objective Genetic Algorithm-Based Autonomous Path Planning for Hinged-Tetro Reconfigurable Tiling Robot

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