A novel path planning method of mobile robots based on an improved bat algorithm

Wang, Zhongbin; Wu, Ziqing; Si, Lina; Tong, Kuangwei; Tan, Chao

doi:10.1177/0954406220963148

Cited by 8 publications

(5 citation statements)

References 26 publications

(32 reference statements)

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“…The proposed LBBA showed better performance than PF and PSO. Wang [59] due to its advantages that related to simplicity, few parameter and optimization capability, a modified bat algorithm was proposed for path planning of mobile robot. The modification tried to solve the problem of slow convergence speed in later stages.…”

Section: Bat Algorithmmentioning

confidence: 99%

Assessment and Review of the Reactive Mobile Robot Navigation

Yosif¹,

Mahmood²,

Al-Khayyt³

2021

(AREJ)

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Nowadays, the mobile robot can be seen in different fields of engineering and science. The mobile robot can do some tasks that are so difficult or very risky to be performed by a human. Most of the works currently focus on implementing artificially intelligent algorithms and other algorithms that depend on the behaviour of nature. These approaches have been used in mobile robot navigation along uncertain manner. Mobile robot navigation strategies can be divided into two approaches: the classical approach and reactive approach. The classical approach related to static environment, whiles the reactive navigation is based on an unstructured environment. Path planning is one of the most important parts of the navigation system. In this paper, review and assessment of path planning strategies that can concern with the reactive approach are discussed, because it deal with the problem of dynamic environment. Numerous reactive methods have been introduced. Most of these presented works were concerned with simulation and a few of them have shown experimental implementation. Many papers tried to make a combination between two algorithms or more to increase the efficiency. It is concluded that reactive algorithms need more learning phases, complex in design, and require large memory storage.

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Section: Bat Algorithmmentioning

confidence: 99%

Assessment and Review of the Reactive Mobile Robot Navigation

Yosif¹,

Mahmood²,

Al-Khayyt³

2021

(AREJ)

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show abstract

“…Nevertheless, the response to unexpected obstacles of DWA in the dense dynamic environment remains a non-ideal action; therefore, the redesgin of local path planner is still an open problem. [21][22][23] Inspired by above literature, this paper aims at improving A* algorithm efficiency and coordinating DWA with global planner. The main contributions of this paper are as follows…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An improved target-oriented path planning algorithm for wheeled mobile robots

Yao

Yue

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part C:

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This paper proposes a target posture-oriented path planning method, where an improved A* algorithm and an optimized dynamic windows approach (DWA) are used at the global and local planning aspects, respectively. The method enables wheeled mobile robots (WMRs) to plan an optimal path under different posture situations which can shorten the time of achieving navigation task in certain scene. On the hand of global path planning, target posture and orientation information are added into A* algorithm to obtain appropriate path. At the level of local path planning, based on DWA, evaluation functions are optimized to make the robot movement adapting to curve global path and dynamic obstacle. By introducing rotation index to evaluate experiment results, the tests verify that WMRs can reasonably plan more efficient path compared with traditional path planning method, by satisfying various posture demand with the aid of the proposed target posture-oriented A* algorithm, and quickly avoid dynamic obstacles via the optimized DWA. Therefore, the proposed algorithm can be effectively applied to WMRs to execute various target posture demands navigation tasks.

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“…1 However, keeping abreast of developing computational tools, the foregoing subject has gradually stimulated the interest of many researchers in the field of mobile robot navigation. [2][3][4][5] Referring to the literature, several scenarios have been proposed for robotic navigation, [6][7][8] which falls into two categories, namely classical and reactive. 4 The Potential Field (PF) 9 and Rapidly exploring Random Tree (RRT), 10 bug, 11,12 and velocity obstacle 13 methods are among the classical approaches.…”

Section: Introductionmentioning

confidence: 99%

The synergy of the multi-modal MPC and Q-learning approach for the navigation of a three-wheeled omnidirectional robot based on the dynamic model with obstacle collision avoidance purposes

Saeedinia

Masouleh

2022

Proceedings of the Institution of Mechanical Engineers, Part C:

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This paper proposes a new navigation control scheme for a three-wheel OMnidirectional Robot (OMR), taking into account the dynamic constraints with the aim of collision avoidance in dynamic environments. The proposed control strategy is inherently a multi-modal adaptive-Reinforcing model-based controller (MAR-MPC), which can be regarded as a judicious synergy of multi-modal MPC (MMPC) and Q-Learning (QL) to optimal navigation. This method takes advantage of a larger convergence area, compared to MMPC and MPC, and presents near-optimal MPC performance. The navigation scheme utilizes an online stochastic observer, namely, a Kalman filter, to estimate the future state of the robot. This paper formulates a general collision avoidance navigation problem in a constrained linear convex cone structure to make it real-time implementable. Furthermore, the proposed multi-modal path planning algorithm reduces the required prediction horizon and consequently affects computational cost. To evaluate the performance of the proposed navigation strategy, two static and two dynamic environment instances are simulated, and the results are compared with four algorithms, namely, Exploring Random Tree (RRT), Potential Field (PF), MPC, and MMPC. Results indicate the superior performance of the proposed navigation method in aspects of time and distance costs and collision avoidance criterion. Moreover, the proposed algorithm provides the feasibility and stability guarantee and a larger convergence region.

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A novel path planning method of mobile robots based on an improved bat algorithm

Cited by 8 publications

References 26 publications

Assessment and Review of the Reactive Mobile Robot Navigation

Assessment and Review of the Reactive Mobile Robot Navigation

An improved target-oriented path planning algorithm for wheeled mobile robots

The synergy of the multi-modal MPC and Q-learning approach for the navigation of a three-wheeled omnidirectional robot based on the dynamic model with obstacle collision avoidance purposes

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