Optimal path planning for a mobile robot using cuckoo search algorithm

Mohanty, Prases K.; Parhi, Dayal R.

doi:10.1080/0952813x.2014.971442

Cited by 140 publications

(58 citation statements)

References 25 publications

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“…Autonomous navigation in an unknown environment should require that the robot finds a suitable, safe, smooth, and even optimal path from the starting point (position and attitude) to the end point (position and attitude). Researchers have performed a large amount of research, using artificial neural networks [1],ant colony algorithm [2], and so on combined with fuzzy logic to achieve understanding and rapid classification of current environmental perceptions, the artificial potential field method [3], behavior dynamics [4], Firefly algorithm [5], full coverage path planning algorithm [6], lidar acquisition data and RBPF-SLAM [7] to construct maps, and other methods to solve the autonomous navigation problem in unknown environments for global planning of the robot path or for a combination of global and local planning [8][9][10]. Researchers combine behavior dynamics and rolling windows to perform path planning [11,12]; the local sub-objective is optimized by using a heuristic function according to the local information in the rolling window obtained by the robot; the behavior dynamics model is used to perform autonomous path planning [13] in the rolling windows; and the planning trajectory of a series of windows is connected end to end to realize the global path planning.…”

Section: Introductionmentioning

confidence: 99%

Indoor robot path planning assisted by wireless network

Wang

Nie

Zhu

2019

J Wireless Com Network

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Indoor robot global path planning needs to complete the motion between the starting point and the target point according to robot position command transmitted by the wireless network. Behavior dynamics and rolling windows in global path planning methods have limitations in their applications because the path may not be optimal, there could be a pseudo attractor or blind search in an environment with a large state space, there could be an environment where offline learning is not applicable to real-time changes, or there could be a need to set the probability of selecting the robot action. To solve these problems, we propose a behavior dynamics and rolling windows approach to a path planning which is based on online reinforcement learning. It applies Q learning to optimize the behavior dynamics model parameters to improve the performance, behavior dynamics guides the learning process of Q learning and improves learning efficiency, and each round of intensive learning action selection knowledge is gradually corrected as the Q table is updated. The learning process is optimized. The simulation results show that this method has achieved remarkable improvement in path planning. And, in the actual experiment, the robot obtains the target location information by wireless network, and plans an optimized and smooth global path online.

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Section: Introductionmentioning

confidence: 99%

Indoor robot path planning assisted by wireless network

Wang

Nie

Zhu

2019

J Wireless Com Network

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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%

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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“…Hong et al [25] attempted dynamic path planning of a mobile robot by the use of the arti cial potential eld approach. Mohanty et al [26][27][28] developed several navigational techniques for path planning of mobile robots using arti cial intelligence. They discussed modi cation of controlling parameters of basic intelligent algorithms for performance improvement.…”

Section: Introductionmentioning

confidence: 99%

Static and Dynamic Path Planning of Humanoids using an Advanced Regression Controller

et al. 2018

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KEYWORDSAbstract. With the ability to mimic human behaviour, humanoid robots have become a topic of major interest among research fellows dealing with robotic investigation. The current work is focused on the design of a novel navigation controller based on the logic of the regression analysis to be used in the path planning and navigation of humanoid robots. The current investigation focuses on static and dynamic path planning of humanoid NAOs. The static path planning represents a single NAO navigating through random static obstacles. The dynamic path planning represents multiple humanoid NAOs navigating through random static obstacles and acting as dynamic obstacles for each other. A Petri-net controller is designed to avoid the collision among multiple NAOs in dynamic path planning. To reduce path length and time travel and provide the shortest possible path, an advanced regression controller is implemented in the NAOs in both simulation and experimental environments. Finally, a comparison has been performed between the simulation and experimental results, and good agreement is observed between both of the results with a minimal percentage of error. The proposed navigation controller is also tested against other existing navigational technologies to validate better e ciency.

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Optimal path planning for a mobile robot using cuckoo search algorithm

Cited by 140 publications

References 25 publications

Indoor robot path planning assisted by wireless network

Indoor robot path planning assisted by wireless network

Inverse kinematics of mobile manipulators based on differential evolution

Static and Dynamic Path Planning of Humanoids using an Advanced Regression Controller

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