Path planning of manipulator based on improved particle swarm optimization

Zhou, Wei; Fan, Chunxia; Wang, Lizhang; Xie, Cong; Tang, Tian; Liu, Running

doi:10.1109/ccdc55256.2022.10033524

Cited by 2 publications

(3 citation statements)

References 24 publications

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“…Another explored area is the optimization of the trajectory itself. Enhanced versions of the PSO algorithm have inspired numerous research proposals [66][67][68][69][70]. One of the first modifications involved the utilization of the normalized step cost (NSC) for particle initialization.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Evolutionary Computation Techniques for Path Planning Problems in Industrial Robotics: A State-of-the-Art Review

Juříček,

Parák,

Kůdela

2023

Computation

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The significance of robot manipulators in engineering applications and scientific research has increased substantially in recent years. The utilization of robot manipulators to save labor and increase production accuracy is becoming a common practice in industry. Evolutionary computation (EC) techniques are optimization methods that have found their use in diverse engineering fields. This state-of-the-art review focuses on recent developments and progress in their applications for industrial robotics, especially for path planning problems that need to satisfy various constraints that are implied by both the geometry of the robot and its surroundings. We discuss the most-used EC method and the modifications that suit this particular purpose, as well as the different simulation environments that are used for their development. Lastly, we outline the possible research gaps and the expected directions future research in this area will entail.

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Section: Particle Swarm Optimizationmentioning

confidence: 99%

Evolutionary Computation Techniques for Path Planning Problems in Industrial Robotics: A State-of-the-Art Review

Juříček,

Parák,

Kůdela

2023

Computation

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“…6. Likewise, the optimal routes obtained by IABC on other three instances are {0, 18, 17, 4, 2, 1, 0, 3, 20, 19, 9, 8, 0, 15, 16, 0, 5, 6, 7, 0, 10, 14, 13, 11, 12, 0}, {0, 3, 19, 20, 4, 0, 6, 1, 0, 7, 8, 9, 5, 0, 10, 23, 18, 0, 15, 14, 11, 0, 2, 21, 0, 17, 13, 12, 0, 22, 16, 25, 24, 0}, and {0, 28, 30, 9, 10, 0, 23,26,18,0,11,29,25,4,0,27,19,17,5,3,7, 0, 2, 15, 14, 0, 24, 8, 21, 1, 20, 0, 16, 22, 6, 0, 12, 13, 0}, respectively. Figures 7−9 depict their routes.…”

Section: Performance Comparison Among Various Algorithmsmentioning

confidence: 99%

“…To improve routing efficiency, Cai [10] constructed a TRV information system for underground transportation and gave the driving rules. Similarly, Han [11] introduced the intelligent dispatching system for TRVs, in which the precise positioning method and the regulation of underground traffic were given. In the abovementioned systems, an efficient platform of high-speed information interaction is provided for TRVs, however, the global routing of TRVs that is mainly done in manual mode can obtain the satisfied but suboptimal dispatching scheme.…”

Section: Introductionmentioning

confidence: 99%

Low-Carbon Routing Based on Improved Artificial Bee Colony Algorithm for Electric Trackless Rubber-Tyred Vehicles

Guo

Sun

et al. 2023

Complex Syst. Model. Simul.

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Trackless rubber-tyerd vehicles are the core equipment for auxiliary transportation in inclined-shaft coal mines, and the rationality of their routes plays the direct impact on operation safety and energy consumption. Rich studies have been done on scheduling rubber-tyerd vehicles driven by diesel oil, however, less works are for electric trackless rubber-tyred vehicles. Furthermore, energy consumption of vehicles gives no consideration on the impact of complex roadway and traffic rules on driving, especially the limited cruising ability of electric trackless rubber-tyred vehichles (TRVs). To address this issue, an energy consumption model of an electric trackless rubber-tyred vehicle is formulated, in which the effects from total mass, speed profiles, slope of roadways, and energy management mode are all considered. Following that, a low-carbon routing model of electric trackless rubber-tyred vehicles is built to minimize the total energy consumption under the constraint of vehicle avoidance, allowable load, and endurance power. As a problem-solver, an improved artificial bee colony algorithm is put forward. More especially, an adaptive neighborhood search is designed to guide employed bees to select appropriate operator in a specific space. In order to assign onlookers to some promising food sources reasonably, their selection probability is adaptively adjusted. For a stagnant food source, a knowledge-driven initialization is developed to generate a feasible substitute. The experimental results on four real-world instances indicate that improved artificial bee colony algorithm (IABC) outperforms other comparative algorithms and the special designs in its three phases effectively avoid premature convergence and speed up convergence.

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Path planning of manipulator based on improved particle swarm optimization

Cited by 2 publications

References 24 publications

Evolutionary Computation Techniques for Path Planning Problems in Industrial Robotics: A State-of-the-Art Review

Evolutionary Computation Techniques for Path Planning Problems in Industrial Robotics: A State-of-the-Art Review

Low-Carbon Routing Based on Improved Artificial Bee Colony Algorithm for Electric Trackless Rubber-Tyred Vehicles

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