Efficient collision-free path-planning of multiple mobile robots system using efficient artificial bee colony algorithm

Liang, Jingshan; Lee, Ching‐Hung

doi:10.1016/j.advengsoft.2014.09.006

Cited by 112 publications

(54 citation statements)

References 26 publications

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“…Equations (19) and (20) correspond to the trajectory tests 1 to 4, respectively. We have to mention that for the compared methods, joint limits are not considered, and we did not solve the orientation problem due to the kinematic limitations of the considered 4-DOF car-like mobile manipulator.…”

Section: Comparison Testsmentioning

confidence: 99%

“…9 With respect to robotic researches, these algorithms are wisely used to solve the inverse kinematics and pathtracking problems, [10][11][12][13] motion planning, 14,15 visual servo control, 16,17 and mobile navigation. [18][19][20][21][22][23] In this work, we propose the use of metaheuristic algorithms to solve the inverse kinematics of mobile manipulators as a constrained optimization problem. Initially, we define an objective function to minimize the error between the desired and the actual end-effector pose.…”

Section: Introductionmentioning

confidence: 99%

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Inverse kinematics of mobile manipulators based on differential evolution

López-Franco

Hernández-Barragán

Alanis

et al. 2018

International Journal of Advanced Robotic Systems

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The solution of the inverse kinematics of mobile manipulators is a fundamental capability to solve problems such as path planning, visual-guided motion, object grasping, and so on. In this article, we present a metaheuristic approach to solve the inverse kinematic problem of mobile manipulators. In this approach, we represent the robot kinematics using the Denavit-Hartenberg model. The algorithm is able to solve the inverse kinematic problem taking into account the mobile platform. The proposed approach is able to avoid singularities configurations, since it does not require the inversion of a Jacobian matrix. Those are two of the main drawbacks to solve inverse kinematics through traditional approaches. Applicability of the proposed approach is illustrated using simulation results as well as experimental ones using an omnidirectional mobile manipulator.

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Section: Comparison Testsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Inverse kinematics of mobile manipulators based on differential evolution

López-Franco

Hernández-Barragán

Alanis

et al. 2018

International Journal of Advanced Robotic Systems

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show abstract

“…Evolutionary algorithms such as Particle Swarm Optimization (PSO) [17][18][19], Ant Colony Optimization (ACO) [20] and Genetic Algorithm (GA) [21] are suitable for multi-objective problems. Many other evolutionary algorithms such as Artificial Bee Colony (ABC) [22], Bacterial Foraging Optimization (BFO) [23], Bio Inspired Neural Networks [24,25], and Fire Fly algorithm [26] are often trapped in local optimum, and bear high computational cost. Moreover, they are highly sensitive to search space size and data representation scheme of problem [27,28].…”

Section: Introductionmentioning

confidence: 99%

Optimal Path Planning using RRT* based Approaches: A Survey and Future Directions

Noreen¹,

Khan²,

Habib³

2016

ijacsa

197

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Abstract-Optimal path planning refers to find the collision free, shortest, and smooth route between start and goal positions. This task is essential in many robotic applications such as autonomous car, surveillance operations, agricultural robots, planetary and space exploration missions. Rapidly-exploring Random Tree Star (RRT*) is a renowned sampling based planning approach. It has gained immense popularity due to its support for high dimensional complex problems. A significant body of research has addressed the problem of optimal path planning for mobile robots using RRT* based approaches. However, no updated survey on RRT* based approaches is available. Considering the rapid pace of development in this field, this paper presents a comprehensive review of RRT* based path planning approaches. Current issues relevant to noticeable advancements in the field are investigated and whole discussion is concluded with challenges and future research directions.

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“…To be able to find this path, the mobile robot should run an adequate path planning algorithm. Several research works, for path planning of mobile robots, have been proposed in the literature (Cai and Peng, 2002;Liang and Lee, 2015;Nishitani and Matsumura, 2015;Liu and Arimoto, 1991;Zhong et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Autonomous mobile robot navigation based on an integrated environment representation designed in dynamic environments

Boujelben

Jaiem

Boutereaa

et al. 2017

IJAAC

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Abstract:The collision avoidance concept is widely applied for developing and designing in autonomous robotic applications. In an unknown environment, the challenge in mobile robot navigation or path planning is to find the path from the starting point to the target avoiding obstacles. This paper investigates the mobile robot navigation based on an integrated environment representation of the environment. It presents a developed algorithm for solving the obstacle avoidance problem in dynamic environment. The proposed algorithm ensures that the mobile robot whenever senses the obstacle, changes his direction without being collided and moves smoothly to the designed target when the path is free. The simulation results and experimental ones are presented to show performances and the effectiveness of the presented approach. These results illustrate that the developed algorithm can be well applied in the mobile robot navigation.

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Efficient collision-free path-planning of multiple mobile robots system using efficient artificial bee colony algorithm

Cited by 112 publications

References 26 publications

Inverse kinematics of mobile manipulators based on differential evolution

Inverse kinematics of mobile manipulators based on differential evolution

Optimal Path Planning using RRT* based Approaches: A Survey and Future Directions

Autonomous mobile robot navigation based on an integrated environment representation designed in dynamic environments

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