Path Planning of Mobile Robots Based on a Multi-Population Migration Genetic Algorithm

Hao, Kun; Zhao, Jiale; Yu, Kaicheng; Li, Cheng; Wang, Chuanqi

doi:10.3390/s20205873

Cited by 55 publications

(27 citation statements)

References 20 publications

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“…Intelligent algorithm simulates biological evolution and insect foraging behaviors in nature, mainly including genetic algorithm(GA) [34] , ant colony algorithm (ACO) [35] , particle swarm algorithm [36] , etc. GA has the characteristics of potential parallelism and is suitable for solving and optimizing complex problems.…”

Section: Figure1 Schematic Diagram Of Mobile Robot Motion Planningmentioning

confidence: 99%

Motion Planning for Mobile Robots—Focusing on Deep Reinforcement Learning: A Systematic Review

Sun

Zhang

et al. 2021

IEEE Access

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Mobile robots contributed significantly to the intelligent development of human society, and the motion-planning policy is critical for mobile robots. This paper reviews the methods based on motionplanning policy, especially the ones involving Deep Reinforcement Learning (DRL) in the unstructured environment. The conventional methods of DRL are categorized to value-based, policy-based and actorcritic-based algorithms, and the corresponding theories and applications are surveyed. Furthermore, the recently-emerged methods of DRL are also surveyed, especially the ones involving the imitation learning, meta-learning and multi-robot systems. According to the surveys, the potential research directions of motion-planning algorithms serving for mobile robots are enlightened.

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Section: Figure1 Schematic Diagram Of Mobile Robot Motion Planningmentioning

confidence: 99%

Motion Planning for Mobile Robots—Focusing on Deep Reinforcement Learning: A Systematic Review

Sun

Zhang

et al. 2021

IEEE Access

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“…Hao et al [ 19 ] pointed out that, with the increase in the map scale, the proportion of the time taken to generate the initial population relative to the whole algorithm program time will gradually increase. In a 50 ∗ 50 grid map, the time taken to generate the initial population accounts for 81.32% of the time taken by the whole algorithm.…”

Section: Algorithm Designmentioning

confidence: 99%

“…To verify the performance of the adaptive genetic algorithm based on collision detection in the field of path planning, this paper compares and analyzes the path generation, optimal individual fitness, and algorithm running time of the adaptive genetic algorithm based on collision detection (AGACD) and the basic genetic algorithm (BGA) under the 20 * 20 grid map environment. In the environment of a 50 * 50 grid map, performance measures such as path generation, optimal individual fitness, and running time are compared and analyzed for the adaptive genetic algorithm based on collision detection, basic genetic algorithm, and multipopulation migration genetic algorithm (MPMGA) proposed in [19]. e hardware and software configuration of the simulation experiment are shown in Table 2.…”

Section: Simulation Environmentmentioning

confidence: 99%

“…However, the efficiency of this algorithm is low when solving MaOPs with complex constraints. Hao et al [ 19 ] proposed a multipopulation migration genetic algorithm (MPMGA). In this algorithm, a large population is randomly divided into several small populations with the same population number, and the selection mechanism of the selection operator is replaced by the migration mechanism among the populations.…”

Section: Introductionmentioning

confidence: 99%

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The Application of an Adaptive Genetic Algorithm Based on Collision Detection in Path Planning of Mobile Robots

Hao

Zhao

Wang

et al. 2021

Computational Intelligence and Neuroscience

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An adaptive genetic algorithm based on collision detection (AGACD) is proposed to solve the problems of the basic genetic algorithm in the field of path planning, such as low convergence path quality, many iterations required for convergence, and easily falling into the local optimal solution. First, this paper introduces the Delphi weight method to evaluate the weight of path length, path smoothness, and path safety in the fitness function, and a collision detection method is proposed to detect whether the planned path collides with obstacles. Then, the population initialization process is improved to reduce the program running time. After comprehensively considering the population diversity and the number of algorithm iterations, the traditional crossover operator and mutation operator are improved, and the adaptive crossover operator and adaptive mutation operator are proposed to avoid the local optimal solution. Finally, an optimization operator is proposed to improve the quality of convergent individuals through the second optimization of convergent individuals. The simulation results show that the adaptive genetic algorithm based on collision detection is not only suitable for simulation maps with various sizes and obstacle distributions but also has excellent performance, such as greatly reducing the running time of the algorithm program, and the adaptive genetic algorithm based on collision detection can effectively solve the problems of the basic genetic algorithm.

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“…The task of generating collision-free paths with applications in industrial robotics for manipulators is a field that has attracted the attention of many researchers due to the large demand in the market [ 7 , 8 ]. In this sense, numerous planning strategies have been developed with different approaches such as sampling-based algorithms [ 4 , 9 , 10 , 11 ], artificial potential fields [ 2 , 12 ], heuristic approaches [ 13 , 14 , 15 ], and grid-based planners [ 1 , 7 , 16 , 17 , 18 ]. The latter ones have properties that make them useful in industrial applications; the most relevant are the following: (1) The

being generated only once for a static environment is the most common in industrial applications.…”

Section: Introductionmentioning

confidence: 99%

Exploring a Novel Multiple-Query Resistive Grid-Based Planning Method Applied to High-DOF Robotic Manipulators

Huerta-Chua

Diaz-Arango

Vázquez-Leal

et al. 2021

Sensors

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The applicability of the path planning strategy to robotic manipulators has been an exciting topic for researchers in the last few decades due to the large demand in the industrial sector and its enormous potential development for space, surgical, and pharmaceutical applications. The automation of high-degree-of-freedom (DOF) manipulator robots is a challenging task due to the high redundancy in the end-effector position. Additionally, in the presence of obstacles in the workspace, the task becomes even more complicated. Therefore, for decades, the most common method of integrating a manipulator in an industrial automated process has been the demonstration technique through human operator intervention. Although it is a simple strategy, some drawbacks must be considered: first, the path’s success, length, and execution time depend on operator experience; second, for a structured environment with few objects, the planning task is easy. However, for most typical industrial applications, the environments contain many obstacles, which poses challenges for planning a collision-free trajectory. In this paper, a multiple-query method capable of obtaining collision-free paths for high DOF manipulators with multiple surrounding obstacles is presented. The proposed method is inspired by the resistive grid-based planner method (RGBPM). Furthermore, several improvements are implemented to solve complex planning problems that cannot be handled by the original formulation. The most important features of the proposed planner are as follows: (1) the easy implementation of robotic manipulators with multiple degrees of freedom, (2) the ability to handle dozens of obstacles in the environment, (3) compatibility with various obstacle representations using mathematical models, (4) a new recycling of a previous simulation strategy to convert the RGBPM into a multiple-query planner, and (5) the capacity to handle large sparse matrices representing the configuration space. A numerical simulation was carried out to validate the proposed planning method’s effectiveness for manipulators with three, five, and six DOFs on environments with dozens of surrounding obstacles. The case study results show the applicability of the proposed novel strategy in quickly computing new collision-free paths using the first execution data. Each new query requires less than 0.2 s for a 3 DOF manipulator in a configuration space free-modeled by a 7291 × 7291 sparse matrix and less than 30 s for five and six DOF manipulators in a configuration space free-modeled by 313,958 × 313,958 and 204,087 × 204,087 sparse matrices, respectively. Finally, a simulation was conducted to validate the proposed multiple-query RGBPM planner’s efficacy in finding feasible paths without collision using a six-DOF manipulator (KUKA LBR iiwa 14R820) in a complex environment with dozens of surrounding obstacles.

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Path Planning of Mobile Robots Based on a Multi-Population Migration Genetic Algorithm

Cited by 55 publications

References 20 publications

Motion Planning for Mobile Robots—Focusing on Deep Reinforcement Learning: A Systematic Review

Motion Planning for Mobile Robots—Focusing on Deep Reinforcement Learning: A Systematic Review

The Application of an Adaptive Genetic Algorithm Based on Collision Detection in Path Planning of Mobile Robots

Exploring a Novel Multiple-Query Resistive Grid-Based Planning Method Applied to High-DOF Robotic Manipulators

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